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  • 1.
    Ahlström, Christer
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Health Sciences.
    Nonlinear phonocardiographic Signal Processing2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The aim of this thesis work has been to develop signal analysis methods for a computerized cardiac auscultation system, the intelligent stethoscope. In particular, the work focuses on classification and interpretation of features derived from the phonocardiographic (PCG) signal by using advanced signal processing techniques.

    The PCG signal is traditionally analyzed and characterized by morphological properties in the time domain, by spectral properties in the frequency domain or by nonstationary properties in a joint time-frequency domain. The main contribution of this thesis has been to introduce nonlinear analysis techniques based on dynamical systems theory to extract more information from the PCG signal. Especially, Takens' delay embedding theorem has been used to reconstruct the underlying system's state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction. In this thesis, the PCG signal's structure in state space has been exploited in several applications. Change detection based on recurrence time statistics was used in combination with nonlinear prediction to remove obscuring heart sounds from lung sound recordings in healthy test subjects. Sample entropy and mutual information were used to assess the severity of aortic stenosis (AS) as well as mitral insufficiency (MI) in dogs. A large number of, partly nonlinear, features was extracted and used for distinguishing innocent murmurs from murmurs caused by AS or MI in patients with probable valve disease. Finally, novel work related to very accurate localization of the first heart sound by means of ECG-gated ensemble averaging was conducted. In general, the presented nonlinear processing techniques have shown considerably improved results in comparison with other PCG based techniques.

    In modern health care, auscultation has found its main role in primary or in home health care, when deciding if special care and more extensive examinations are required. Making a decision based on auscultation is however difficult, why a simple tool able to screen and assess murmurs would be both time- and cost-saving while relieving many patients from needless anxiety. In the emerging field of telemedicine and home care, an intelligent stethoscope with decision support abilities would be of great value.

    List of papers
    1. A method for accurate localization of the first heart sound and possible applications
    Open this publication in new window or tab >>A method for accurate localization of the first heart sound and possible applications
    2008 (English)In: Physiological Measurement, ISSN 0967-3334, E-ISSN 1361-6579, Vol. 29, no 3, p. 417-428Article in journal (Refereed) Published
    Abstract [en]

    We have previously developed a method for localization of the first heart sound (S1) using wavelet denoising and ECG-gated peak-picking. In this study, an additional enhancement step based on cross-correlation and ECG-gated ensemble averaging (EA) is presented. The main objective of the improved method was to localize S1 with very high temporal accuracy in (pseudo-) real time. The performance of S1 detection and localization, with and without EA enhancement, was evaluated on simulated as well as experimental data. The simulation study showed that EA enhancement reduced the localization error considerably and that S1 could be accurately localized at much lower signal-to-noise ratios. The experimental data were taken from ten healthy subjects at rest and during invoked hyper- and hypotension. For this material, the number of correct S1 detections increased from 91% to 98% when using EA enhancement. Improved performance was also demonstrated when EA enhancement was used for continuous tracking of blood pressure changes and for respiration monitoring via the electromechanical activation time. These are two typical applications where accurate localization of S1 is essential for the results.

    Place, publisher, year, edition, pages
    Institutionen för medicinsk teknik, 2008
    Keywords
    ensemble averaging, detection, localization, heart sound, bioacoustics
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-11856 (URN)10.1088/0967-3334/29/3/011 (DOI)
    Note
    Original publication: C Ahlstrom, T Länne, P Ask and A Johansson, A method for accurate localization of the first heart sound and possible applications, 2008, Physiological Measurement, (29), 3, 417-428. http://dx.doi.org/10.1088/0967-3334/29/3/011. Copyright: Institute of Physics and IOP Publishing Limited, http://www.iop.org/EJ/journal/PMAvailable from: 2008-05-20 Created: 2008-05-20 Last updated: 2017-12-13
    2. Assessing Aortic Stenosis using Sample Entropy of the Phonocardiographic Signal in Dogs
    Open this publication in new window or tab >>Assessing Aortic Stenosis using Sample Entropy of the Phonocardiographic Signal in Dogs
    Show others...
    2008 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 55, no 8, p. 2107-2109Article in journal (Refereed) Published
    Abstract [en]

    In aortic valve stenosis (AS), heart murmurs arise as an effect of turbulent blood flow distal to the obstructed valves. With increasing AS severity, the flow becomes more unstable, and the ensuing murmur becomes more complex. We hypothesize that these hemodynamic flow changes can be quantified based on the complexity of the phonocardiographic (PCG) signal. In this study, sample entropy (SampEn) was investigated as a measure of complexity using a dog model. Twenty-seven boxer dogs with various degrees of AS were examined with Doppler echocardiography, and the peak aortic flow velocity (Vmax) was used as a reference of AS severity. SampEn correlated to Vmax with R = 0.70 using logarithmic regression. In a separate analysis, significant differences were found between physiologic murmurs and murmurs caused by AS (p < 0.05), and the area under a receiver operating characteristic curve was calculated to 0.96. Comparison with previously presented PCG measures for AS assessment showed improved performance when using SampEn, especially for differentiation between physiological murmurs and murmurs caused by mild AS. Studies in patients will be needed to properly assess the technique in humans.

    Keywords
    Aortic stenosis (AS), bioacoustics, heart sound, murmur, sample entropy (SampEn)
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-13042 (URN)10.1109/TBME.2008.923767 (DOI)
    Available from: 2008-03-20 Created: 2008-03-20 Last updated: 2017-12-13
    3. Assessing mitral regurgitation attributable to myxomatous mitral valve disease in dogs using signal analysis of heart sounds and murmurs
    Open this publication in new window or tab >>Assessing mitral regurgitation attributable to myxomatous mitral valve disease in dogs using signal analysis of heart sounds and murmurs
    Show others...
    2008 (English)Article in journal (Refereed) Submitted
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-13043 (URN)
    Available from: 2008-03-20 Created: 2008-03-20 Last updated: 2009-03-26
    4. Feature Extraction for Systolic Heart Murmur Classification
    Open this publication in new window or tab >>Feature Extraction for Systolic Heart Murmur Classification
    Show others...
    2006 (English)In: Annals of Biomedical Engineering, ISSN 0090-6964, E-ISSN 1573-9686, Vol. 34, no 11, p. 1666-1677Article in journal (Refereed) Published
    Abstract [en]

    Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an “intelligent stethoscope” with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil’s sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.

    Keywords
    Auscultation, Bioacoustics, Feature selection, Heart sounds, Valvular disease
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13044 (URN)10.1007/s10439-006-9187-4 (DOI)
    Available from: 2008-03-20 Created: 2008-03-20 Last updated: 2017-12-13
    5. Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction
    Open this publication in new window or tab >>Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction
    2005 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 12, no 12, p. 812-815Article in journal (Refereed) Published
    Abstract [en]

    Heart sounds (HS) obscure the interpretation of lung sounds (LS). This letter presents a new method to detect and remove this undesired disturbance. The HS detection algorithm is based on a recurrence time statistic that is sensitive to changes in a reconstructed state space. Signal segments that are found to contain HS are removed, and the arising missing parts are replaced with predicted LS using a nonlinear prediction scheme. The prediction operates in the reconstructed state space and uses an iterated integrated nearest trajectory algorithm. The HS detection algorithm detects HS with an error rate of 4% false positives and 8% false negatives. The spectral difference between the reconstructed LS signal and an LS signal with removed HS was 0.34/spl plusmn/0.25, 0.50/spl plusmn/0.33, 0.46/spl plusmn/0.35, and 0.94/spl plusmn/0.64 dB/Hz in the frequency bands 20-40, 40-70, 70-150, and 150-300 Hz, respectively. The cross-correlation index was found to be 99.7%, indicating excellent similarity between actual LS and predicted LS. Listening tests performed by a skilled physician showed high-quality auditory results.

    Place, publisher, year, edition, pages
    Institutionen för medicinsk teknik, 2005
    Keywords
    Bioacoustics, heart sound (HS), lung sound (LS), nonlinear prediction, recurrence time statistics
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-11857 (URN)10.1109/LSP.2005.859528 (DOI)
    Note
    Original publication: Ahlstrom, C., Liljefeldt, O., Hult, P. and Ask, P., Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction, 2005, IEEE Signal Processing Letters, (12), 12, 812-815. http://dx.doi.org/10.1109/LSP.2005.859528. Copyright: IEEE, http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=97Available from: 2008-05-20 Created: 2008-05-20 Last updated: 2017-12-13
  • 2.
    Ahlström, Christer
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Processing of the Phonocardiographic Signal: methods for the intelligent stethoscope2006Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Phonocardiographic signals contain bioacoustic information reflecting the operation of the heart. Normally there are two heart sounds, and additional sounds indicate disease. If a third heart sound is present it could be a sign of heart failure whereas a murmur indicates defective valves or an orifice in the septal wall. The primary aim of this thesis is to use signal processing tools to improve the diagnostic value of this information. More specifically, three different methods have been developed:

    • A nonlinear change detection method has been applied to automatically detect heart sounds. The first and the second heart sounds can be found using recurrence times of the first kind while the third heart sound can be found using recurrence times of the second kind. Most third heart sound occurrences were detected (98 %), but the amount of false extra detections was rather high (7 % of the heart cycles).

    • Heart sounds obscure the interpretation of lung sounds. A new method based on nonlinear prediction has been developed to remove this undesired disturbance. High similarity was obtained when comparing actual lung sounds with lung sounds after removal of heart sounds.

    • Analysis methods such as Shannon energy, wavelets and recurrence quantification analysis were used to extract information from the phonocardiographic signal. The most prominent features, determined by a feature selection method, were used to create a new feature set for heart murmur classification. The classification result was 86 % when separating patients with aortic stenosis, mitral insufficiency and physiological murmurs.

    The derived methods give reasonable results, and they all provide a step forward in the quest for an intelligent stethoscope, a universal phonocardiography tool able to enhance auscultation by improving sound quality, emphasizing abnormal events in the heart cycle and distinguishing different heart murmurs.

    List of papers
    1. Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction
    Open this publication in new window or tab >>Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction
    2005 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 12, no 12, p. 812-815Article in journal (Refereed) Published
    Abstract [en]

    Heart sounds (HS) obscure the interpretation of lung sounds (LS). This letter presents a new method to detect and remove this undesired disturbance. The HS detection algorithm is based on a recurrence time statistic that is sensitive to changes in a reconstructed state space. Signal segments that are found to contain HS are removed, and the arising missing parts are replaced with predicted LS using a nonlinear prediction scheme. The prediction operates in the reconstructed state space and uses an iterated integrated nearest trajectory algorithm. The HS detection algorithm detects HS with an error rate of 4% false positives and 8% false negatives. The spectral difference between the reconstructed LS signal and an LS signal with removed HS was 0.34/spl plusmn/0.25, 0.50/spl plusmn/0.33, 0.46/spl plusmn/0.35, and 0.94/spl plusmn/0.64 dB/Hz in the frequency bands 20-40, 40-70, 70-150, and 150-300 Hz, respectively. The cross-correlation index was found to be 99.7%, indicating excellent similarity between actual LS and predicted LS. Listening tests performed by a skilled physician showed high-quality auditory results.

    Place, publisher, year, edition, pages
    Institutionen för medicinsk teknik, 2005
    Keywords
    Bioacoustics, heart sound (HS), lung sound (LS), nonlinear prediction, recurrence time statistics
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-11857 (URN)10.1109/LSP.2005.859528 (DOI)
    Note
    Original publication: Ahlstrom, C., Liljefeldt, O., Hult, P. and Ask, P., Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction, 2005, IEEE Signal Processing Letters, (12), 12, 812-815. http://dx.doi.org/10.1109/LSP.2005.859528. Copyright: IEEE, http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=97Available from: 2008-05-20 Created: 2008-05-20 Last updated: 2017-12-13
    2. Detection of the 3rd Heart Sound using Recurrence Time Statistics
    Open this publication in new window or tab >>Detection of the 3rd Heart Sound using Recurrence Time Statistics
    2006 (English)In: Proc. 31st IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Toulouse, France, 2006, 2006, p. 1040-1043Conference paper, Published paper (Other academic)
    Abstract [en]

    The 3rd heart sound (S3) is normally heard during auscultation of younger individuals, but it is also common in many patients with heart failure. Compared to the 1st and 2nd heart sounds, S3 has low amplitude and low frequency content, making it hard to detect (both manually for the physician and automatically by a detection algorithm). We present an algorithm based on a recurrence time statistic which is sensitive to changes in a reconstructed state space, particularly for detection of transitions with very low energy. Heart sound signals from ten children were used in this study. Most S3 occurrences were detected (98 %), but the amount of false extra detections was rather high (7% of the heart cycles). In conclusion, the method seems capable of detecting S3 with high accuracy and robustness.

    Series
    IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149
    Keywords
    acoustic, signal detection, bioacoustics, signal reconstruction, statistics, heart sound, auscultation, heart failure, reconstructed state space, recurrence time statistics
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-14058 (URN)
    Available from: 2006-10-09 Created: 2006-10-09 Last updated: 2009-04-21
    3. Feature Extraction for Systolic Heart Murmur Classification
    Open this publication in new window or tab >>Feature Extraction for Systolic Heart Murmur Classification
    Show others...
    2006 (English)In: Annals of Biomedical Engineering, ISSN 0090-6964, E-ISSN 1573-9686, Vol. 34, no 11, p. 1666-1677Article in journal (Refereed) Published
    Abstract [en]

    Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an “intelligent stethoscope” with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil’s sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.

    Keywords
    Auscultation, Bioacoustics, Feature selection, Heart sounds, Valvular disease
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13044 (URN)10.1007/s10439-006-9187-4 (DOI)
    Available from: 2008-03-20 Created: 2008-03-20 Last updated: 2017-12-13
  • 3.
    Ahlström, Christer
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Rask, Peter
    University Hospital, Örebro, Sweden .
    Karlsson, Jan-Erik
    County Hospital Ryhov, Jönköping, Sweden.
    Nylander, Eva
    Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Dahlström, Ulf
    Linköping University, Department of Medicine and Care, Cardiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Cardiology.
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Assessment of Suspected Aortic Stenosis by Auto Mutual Information Analysis of Murmurs2007In: Engineering in Medicine and Biology Society, 2007. EMBS 2007, 2007, p. 1945-1948Conference paper (Refereed)
    Abstract [en]

    Mild sclerotic thickening of the aortic valve affects 25% of the population, and the condition causes aortic valve stenosis (AS) in 2% of adults above 65 years. Echocardiography is today the clinical standard for assessing AS. However, a cost effective and uncomplicated technique that can be used for decision support in the primary health care would be of great value. In this study, recorded phonocardiographic signals were analyzed using the first local minimum of the auto mutual information (AMI) function. The AMI method measures the complexity in the sound signal, which is related to the amount of turbulence in the blood flow and thus to the severity of the stenosis. Two previously developed phonocardiographic methods for assessing AS severity were used for comparison, the murmur energy ratio and the sound spectral averaging technique. Twenty-nine patients with suspected AS were examined with Doppler echocardiography. The aortic jet velocity was used as a reference of AS severity, and it was found to correlate with the AMI method, the murmur energy ratio and the sound spectral averaging technique with the correlation coefficient R = 0.82, R = 0.73 and R = 0.76, respectively.

  • 4.
    Ahlström, Christer
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Arts and Sciences.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Detection of the 3(rd) heart sound using recurrence time statistics2006In: 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, Vols 1-13, 2006, Vol. 1-13, p. 2288-2291Conference paper (Refereed)
    Abstract [en]

    The 3(rd) heart sound (S3) is normally heard during auscultation of younger individuals, but it is also common in many patients with heart failure. Compared to the 1(st) and 2(nd) heart sounds, S3 has low amplitude and low frequency content, making it hard to detect (both manually for the physician and automatically by a detection algorithm). We present an algorithm based on a recurrence time statistic which is sensitive to changes in a reconstructed state space, particularly for detection of transitions with very low energy. Heart sound signals from ten children were used in this study. Most S3 occurrences were detected (98%), but the amount of false extra detections was rather high (7% of the heart cycles). In conclusion, the method seems capable of detecting S3 with high accuracy and robustness.

  • 5.
    Ahlström, Christer
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Detection of the 3rd Heart Sound using Recurrence Time Statistics2006In: Proc. 31st IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Toulouse, France, 2006, 2006, p. 1040-1043Conference paper (Other academic)
    Abstract [en]

    The 3rd heart sound (S3) is normally heard during auscultation of younger individuals, but it is also common in many patients with heart failure. Compared to the 1st and 2nd heart sounds, S3 has low amplitude and low frequency content, making it hard to detect (both manually for the physician and automatically by a detection algorithm). We present an algorithm based on a recurrence time statistic which is sensitive to changes in a reconstructed state space, particularly for detection of transitions with very low energy. Heart sound signals from ten children were used in this study. Most S3 occurrences were detected (98 %), but the amount of false extra detections was rather high (7% of the heart cycles). In conclusion, the method seems capable of detecting S3 with high accuracy and robustness.

  • 6.
    Ahlström, Christer
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Thresholding distance plots using true recurrence points2006In: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006, IEEE , 2006, p. 688-691Conference paper (Refereed)
    Abstract [en]

    Recurrence plots (RP) visualize multi-dimensional state spaces and represent the recurrence of states of a system. Recurrence points can be divided into true recurrence points and false recurrence points (also called sojourn points). We introduce the true recurrence point recurrence plot, TRP, a variant of the traditional RP excluding the sojourn points. This is a cleaned up RP free from recurrence points originating from tangential motion, and hence a more robust representation of unstable periodic orbits. The method is demonstrated with three simple systems, a periodic sine wave, a quasi-periodic torus and the x-component of the chaotic Lorenz system

  • 7.
    Ahlström, Christer
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Hult, Peter
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Ask, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Wheeze analysis and detection with non-linear phase-space embedding2005In: Nordic Baltic Conference Biomedical Engineering and Medical Physics,2005, Umeå: IFMBE , 2005, p. 305-Conference paper (Refereed)
  • 8.
    Ahlström, Christer
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Hult, Peter
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Rask, P
    Karlsson, J-E
    Nylander, Eva
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Dahlström, Ulf
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Cardiology. Östergötlands Läns Landsting, Heart Centre, Department of Cardiology.
    Ask, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Using the intelligent stethoscope for extraction of features for systolic heart murmur classification2006In: World Congress on Medical Physics and Biomedical Engineering WC2006,2006, 2006Conference paper (Other academic)
  • 9.
    Ahlström, Christer
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Rask, Peter
    Örebro university.
    Karlsson, Jan-Erik
    Nylander, Eva
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Dahlström, Ulf
    Linköping University, Department of Medicine and Health Sciences, Cardiology . Linköping University, Faculty of Health Sciences.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Feature Extraction for Systolic Heart Murmur Classification2006In: Annals of Biomedical Engineering, ISSN 0090-6964, E-ISSN 1573-9686, Vol. 34, no 11, p. 1666-1677Article in journal (Refereed)
    Abstract [en]

    Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an “intelligent stethoscope” with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil’s sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.

  • 10.
    Ahlström, Christer
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Hult, Peter
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Schmekel, Birgitta
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ask, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Automatisk detektering av ronki med icke-linjära metoder2004In: Svenska Läkaresällskapets riksstämma,2004, 2004, p. 66-66Conference paper (Other academic)
  • 11.
    Ahlström, Christer
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Hult, Peter
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Schmekel, Birgitta
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ask, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Wheeze detection with nonlinear statespace embedding2004In: International Lung Sound Association,2004, 2004, p. 38-39Conference paper (Other academic)
  • 12.
    Ahlström, Christer
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Health Sciences.
    Höglund, Katja
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Health Sciences.
    Häggström, Jens
    Kvart, Clarence
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Health Sciences.
    Assessing Aortic Stenosis using Sample Entropy of the Phonocardiographic Signal in Dogs2008In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 55, no 8, p. 2107-2109Article in journal (Refereed)
    Abstract [en]

    In aortic valve stenosis (AS), heart murmurs arise as an effect of turbulent blood flow distal to the obstructed valves. With increasing AS severity, the flow becomes more unstable, and the ensuing murmur becomes more complex. We hypothesize that these hemodynamic flow changes can be quantified based on the complexity of the phonocardiographic (PCG) signal. In this study, sample entropy (SampEn) was investigated as a measure of complexity using a dog model. Twenty-seven boxer dogs with various degrees of AS were examined with Doppler echocardiography, and the peak aortic flow velocity (Vmax) was used as a reference of AS severity. SampEn correlated to Vmax with R = 0.70 using logarithmic regression. In a separate analysis, significant differences were found between physiologic murmurs and murmurs caused by AS (p < 0.05), and the area under a receiver operating characteristic curve was calculated to 0.96. Comparison with previously presented PCG measures for AS assessment showed improved performance when using SampEn, especially for differentiation between physiological murmurs and murmurs caused by mild AS. Studies in patients will be needed to properly assess the technique in humans.

  • 13.
    Ahlström, Christer
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Höglund, Katja
    Dept. of Anatomy and Physiology, Faculty of Veterinary Medicine and Animal Science, Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Häggström, Jens
    Dept. of Clinical Sciences, Faculty of Veterinary Medicine and Animal Science, Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Kvart, Clarence
    Dept. of Anatomy and Physiology, Faculty of Veterinary Medicine and Animal Science, Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Distinguishing Innocent Murmurs from Murmurs caused by Aortic Stenosis by Recurrence Quantification Analysis2006In: ROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 18, Canakkale, Turkey: World Academy of Science, Engineering and Technology (W A S E T) , 2006, p. 40-45Conference paper (Refereed)
    Abstract [en]

    It is sometimes difficult to differentiate between innocent murmurs and pathological murmurs during auscultation. In these difficult cases, an intelligent stethoscope with decision support abilities would be of great value. In this study, using a dog model, phonocardiographic recordings were obtained from 27 boxer dogs with various degrees of aortic stenosis (AS) severity. As a reference for severity assessment, continuous wave Doppler was used. The data were analyzed with recurrence quantification analysis (RQA) with the aim to find features able to distinguish innocent murmurs from murmurs caused by AS. Four out of eight investigated RQA features showed significant differences between innocent murmurs and pathological murmurs. Using a plain linear discriminant analysis classifier, the best pair of features (recurrence rate and entropy) resulted in a sensitivity of 90% and a specificity of 88%. In conclusion, RQA provide valid features which can be used for differentiation between innocent murmurs and murmurs caused by AS.

  • 14.
    Ahlström, Christer
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Johansson, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Hult, Peter
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Ask, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Chaotic dynamics of respiratory sounds2006In: Chaos, Solitons & Fractals, ISSN 0960-0779, E-ISSN 1873-2887, Vol. 29, no 5, p. 1054-1062Article in journal (Refereed)
    Abstract [en]

    There is a growing interest in nonlinear analysis of respiratory sounds (RS), but little has been done to justify the use of nonlinear tools on such data. The aim of this paper is to investigate the stationarity, linearity and chaotic dynamics of recorded RS. Two independent data sets from 8 + 8 healthy subjects were recorded and investigated. The first set consisted of lung sounds (LS) recorded with an electronic stethoscope and the other of tracheal sounds (TS) recorded with a contact accelerometer. Recurrence plot analysis revealed that both LS and TS are quasistationary, with the parts corresponding to inspiratory and expiratory flow plateaus being stationary. Surrogate data tests could not provide statistically sufficient evidence regarding the nonlinearity of the data. The null hypothesis could not be rejected in 4 out of 32 LS cases and in 15 out of 32 TS cases. However, the Lyapunov spectra, the correlation dimension (D2) and the Kaplan-Yorke dimension (DKY) all indicate chaotic behavior. The Lyapunov analysis showed that the sum of the exponents was negative in all cases and that the largest exponent was found to be positive. The results are partly ambiguous, but provide some evidence of chaotic dynamics of RS, both concerning LS and TS. The results motivate continuous use of nonlinear tools for analysing RS data. © 2005 Elsevier Ltd. All rights reserved.

  • 15.
    Ahlström, Christer
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Johansson, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Länne, Toste
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Ask, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    A respiration monitor based on electrocardiographic and photoplethysmographic sensor fusion2004In: IEEE Engineering in Medical and Biological Society,2004, Piscataway, N.J. USA: IEEEEMBS , 2004, p. 2311-Conference paper (Refereed)
  • 16.
    Ahlström, Christer
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Johansson, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Länne, Toste
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Ask, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Monitorering av andning and blodtrycksförändringar baserat på EKG och hjärtljud2007In: Medicinteknik dagarna,2007, 2007Conference paper (Other academic)
  • 17.
    Ahlström, Christer
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Johansson, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Uhlin, Fredrik
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Nephrology.
    Länne, Toste
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Ask, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Noninvasive investigation of blood pressure changes using the pulse wave transit time: A novel approach in the monitoring of hemodialysis patients2005In: Journal of Artificial Organs, ISSN 1434-7229, E-ISSN 1619-0904, Vol. 8, no 3, p. 192-197Article in journal (Refereed)
    Abstract [en]

    Severe blood pressure changes are well known in hemodialysis. Detection and prediction of these are important for the well-being of the patient and for optimizing treatment. New noninvasive methods for this purpose are required. The pulse wave transit time technique is an indirect estimation of blood pressure, and our intention is to investigate whether this technique is applicable for hemodialysis treatment. A measurement setup utilizing lower body negative pressure and isometric contraction was used to simulate dialysis-related blood pressure changes in normal test subjects. Systolic blood pressure levels were compared to different pulse wave transit times, including and excluding the cardiac preejection period. Based on the results of these investigations, a pulse wave transit time technique adapted for dialysis treatment was developed and tried out on patients. To determine systolic blood pressure in the normal group, the total pulse wave transit time was found most suitable (including the cardiac preejection period). Correlation coefficients were r = 0.80 ± 0.06 (mean ± SD) overall and r = 0.81 ± 0.16 and r = 0.09 ± 0.62 for the hypotension and hypertension phases, respectively. When applying the adapted technique in dialysis patients, large blood pressure variations could easily be detected when present. Pulse wave transit time is correlated to systolic blood pressure within the acceptable range for a trend-indicating system. The method's applicability for dialysis treatment requires further studies. The results indicate that large sudden pressure drops, like those seen in sudden hypovolemia, can be detected. © The Japanese Society for Artificial Organs 2005.

  • 18.
    Ahlström, Christer
    et al.
    Linköping University, Department of Biomedical Engineering.
    Liljefeldt, Olle
    Hult, Peter
    Linköping University, Department of Biomedical Engineering.
    Ask, Per
    Linköping University, Department of Biomedical Engineering.
    Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction.2005In: Medicinteknikdagarna, 2005, Vol. 12, p. 812-815Conference paper (Other academic)
    Abstract [en]

    Heart sounds (HS) obscure the interpretation of lung sounds (LS). This letter presents a new method to detect and remove this undesired disturbance. The HS detection algorithm is based on a recurrence time statistic that is sensitive to changes in a reconstructed state space. Signal segments that are found to contain HS are removed, and the arising missing parts are replaced with predicted LS using a nonlinear prediction scheme. The prediction operates in the reconstructed state space and uses an iterated integrated nearest trajectory algorithm. The HS detection algorithm detects HS with an error rate of 4% false positives and 8% false negatives. The spectral difference between the reconstructed LS signal and an LS signal with removed HS was 0 34 0 25, 0 50 0 33, 0 46 0 35, and 0 94 0 64 dB/Hz in the frequency bands 20–40, 40–70, 70–150, and 150–300 Hz, respectively. The cross-correlation index was found to be 99.7%, indicating excellent similarity between actual LS and predicted LS. Listening tests performed by a skilled physician showed high-quality auditory results.

  • 19.
    Ahlström, Christer
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Liljefeldt, Olle
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction2005In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 12, no 12, p. 812-815Article in journal (Refereed)
    Abstract [en]

    Heart sounds (HS) obscure the interpretation of lung sounds (LS). This letter presents a new method to detect and remove this undesired disturbance. The HS detection algorithm is based on a recurrence time statistic that is sensitive to changes in a reconstructed state space. Signal segments that are found to contain HS are removed, and the arising missing parts are replaced with predicted LS using a nonlinear prediction scheme. The prediction operates in the reconstructed state space and uses an iterated integrated nearest trajectory algorithm. The HS detection algorithm detects HS with an error rate of 4% false positives and 8% false negatives. The spectral difference between the reconstructed LS signal and an LS signal with removed HS was 0.34/spl plusmn/0.25, 0.50/spl plusmn/0.33, 0.46/spl plusmn/0.35, and 0.94/spl plusmn/0.64 dB/Hz in the frequency bands 20-40, 40-70, 70-150, and 150-300 Hz, respectively. The cross-correlation index was found to be 99.7%, indicating excellent similarity between actual LS and predicted LS. Listening tests performed by a skilled physician showed high-quality auditory results.

  • 20.
    Ahlström, Christer
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Health Sciences.
    Länne, Toste
    Linköping University, Department of Medicine and Health Sciences, Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Health Sciences.
    Johansson, Anders
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Health Sciences.
    A method for accurate localization of the first heart sound and possible applications2008In: Physiological Measurement, ISSN 0967-3334, E-ISSN 1361-6579, Vol. 29, no 3, p. 417-428Article in journal (Refereed)
    Abstract [en]

    We have previously developed a method for localization of the first heart sound (S1) using wavelet denoising and ECG-gated peak-picking. In this study, an additional enhancement step based on cross-correlation and ECG-gated ensemble averaging (EA) is presented. The main objective of the improved method was to localize S1 with very high temporal accuracy in (pseudo-) real time. The performance of S1 detection and localization, with and without EA enhancement, was evaluated on simulated as well as experimental data. The simulation study showed that EA enhancement reduced the localization error considerably and that S1 could be accurately localized at much lower signal-to-noise ratios. The experimental data were taken from ten healthy subjects at rest and during invoked hyper- and hypotension. For this material, the number of correct S1 detections increased from 91% to 98% when using EA enhancement. Improved performance was also demonstrated when EA enhancement was used for continuous tracking of blood pressure changes and for respiration monitoring via the electromechanical activation time. These are two typical applications where accurate localization of S1 is essential for the results.

  • 21.
    Ahlström, Christer
    et al.
    Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Nystrom, Marcus
    Lund University, Sweden .
    Holmqvist, Kenneth
    Lund University, Sweden .
    Fors, Carina
    Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Sandberg, David
    Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Anund, Anna
    Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Kecklund, Goran
    Stockholm University, Sweden .
    Akerstedt, Torbjorn
    Stockholm University, Sweden .
    Fit-for-duty test for estimation of drivers sleepiness level: Eye movements improve the sleep/wake predictor2013In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 26, p. 20-32Article in journal (Refereed)
    Abstract [en]

    Driver sleepiness contributes to a considerable proportion of road accidents, and a fit-for-duty test able to measure a drivers sleepiness level might improve traffic safety. The aim of this study was to develop a fit-for-duty test based on eye movement measurements and on the sleep/wake predictor model (SWP, which predicts the sleepiness level) and evaluate the ability to predict severe sleepiness during real road driving. Twenty-four drivers participated in an experimental study which took place partly in the laboratory, where the fit-for-duty data were acquired, and partly on the road, where the drivers sleepiness was assessed. A series of four measurements were conducted over a 24-h period during different stages of sleepiness. Two separate analyses were performed; a variance analysis and a feature selection followed by classification analysis. In the first analysis it was found that the SWP and several eye movement features involving anti-saccades, pro-saccades, smooth pursuit, pupillometry and fixation stability varied significantly with different stages of sleep deprivation. In the second analysis, a feature set was determined based on floating forward selection. The correlation coefficient between a linear combination of the acquired features and subjective sleepiness (Karolinska sleepiness scale, KSS) was found to be R = 0.73 and the correct classification rate of drivers who reached high levels of sleepiness (KSS andgt;= 8) in the subsequent driving session was 82.4% (sensitivity = 80.0%, specificity = 84.2% and AUC = 0.86). Future improvements of a fit-for-duty test should focus on how to account for individual differences and situational/contextual factors in the test, and whether it is possible to maintain high sensitive/specificity with a shorter test that can be used in a real-life environment, e.g. on professional drivers.

  • 22.
    Hoglund, K.
    et al.
    Höglund, K., Department of Anatomy and Physiology, Faculty of Veterinary Medicine and Animal Science, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden.
    Ahlström, Christer
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Haggstrom, J.
    Häggström, J., Department of Clinical Sciences, Faculty of Veterinary Medicine and Animal Science, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Hult, Peter
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Kvart, C.
    Department of Anatomy and Physiology, Faculty of Veterinary Medicine and Animal Science, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden.
    Time-frequency and complexity analyses for differentiation of physiologic murmurs from heart murmurs caused by aortic stenosis in boxers2007In: American Journal of Veterinary Research, ISSN 0002-9645, E-ISSN 1943-5681, Vol. 68, no 9, p. 962-969Article in journal (Refereed)
    Abstract [en]

    Objective - To investigate whether time-frequency and complexity analyses of heart murmurs can be used to differentiate physiologic murmurs from murmurs caused by aortic stenosis (AS) in Boxers. Animals - 27 Boxers with murmurs. Procedures - Dogs were evaluated via auscultation and echocardiography. Analyses of time-frequency properties (TFPs, ie, maximal murmur frequency and duration of murmur frequency > 200 Hz) and correlation dimension (T2) of murmurs were performed on phonocardiographic sound data. Time-frequency property and T2 analyses of low-intensity murmurs in 16 dogs without AS were performed at 7 weeks and 12 months of age. Additionally, TFP and T2 analyses were performed on data obtained from 11 adult AS-affected dogs with murmurs. Results - In dogs with low-intensity murmurs, TFP or T2 values at 7 weeks and 12 months did not differ significantly. For differentiation of physiologic murmurs from murmurs caused by mild AS, duration of murmur frequency > 200 Hz was useful and the combination assessment of duration of frequency > 200 Hz and T2 of the murmur had a sensitivity of 94% and a specificity of 82%. Maximal murmur frequency did not differentiate dogs with AS from those without AS. Conclusions and Clinical Relevance - Results suggested that assessment of the duration of murmur frequency > 200 Hz can be used to distinguish physiologic heart murmurs from murmurs caused by mild AS in Boxers. Combination of this analysis with T2 analysis may be a useful complementary method for diagnostic assessment of cardiovascular function in dogs.

  • 23.
    Hult, Peter
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Ahlström, Christer
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Rattfält, Linda
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Hagström, Caroline
    Medicinsk teknik Örebro universitetssjukhus.
    Pettersson, Nils-Erik
    Medicinsk teknik Örebro universitetssjukhus.
    Ask, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    The intelligent stethoscope as a tool in modern health care2005In: Nordic Baltic Conference Biomedical Engineering and Medical Physics,2005, Umeå: IFMBE , 2005, p. 79-Conference paper (Refereed)
  • 24.
    Hult, Peter
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Ahlström, Christer
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Rattfält, Linda
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Hagström, Cecilia
    Örebro University Hospital .
    Pettersson, Nils-Erik
    Örebro University Hospital .
    Ask, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    The intelligent stethoscope2005In: EMBEC05,2005, Prag: IFMBE , 2005Conference paper (Refereed)
  • 25.
    Hurtig-Wennlof, A.
    et al.
    Hurtig-Wennlöf, A., School of Health and Medical Sciences/Clinical Medicine, Örebro University, SE-701 82 Örebro, Sweden.
    Ahlström, Christer
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Egerlid, R.
    Department of Clinical Physiology, Örebro University Hospital, Örebro, Sweden.
    Resare, M.
    Department of Clinical Physiology, Örebro University Hospital, Örebro, Sweden.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Rask, P.
    Department of Clinical Physiology, Örebro University Hospital, Örebro, Sweden.
    Heart sounds are altered by open cardiac surgery2009In: Experimental and Clinical Cardiology, ISSN 1205-6626, Vol. 14, no 2, p. 18-20Article in journal (Refereed)
    Abstract [en]

    BACKGROUND AND OBJECTIVE: Patients have reported that they perceive their own heart sounds differently after open cardiac surgery than before the surgery. The present study was designed to investigate whether changes in heart sounds can be quantitatively measured. METHOD: Heart sounds were recorded from 57 patients undergoing coronary artery bypass graft (CABG) surgery and from a control group of 10 subjects. The so-called Hjorth descriptors and the main frequency peak were compared before and after surgery to determine whether the characteristics of the heart sounds had changed. RESULTS: At a group level, the first heart sound was found to be significantly different after CABG surgery. Generally, the heart sounds shifted toward a lower frequency after surgery in the CABG group. No significant changes were found in the control group. CONCLUSIONS: Heart sounds are altered after CABG surgery. The changes are objectively quantifiable and may also be subjectively perceived by the patients.

  • 26. Höglund, K
    et al.
    Ahlström, Christer
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Häggström, J
    Kvart, C
    Spectral analysis of heart murmurs in boxer dogs2006In: 16th European College of veterinary Internal medicine - Companion Animals Congress ECVIM-CA,2006, 2006Conference paper (Other academic)
  • 27.
    Johansson, Anders
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Ahlström, Christer
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Länne, Toste
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Ask, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Pulse wave transit time for monitoring respiration rate2006In: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 44, no 6, p. 471-478Article in journal (Refereed)
    Abstract [en]

    In this study, we investigate the beat-to-beat respiratory fluctuations in pulse wave transit time (PTT) and its subcomponents, the cardiac pre-ejection period (PEP) and the vessel transit time (VTT) in ten healthy subjects. The three transit times were found to fluctuate in pace with respiration. When applying a simple breath detecting algorithm, 88% of the breaths seen in a respiration air-flow reference could be detected correctly in PTT. Corresponding numbers for PEP and VTT were 76 and 81%, respectively. The performance during hypo- and hypertension was investigated by invoking blood pressure changes. In these situations, the error rates in breath detection were significantly higher. PTT can be derived from signals already present in most standard monitoring set-ups. The transit time technology thus has prospects to become an interesting alternative for respiration rate monitoring. © International Federation for Medical and Biological Engineering 2006.

  • 28.
    Ljungvall, Ingrid
    et al.
    Swedish University of Agriculture Science.
    Ahlström, Christer
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Hoglund, Katja
    Swedish University of Agriculture Science.
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Kvart, Clarence
    Swedish University of Agriculture Science.
    Borgarelli, Michele
    Kansas State University.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Haggstrom , Jens
    Swedish University of Agriculture Science.
    Use of signal analysis of heart sounds and murmurs to assess severity of mitral valve regurgitation attributable to myxomatous mitral valve disease in dogs2009In: AMERICAN JOURNAL OF VETERINARY RESEARCH, ISSN 0002-9645 , Vol. 70, no 5, p. 604-613Article in journal (Refereed)
    Abstract [en]

    Objective-To investigate use of signal analysis of heart sounds and murmurs in assessing severity of mitral valve regurgitation (mitral regurgitation [MR]) in dogs with myxomatous mitral valve disease (MMVD).

    Animals-77 client-owned dogs.

    Procedures-Cardiac sounds were recorded from dogs evaluated by use of auscultatory and echocardiographic classification systems. Signal analysis techniques were developed to extract 7 sound variables (first frequency peak, murmur energy ratio, murmur duration > 200 Hz, sample entropy and first minimum of the auto mutual information function of the murmurs, and energy ratios of the first heart sound [S1] and second heart sound [S2]).

    Results-Significant associations were detected between severity of MR and all sound variables, except the energy ratio of S1. An increase in severity of MR resulted in greater contribution of higher frequencies, increased signal irregularity, and decreased energy ratio of S2. The optimal combination of variables for distinguishing dogs with high-intensity murmurs from other dogs was energy ratio of S2 and murmur duration > 200 Hz (sensitivity, 79%; specificity, 71%) by use of the auscultatory classification. By use of the echocardiographic classification, corresponding variables were auto mutual information, first frequency peak, and energy ratio of S2 (sensitivity, 88%; specificity, 82%).

    Conclusions and Clinical Relevance-Most of the investigated sound variables were significantly associated with severity of MR, which indicated a powerful diagnostic potential for monitoring MMVD. Signal analysis techniques could be valuable for clinicians when performing risk assessment or determining whether special care and more extensive examinations are required.

  • 29.
    Ljungvall, Ingrid
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Ahlström, Christer
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Höglund, Katja
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Kvart, Clarence
    Borgarelli, Michele
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Häggström, Jens
    Assessing mitral regurgitation attributable to myxomatous mitral valve disease in dogs using signal analysis of heart sounds and murmurs2008Article in journal (Refereed)
  • 30.
    Rattfält, Linda
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Ahlström, Christer
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Berglin, Lena
    The Swedish School of Textiles, University College of Borås, Borås, Sweden.
    Lindén, Maria
    Dept. of Computer Science and Electronics, Mälardalen University, Västerås, Sweden.
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Wiklund, Urban
    Dept. of Biomedical Engineering & Informatics, Umeå University Hospital, Umeå, Sweden.
    A Canonical correlation approach to heart beat detection in textile ECG measurements2006In: IET 3rd International Conference On Advances in Medical, Signal and Information Processing, 2006. MEDSIP 2006, IEEE , 2006, p. 1-4Conference paper (Refereed)
    Abstract [en]

    Research in textile sensors has lead to new ways to measure electrocardiograms (ECG). However, additional disturbances from e.g. muscular noise and high skin-electrode impedances often result in poor signal quality. The paper contains a simple application of canonical correlation analysis (CCA) on multi channel ECG signals recorded with textile electrodes. Using CCA to solve the blind source separation (BSS) problem, we intend to separate the ECG signal from the various noise sources. The method (CCABSS) was compared to simple averaging of the ECG channels and to the independent component analysis method (ICA). A heart beat detector was used to evaluate the signal quality. Results show that the signal was completely lost while simulating various noise in 33%, 17% and 7% of the cases for averaging, ICA and CCA, respectively.

  • 31.
    Rattfält, Linda
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology. Biomedical Engineering, Örebro County Council, Örebro, Sweden.
    Ahlström, Christer
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology. Biomedical Engineering, Örebro County Council, Örebro, Sweden.
    Eneling, Martin
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Ragnemalm, Bengt
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Hult, Peter
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements. Biomedical Engineering, Örebro County Council, Örebro, Sweden.
    Lindén, M.
    Intelligent Sensor Systems, Mälardalen University, Västerås, Sweden.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology. Biomedical Engineering, Örebro County Council, Örebro, Sweden.
    A platform for physiological signals including an intelligent stethoscope2009In: 4th European Conference of the International Federation for Medical and Biological Engineering: ECIFMBE 2008 23–27 November 2008 Antwerp, Belgium / [ed] Jos Sloten, Pascal Verdonck, Marc Nyssen, Jens Haueisen, Springer Berlin/Heidelberg, 2009, Vol. 22, p. 1038-1041Chapter in book (Refereed)
    Abstract [en]

    We have developed a physiological signal platform where presently phonocardiographic (PCG) and electrocardiographic (ECG) signals can be acquired and on which our signal analysis techniques can be implemented. The platform can also be used to store patient data, to enable comparison over time and invoke distance consultation if necessary. Our studies so far indicate that with our signal analysis techniques of heart sounds we are able to separate normal subject from those with aortic stenosis and mitral insufficiency. Further we are able to identify the third heart sound. The platform is being tested in a primary health care setting.

  • 32.
    Thorslund, Birgitta
    et al.
    Linköping University, Department of Behavioural Sciences and Learning. Linköping University, The Swedish Institute for Disability Research. Linköping University, Faculty of Arts and Sciences.
    Ahlström, Christer
    VTI, Swedish National Road and Transport Research Institute, Linköping, Sweden.
    Peters, Björn
    VTI, Swedish National Road and Transport Research Institute, Linköping, Sweden.
    Eriksson, Olle
    VTI, Swedish National Road and Transport Research Institute, Linköping, Sweden .
    Lidestam, Björn
    Linköping University, Department of Behavioural Sciences and Learning, Psychology. Linköping University, Faculty of Arts and Sciences.
    Lyxell, Björn
    Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences. Linköping University, The Swedish Institute for Disability Research. Östergötlands Läns Landsting, Anaesthetics, Operations and Specialty Surgery Center, Department of Otorhinolaryngology in Linköping.
    Cognitive workload and visual behavior in elderly drivers with hearing loss2014In: European Transport Research Review, ISSN 1867-0717, E-ISSN 1866-8887, Vol. 6, no 4, p. 377-385Article in journal (Refereed)
    Abstract [en]

    Purpose

    To examine eye tracking data and compare visual behavior in individuals with normal hearing (NH) and with moderate hearing loss (HL) during two types of driving conditions: normal driving and driving while performing a secondary task.

    Methods

    24 participants with HL and 24 with NH were exposed to normal driving and to driving with a secondary task (observation and recall of 4 visually displayed letters). Eye movement behavior was assessed during normal driving by the following performance indicators: number of glances away from the road; mean duration of glances away from the road; maximum duration of glances away from the road; and percentage of time looking at the road. During driving with the secondary task, eye movement data were assessed in terms of number of glances to the secondary task display, mean duration of glances to the secondary task display, and maximum duration of glances to the secondary task display. The secondary task performance was assessed as well, counting the number of correct letters, the number of skipped letters, and the number of correct letters ignoring order.

    Results

    While driving with the secondary task, drivers with HL looked twice as often in the rear-view mirror than during normal driving and twice as often as drivers with NH regardless of condition. During secondary task, the HL group looked away from the road more frequently but for shorter durations than the NH group. Drivers with HL had fewer correct letters and more skipped letters than drivers with NH.

    Conclusions

    Differences in visual behavior between drivers with NH and with HL are bound to the driving condition. Driving with a secondary task, drivers with HL spend as much time looking away from the road as drivers with NH, however with more frequent and shorter glances away. Secondary task performance is lower for the HL group, suggesting this group is less willing to perform this task. The results also indicate that drivers with HL use fewer but more focused glances away than drivers with NH, they also perform a visual scan of the surrounding traffic environment before looking away towards the secondary task display.

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