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  • 1.
    Cai, Hongming
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik.
    Rohman, Håkan
    Linköpings universitet, Institutionen för medicinsk teknik.
    Pettersson, Hans
    IMT LiU.
    Larsson, Sven-Erik
    Öberg, Åke
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik.
    A new single fibre laser Doppler flowmeter based on digital signal processing1996Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 18, s. 523-528Artikkel i tidsskrift (Fagfellevurdert)
  • 2.
    Cai, Hongming
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik.
    Rohman, Håkan
    Linköpings universitet, Institutionen för medicinsk teknik.
    Pettersson, Hans
    IMT .
    Larsson, Sven-Erik
    Öberg, Åke
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik.
    Laser Doppler flowmetry: Charactersistics of a modified single fibre technique1996Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 34Artikkel i tidsskrift (Fagfellevurdert)
  • 3.
    Gharehbaghi, Arash
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik. Linköpings universitet, Tekniska högskolan.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Janerot Sjöberg, Birgitta
    Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden; School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
    Per, Ask
    Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik. Linköpings universitet, Tekniska högskolan.
    A novel method for discrimination between innocent and pathological heart murmurs2015Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 37, nr 7, s. 674-682Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel method for discrimination between innocent and pathological murmurs using the growing time support vector machine (GTSVM). The proposed method is tailored for characterizing innocent murmurs (IM) by putting more emphasis on the early parts of the signal as IMs are often heard in early systolic phase. Individuals with mild to severe aortic stenosis (AS) and IM are the two groups subjected to analysis, taking the normal individuals with no murmur (NM) as the control group. The AS is selected due to the similarity of its murmur to IM, particularly in mild cases. To investigate the effect of the growing time windows, the performance of the GTSVM is compared to that of a conventional support vector machine (SVM), using repeated random sub-sampling method. The mean value of the classification rate/sensitivity is found to be 88%/86% for the GTSVM and 84%/83% for the SVM. The statistical evaluations show that the GTSVM significantly improves performance of the classification as compared to the SVM.

  • 4.
    Gharehbaghi, Arash
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Dutoit, Thierry
    University of Mons, Belgium .
    Ask, Per
    Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik. Linköpings universitet, Tekniska högskolan.
    Sornmo, Leif
    Lund University, Sweden .
    Detection of systolic ejection click using time growing neural network2014Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 36, nr 4, s. 477-483Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper, we present a novel neural network for classification of short-duration heart sounds: the time growing neural network (TGNN). The input to the network is the spectral power in adjacent frequency bands as computed in time windows of growing length. Children with heart systolic ejection click (SEC) and normal children are the two groups subjected to analysis. The performance of the TGNN is compared to that of a time delay neural network (TDNN) and a multi-layer perceptron (MLP), using training and test datasets of similar sizes with a total of 614 normal and abnormal cardiac cycles. From the test dataset, the classification rate/sensitivity is found to be 97.0%/98.1% for the TGNN, 85.1%/76.4% for the TDNN, and 92.7%/85.7% for the MLP. The results show that the TGNN performs better than do TDNN and MLP when frequency band power is used as classifier input. The performance of TGNN is also found to exhibit better immunity to noise.

  • 5.
    Hult, Peter
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Ask, Per
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Wranne, Bengt
    Linköpings universitet, Institutionen för medicin och vård, Klinisk fysiologi. Linköpings universitet, Hälsouniversitetet.
    A bioacoustic method for timing of the different phases of the breathing cycle and monitoring of breathing frequency2000Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 22, nr 6, s. 425-433Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    It is well known that the flow of air through the trachea during respiration causes vibrations in the tissue near the trachea, which propagate to the surface of the body and can be picked up by a microphone placed on the throat over the trachea. Since the vibrations are a direct result of the airflow, accurate timing of inspiration and expiration is possible. This paper presents a signal analysis solution for automated monitoring of breathing and calculation of the breathing frequency. The signal analysis approach uses tracheal sound variables in the time and frequency domains, as well as the characteristics of the disturbances that can be used to discriminate tracheal sound from noise. One problem associated with the bioacoustic method is its sensitivity for acoustic disturbances, because the microphone tends to pick up all vibrations, independent of their origin. A signal processing method was developed that makes the bioacoustic method clinically useful in a broad variety of situations, for example in intensive care and during certain heart examinations, where information about both the precise timing and the phases of breathing is crucial.

  • 6.
    Hult, Peter
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik. Linköpings universitet, Filosofiska fakulteten.
    Wranne, Bengt
    Linköpings universitet, Institutionen för medicin och vård, Klinisk fysiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Hjärt- och Medicincentrum, Fysiologiska kliniken US.
    Ask, Per
    Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik. Linköpings universitet, Tekniska högskolan.
    A bioacoustic method for timing of the different phases of the breathing cycle and monitoring of breathing frequency.2000Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 22, nr 6, s. 425-433Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    It is well known that the flow of air through the trachea during respiration causes vibrations in the tissue near the trachea, which propagate to the surface of the body and can be picked up by a microphone placed on the throat over the trachea. Since the vibrations are a direct result of the airflow, accurate timing of inspiration and expiration is possible. This paper presents a signal analysis solution for automated monitoring of breathing and calculation of the breathing frequency. The signal analysis approach uses tracheal sound variables in the time and frequency domains, as well as the characteristics of the disturbances that can be used to discriminate tracheal sound from noise. One problem associated with the bioacoustic method is its sensitivity for acoustic disturbances, because the microphone tends to pick up all vibrations, independent of their origin. A signal processing method was developed that makes the bioacoustic method clinically useful in a broad variety of situations, for example in intensive care and during certain heart examinations, where information about both the precise timing and the phases of breathing is crucial.

  • 7.
    Lantz, Jonas
    et al.
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Mekanisk värmeteori och strömningslära. Linköpings universitet, Tekniska högskolan.
    Gårdhagen, Roland
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Mekanisk värmeteori och strömningslära. Linköpings universitet, Tekniska högskolan.
    Karlsson, Matts
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Mekanisk värmeteori och strömningslära. Linköpings universitet, Tekniska högskolan.
    Quantifying turbulent wall shear stress in a subject specific human aorta using large eddy simulation2012Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 34, nr 8, s. 1139-1148Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this study, large-eddy simulation (LES) is employed to calculate the disturbed flow field and the wall shear stress (WSS) in a subject specific human aorta. Velocity and geometry measurements using magnetic resonance imaging (MRI) are taken as input to the model to provide accurate boundary conditions and to assure the physiological relevance. In total, 50 consecutive cardiac cycles were simulated from which a phase average was computed to get a statistically reliable result. A decomposition similar to Reynolds decomposition is introduced, where the WSS signal is divided into a pulsating part (due to the mass flow rate) and a fluctuating part (originating from the disturbed flow). Oscillatory shear index (OSI) is plotted against time-averaged WSS in a novel way, and locations on the aortic wall where elevated values existed could easily be found. In general, high and oscillating WSS values were found in the vicinity of the branches in the aortic arch, while low and oscillating WSS were present in the inner curvature of the descending aorta. The decomposition of WSS into a pulsating and a fluctuating part increases the understanding of how WSS affects the aortic wall, which enables both qualitative and quantitative comparisons.

  • 8.
    Lantz, Jonas
    et al.
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Tekniska fakulteten. Swedish E Science Research Centre SeRC, Sweden.
    Renner, Johan
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Mekanisk värmeteori och strömningslära. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Länne, Toste
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Östergötlands Läns Landsting, Hjärt- och Medicincentrum, Thorax-kärlkliniken i Östergötland.
    Karlsson, Matts
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Mekanisk värmeteori och strömningslära. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Is aortic wall shear stress affected by aging? An image-based numerical study with two age groups2015Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 37, nr 3, s. 265-271Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The size of the larger arteries increases during the entire life, but not much is known about how the change in size affects the blood flow. This study compares the flow field in a group of young males (N = 10, age = 23.5 +/- 1.4), with a group of older males (N = 8, age = 58.0 +/- 2.8). Aortic geometries were obtained by magnetic resonance imaging, and the aortic blood flow field was computed using computational fluid dynamics. The aortic wall shear stress was obtained from the computations, and it was concluded that time-averaged wall shear stress decreased with increased age, probably as a consequence of increased aortic diameter and decreased stroke volume, which in turn reduces the shear rates in the aorta. However, the oscillatory shear index, which is a measure of the oscillatory nature of the wall shear stress vector, seemed to be unaffected by aging.

  • 9.
    Ugnell, Håkan
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik.
    Öberg, Åke
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik.
    The time-variable photoplethysmographic signal; dependence on the heart synchronous signal on wavelengt and sample volume1995Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 17, nr 8, s. 571-578Artikkel i tidsskrift (Fagfellevurdert)
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