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  • 1.
    Ghareh Baghi, Ghareh Baghi
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Assessment of Valvular Aortic Stenosis by Signal Analysis of the Phonocardiogram2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Aortic stenosis (AS) is one of the most prevalent valvular heart diseases in elderly people. According to the recommendations of both the American Heart Association and the European Society of Cardiology, severity assessment of AS is primarily based on echocardiographic findings. The experience of the investigator here play important roles in the accuracy of the assessment, and therefore in the disease management. However, access to the expert physicians could be limited, especially in rural health care centers of developing countries.

    This thesis aims to develop processing algorithms tailored for phonocardiographic signal with the intension to obtain a noninvasive diagnostic tool for AS assessment and severity grading. The algorithms employ a phonocardiogram as input signal and perform analysis for screening and diagnostics. Such a decision support system, which we call “the intelligent phonocardiography”, can be widely used in primary healthcare centers.

    The main contribution of the thesis is to present innovative models for the phonocardiographic analysis by taking the segmental characteristics of the signal into consideration. Three novel methodologies are described, based on the presented models, to perform robust classification. In the first attempt, a novel pattern recognition framework is presented for screening of AS-related murmurs. The framework offers a hybrid model for classifying cyclic time series in general, but is tailored to detect the murmurs as a special case study. The time growing neural network is another method that we use to classify short time signals with abrupt frequency transition. The idea of the growing frames is extended to the cyclic signals with stochastic properties for the screening purposes. Finally, a combined statistical and artificial intelligent classifier is proposed for grading the severity of AS.

    The study suggests comprehensive statistical validations not only for the evaluation and representation of systolic murmurs but also for setting the methodology design parameters, which can be considered as one of the significant features of the study. The resulting methodologies can be implemented by using web and mobile technologies to be utilized in distributed healthcare system.

    List of papers
    1. A pattern recognition framework for detecting dynamic changes on cyclic time series
    Open this publication in new window or tab >>A pattern recognition framework for detecting dynamic changes on cyclic time series
    2015 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 3, p. 696-708Article in journal (Refereed) Published
    Abstract [en]

    This paper proposes a framework for binary classification of the time series with cyclic characteristics. The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method. The DFBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques. The framework offers a systematic procedure for finding the optimal design parameters associated with the hybrid model. The proposed  model is optimized to detect the changes of the heart sound recordings (HSRs) related to aortic stenosis. Experimental results show that the proposed framework provides efficient tools for classification of the HSRs based on the heart murmurs. It is also evidenced that the hybrid model, proposed by the framework, substantially improves the classification performance when it comes to detection of the heart disease.

    Place, publisher, year, edition, pages
    Elsevier, 2015
    Keywords
    Hybrid model, cyclic time series, time series, phonocardiogram, systolic murmurs
    National Category
    Biomedical Laboratory Science/Technology Medical Biotechnology
    Identifiers
    urn:nbn:se:liu:diva-110177 (URN)10.1016/j.patcog.2014.08.017 (DOI)000347747000008 ()
    Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2017-12-05Bibliographically approved
    2. Detection of systolic ejection click using time growing neural network
    Open this publication in new window or tab >>Detection of systolic ejection click using time growing neural network
    2014 (English)In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 36, no 4, p. 477-483Article in journal (Refereed) Published
    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.

    Place, publisher, year, edition, pages
    Elsevier, 2014
    Keywords
    Systolic ejection click; Time growing neural network; Time delay neural network; Heart sound
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-106865 (URN)10.1016/j.medengphy.2014.02.011 (DOI)000334976800008 ()
    Available from: 2014-05-28 Created: 2014-05-23 Last updated: 2017-12-05
    3. A novel method for discrimination between innocent and pathological heart murmurs
    Open this publication in new window or tab >>A novel method for discrimination between innocent and pathological heart murmurs
    2015 (English)In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 37, no 7, p. 674-682Article in journal (Refereed) Published
    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.

    Place, publisher, year, edition, pages
    Elsevier, 2015
    Keywords
    Growing-time support vector machine, support vector machine, phonocardiogram signal, heart murmurs, innocent murmurs.
    National Category
    Medical Engineering
    Identifiers
    urn:nbn:se:liu:diva-117825 (URN)10.1016/j.medengphy.2015.04.013 (DOI)000357354400007 ()26003286 (PubMedID)
    Note

    At the time for thesis presentation publication was in status: Manuscript

    Available from: 2015-05-08 Created: 2015-05-08 Last updated: 2017-12-04Bibliographically approved
    4. An Automatic Tool for Pediatric Heart Sounds Segmentation
    Open this publication in new window or tab >>An Automatic Tool for Pediatric Heart Sounds Segmentation
    Show others...
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    In this paper, we present a novel algorithm for pediatric heart sound segmentation, incorporated into a graphical user interface. The algorithm employs both the Electrocardiogram (ECG) and Phonocardiogram (PCG) signals for an efficient segmentation under pathological circumstances.First, the ECG signal is invoked in order to determine the beginning and end points of each cardiac cycle by using wavelet transform technique. Then, first and second heart sounds within the cycles are identified over the PCG signal by paying attention to the spectral properties of the sounds. The algorithm is applied on 120 recordings of normal and pathological children, totally containing 1976 cardiac cycles. The accuracy of the segmentation algorithm is 97% for S1 and 94% for S2 identification while all the cardiac cycles are correctly determined.

    National Category
    Biomedical Laboratory Science/Technology Medical Biotechnology
    Identifiers
    urn:nbn:se:liu:diva-110179 (URN)
    Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2014-09-04Bibliographically approved
    5. Severity assessments of aortic stenosis using intelligent phonocardiography
    Open this publication in new window or tab >>Severity assessments of aortic stenosis using intelligent phonocardiography
    Show others...
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Objectives: To study capabilities of the intelligent phonocardiography (IPCG) in automatic grading severity of the aortic stenosis (AS).

    Methods: Phonocardiogram signals were recorded from the patients with AS, as diagnosed by echocardiography. The patient group is comprised of signals, recorded from 5 patients (2 recordings from each), mostly elderly referrals (>60 years) with mild to severe AS. An advanced processing algorithm, consisted of the wavelet transform and the stepwise regression analysis, characterizes the systolic murmur caused by the AS in order to predict the 5 indicators; mean pressure gradient over the aortic valve (MPG), maximum jet velocity (MJV), aortic valve area (AVA), velocity time integral and the ejection period. The automatic assessment is performed by an artificial neural network using the predicted values of the indicators as the input data. Reliability of the IPCG is validated by applying repeated random sub-sampling (RRSS) with 70%/30% of the training/testing data, and calculating the accuracy. The RRSS is also employed to validate reproducibility of the IPCG by using 70% of the signals for training and the second recording of the same individuals for  testing.

    Results: Accuracy of the IPCG is estimated to be and (95% confidence interval) for the reliability and the reproducibility, respectively. Linear correlation between the characterized systolic murmur and the MPG (r>0.81), the MJV (r>0.82) and the AVA (r>0.85) is observed.

    Conclusions: The IPCG has the potential to objectively serve as a clinical tool for grading severity of the aortic stenosis.

    National Category
    Biomedical Laboratory Science/Technology Medical Biotechnology
    Identifiers
    urn:nbn:se:liu:diva-110181 (URN)
    Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2014-09-04Bibliographically approved
    Download (pdf)
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  • 2.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Science & Engineering.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Department of Information Science and Media Studies, University of Bergen, Norway.
    A pattern recognition framework for detecting dynamic changes on cyclic time series2015In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 3, p. 696-708Article in journal (Refereed)
    Abstract [en]

    This paper proposes a framework for binary classification of the time series with cyclic characteristics. The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method. The DFBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques. The framework offers a systematic procedure for finding the optimal design parameters associated with the hybrid model. The proposed  model is optimized to detect the changes of the heart sound recordings (HSRs) related to aortic stenosis. Experimental results show that the proposed framework provides efficient tools for classification of the HSRs based on the heart murmurs. It is also evidenced that the hybrid model, proposed by the framework, substantially improves the classification performance when it comes to detection of the heart disease.

  • 3.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Ask, Per
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Lindèn, Maria
    Mälardalen University, Sweden.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Univeristy of Bergen, Norway.
    A Novel Model for Screening Aortic Stenosis Using Phonocardiogram2015In: 16th Nordic-Baltic Conference on Biomedical Engineering / [ed] Henrik Mindedal and Mikael Persson, Springer Science Business Media , 2015, p. 48-51Conference paper (Refereed)
    Abstract [en]

    This study presents an algorithm for screening aortic stenosis, based on heart sound signal processing. It benefits from an artificial intelligent-based (AI-based) model using a multi-layer perceptron neural network. The AI-based model learns disease related murmurs using non-stationary features of the murmurs. Performance of the model is statistically evaluated using two different databases, one of children and the other of elderly volunteers with normal heart condition and aortic stenosis. Results showed a 95% confidence interval of the high accuracy/sensitivity (84.1%-86.0%)/(86.0%-88.4%) thus exhibiting a superior performance to a cardiologist who relies on the conventional auscultation. The study suggests including the heart sound signal in the clinical decision making due to its potential to improve the screening accuracy.

  • 4.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Department of Information Science and Media Studies, University of Bergen, Norway.
    A-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methods2022In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 289, p. 132-135Article in journal (Refereed)
    Abstract [en]

    This paper presents an original method for studying the performance of the supervised Machine Learning (ML) methods, the A-Test method. The method offers the possibility of investigating the structural risk as well as the learning capacity of ML methods in a quantitating manner. A-Test provides a powerful validation method for the learning methods with small or medium size of the learning data, where overfitting is regarded as a common problem of learning. Such a condition can occur in many applications of bioinformatics and biomedical engineering in which access to a large dataset is a challengeable task. Performance of the A-Test method is explored by validation of two ML methods, using real datasets of heart sound signals. The datasets comprise of children cases with a normal heart condition as well as 4 pathological cases: aortic stenosis, ventricular septal defect, mitral regurgitation, and pulmonary stenosis. It is observed that the A[1]Test method provides further comprehensive and more realistic information about the performance of the classification methods as compared to the existing alternatives, the K-fold validation and repeated random sub-sampling.

    Download full text (pdf)
    fulltext
  • 5.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (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
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    A novel method for discrimination between innocent and pathological heart murmurs2015In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 37, no 7, p. 674-682Article in journal (Refereed)
    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.

  • 6.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Dutoit, Thierry
    University of Mons, Belgium .
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Sornmo, Leif
    Lund University, Sweden .
    Detection of systolic ejection click using time growing neural network2014In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 36, no 4, p. 477-483Article in journal (Refereed)
    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.

  • 7.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Dutoit, Thierry
    TCTS Lab,University of Mons, Belgium.
    Sepehri, Amir
    ICT research center, Amir Kabir University, Tehran, Iran.
    Hult, Peter
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Ask, Per
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    An Automatic Tool for Pediatric Heart Sounds SegmentationManuscript (preprint) (Other academic)
    Abstract [en]

    In this paper, we present a novel algorithm for pediatric heart sound segmentation, incorporated into a graphical user interface. The algorithm employs both the Electrocardiogram (ECG) and Phonocardiogram (PCG) signals for an efficient segmentation under pathological circumstances.First, the ECG signal is invoked in order to determine the beginning and end points of each cardiac cycle by using wavelet transform technique. Then, first and second heart sounds within the cycles are identified over the PCG signal by paying attention to the spectral properties of the sounds. The algorithm is applied on 120 recordings of normal and pathological children, totally containing 1976 cardiac cycles. The accuracy of the segmentation algorithm is 97% for S1 and 94% for S2 identification while all the cardiac cycles are correctly determined.

  • 8.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Ekman, Inger
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Ask, Per
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Nylander, Eva
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Janerot Sjöberg, Birgitta
    Departments of Clinical Science, Intervention and Technology, Karolinska Institutet & Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden.
    Severity assessments of aortic stenosis using intelligent phonocardiographyManuscript (preprint) (Other academic)
    Abstract [en]

    Objectives: To study capabilities of the intelligent phonocardiography (IPCG) in automatic grading severity of the aortic stenosis (AS).

    Methods: Phonocardiogram signals were recorded from the patients with AS, as diagnosed by echocardiography. The patient group is comprised of signals, recorded from 5 patients (2 recordings from each), mostly elderly referrals (>60 years) with mild to severe AS. An advanced processing algorithm, consisted of the wavelet transform and the stepwise regression analysis, characterizes the systolic murmur caused by the AS in order to predict the 5 indicators; mean pressure gradient over the aortic valve (MPG), maximum jet velocity (MJV), aortic valve area (AVA), velocity time integral and the ejection period. The automatic assessment is performed by an artificial neural network using the predicted values of the indicators as the input data. Reliability of the IPCG is validated by applying repeated random sub-sampling (RRSS) with 70%/30% of the training/testing data, and calculating the accuracy. The RRSS is also employed to validate reproducibility of the IPCG by using 70% of the signals for training and the second recording of the same individuals for  testing.

    Results: Accuracy of the IPCG is estimated to be and (95% confidence interval) for the reliability and the reproducibility, respectively. Linear correlation between the characterized systolic murmur and the MPG (r>0.81), the MJV (r>0.82) and the AVA (r>0.85) is observed.

    Conclusions: The IPCG has the potential to objectively serve as a clinical tool for grading severity of the aortic stenosis.

  • 9.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Science & Engineering.
    Ekman, Inger
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Science & Engineering.
    Nylander, Eva
    Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Janerot-Sjoberg, Birgitta
    Karolinska Institute, Sweden; Karolinska University Hospital, Sweden; Karolinska University Hospital, Sweden; KTH Royal Institute Technology, Sweden.
    Letter: Assessment of aortic valve stenosis severity using intelligent phonocardiography2015In: International Journal of Cardiology, ISSN 0167-5273, E-ISSN 1874-1754, Vol. 198, p. 58-60Article in journal (Other academic)
    Abstract [en]

    n/a

    Download full text (pdf)
    fulltext
  • 10.
    Gharehbaghi, Arash
    et al.
    School of Information Technology, Halmstad University, Halmstad, Sweden.
    Partovi, Elaheh
    Department of Electrical Engineering, Amirkabir University, Tehran, Iran.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Department Information Science and Media Studies, University of Bergen, Bergen, Norway.
    Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification2023In: CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023, IOS PRESS , 2023, Vol. 302, p. 526-530Conference paper (Refereed)
    Abstract [en]

    This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known public dataset of heart sound signals: the Physionet heart sound. The accuracy of the PCNN, was estimated to be 87.2% which outperforms the rest of the three methods: the SCNN, the LSTM, and the CCNN by 12%, 7%, and 0.5%, respectively. The resulting method can be easily implemented in an Internet of Things platform to be employed as a decision support system for the screening heart abnormalities.

    Download full text (pdf)
    fulltext
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