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  • 1.
    Ghareh Baghi, Arash
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Babic, A.
    Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
    Sepehri, A. A.
    CAPIS Biomedical Research and Development Centre, Mon, Belgium.
    Extraction of diagnostic information from phonocardiographic signal using time-growing neural network2019Inngår i: IFMBE Proceedings, Springer Verlag , 2019, nr 3, s. 849-853Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents an original method for extracting medical information from a heart sound recording, so called Phonocardiographic (PCG) signal. The extracted information is employed by a binary classifier to distinguish between stenosis and regurgitation murmurs. The method is based on using our original neural network, the Time-Growing Neural Network (TGNN), in an innovative way. Children with an obstruction on their semilunar valve are considered as the patient group (PG) against a reference group (RG) of children with a regurgitation in their atrioventricular valve. PCG signals were collected from 55 children, 25/30 from the PG/RG, who referred to the Children Medical Center of Tehran University. The study was conducted according to the guidelines of Good Clinical Practices and the Declaration of Helsinki. Informed consents were obtained for all the patients prior to the data acquisition. The accuracy and sensitivity of the method was estimated to be 85% and 80% respectively, exhibiting a very good performance to be used as a part of decision support system. Such a decision support system can improve the screening accuracy in primary healthcare centers, thanks to the innovative use of TGNN.

  • 2.
    Ghareh Baghi, Arash
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network2018Inngår i: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 29, nr 9, s. 4102-4115, artikkel-id 8066455Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk. 

  • 3.
    Ghareh Baghi, Arash
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Babic, A.
    University of Bergen, Norway.
    An artificial intelligent-based model for detecting systolic pathological patterns of phonocardiogram based on time-growing neural network2019Inngår i: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 83, artikkel-id 105615Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel hybrid model for classifying time series of heart sound signal using time-growing neural network. The proposed hybrid model takes segmental behaviour of heart sound signal into account by combining two different deep learning methods, the Static and the Moving Time-Growing Neural Network, which we call STGNN and MTGNN, respectively. Flexibility of the model in learning both deterministic and stochastic segments of signal allows it to learn those complicated characteristics of heart sound signal caused by any obstruction on semilunar heart valve. The model is trained to distinguish between a patient group and a reference group. The patient group is comprised of the subjects with the semilunar heart valve abnormalities including aortic stenosis, pulmonary stenosis and bicuspid aortic valve, whereas the reference group which is composed of the individuals with the heart abnormalities other than those of the reference group or the healthy ones. The model is validated using two different databases: one comprised of 140 children with various heart conditions, and the other one constituted of 50 elderly patients with aortic stenosis. Both the datasets were collected from the referrals to the University hospitals. The overall accuracy and sensitivity of the model are estimated to be 84.2% and 82.8%, respectively. The results show that the model exhibits sufficient capability to distinguish between the patient and the reference group in such a demanding clinical application. 

  • 4.
    Ghareh Baghi, Arash
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Sepehri, A. A.
    CAPIS Biomedical Research and Development Centre, Mons, Belgium.
    Babic, A.
    Department of Biomedical Engineering, Linköping University, Sweden.
    An Edge Computing Method for Extracting Pathological Information from Phonocardiogram2019Inngår i: Studies in Health Technology and Informatics, ISSN 09269630, Vol. 262, s. 364-367Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a structure of decision support system for pediatric cardiac disease, based on an Internet of Things (IoT) framework. The structure performs the intelligent decision making at its edge processing level, which classifies the heart sound signal, to three classes of cardiac conditions, normal, mild disease, and critical disease. Three types of the errors are introduced to evaluate the performance of the processing method, Type 1, 2 and 3, defined as the incorrect classification from the critical disease, mild, and normal, respectively. The method is validated using 140 real data patient records collected from the hospital referrals. The estimated negative errors for the Type 1, and 2, are calculated to be 0% and 4.8%, against the positive errors which are 6.3% and 13.3%, respectively. The Type 3, is calculated to be 16.7%, showing a high sensitivity of the method to be used in an IoT healthcare framework.

  • 5.
    Ghareh Baghi, Arash
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Sepehri, A. A.
    CAPIS Biomedical Research and Development Centre, Mon, Belgium.
    Babic, A.
    Linköping University.
    Forth heart sound detection using backward time-growing neural network2020Inngår i: IFMBE Proceedings, Springer Verlag , 2020, s. 341-345Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel method for processing heart sound signal for screening forth heart sound (S4). The proposed method is based on time growing neural network with a new scheme, which we call the Backward Time-Growing Neural Network (BTGNN). The BTGNN is trained for detecting S4 in recordings of heart sound signal. In total, 83 children patients, referred to a children University hospital, participated in the study. The collected signals are composed of the subjects with and without S4 for training and testing the method. Performance of the method is evaluated using the Leave-One-Out and the repeated random sub sampling methods. The accuracy/sensitivity of the method is estimated to be 88.3%/82.4% and the structural risk is calculated to be 18.3% using repeated random sub sampling and the A-Test methods, respectively.

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