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Liu A, Zhang S, Wang Z, Tang Y, Zhang X, Wang Y. A learnable front-end based efficient channel attention network for heart sound classification. Physiol Meas 2023; 44:095003. [PMID: 37619586 DOI: 10.1088/1361-6579/acf3cf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 08/24/2023] [Indexed: 08/26/2023]
Abstract
Objective. To enhance the accuracy of heart sound classification, this study aims to overcome the limitations of common models which rely on handcrafted feature extraction. These traditional methods may distort or discard crucial pathological information within heart sounds due to their requirement of tedious parameter settings.Approach.We propose a learnable front-end based Efficient Channel Attention Network (ECA-Net) for heart sound classification. This novel approach optimizes the transformation of waveform-to-spectrogram, enabling adaptive feature extraction from heart sound signals without domain knowledge. The features are subsequently fed into an ECA-Net based convolutional recurrent neural network, which emphasizes informative features and suppresses irrelevant information. To address data imbalance, Focal loss is employed in our model.Main results.Using the well-known public PhysioNet challenge 2016 dataset, our method achieved a classification accuracy of 97.77%, outperforming the majority of previous studies and closely rivaling the best model with a difference of just 0.57%.Significance.The learnable front-end facilitates end-to-end training by replacing the conventional heart sound feature extraction module. This provides a novel and efficient approach for heart sound classification research and applications, enhancing the practical utility of end-to-end models in this field.
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Affiliation(s)
- Aolei Liu
- School of Optical Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Sunjie Zhang
- School of Optical Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Zhe Wang
- School of Optical Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Yiheng Tang
- School of Optical Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Xiaoli Zhang
- School of Optical Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Yongxiong Wang
- School of Optical Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
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Helman SM, Herrup EA, Christopher AB, Al-Zaiti SS. The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review. Cardiol Young 2021; 31:1770-1780. [PMID: 34725005 PMCID: PMC8805679 DOI: 10.1017/s1047951121004212] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review.
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Affiliation(s)
- Stephanie M Helman
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elizabeth A Herrup
- Division of Pediatric Critical Care Medicine, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Adam B Christopher
- Division of Pediatric Cardiology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Salah S Al-Zaiti
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Division of Cardiology, University of Pittsburgh, Pittsburgh, PA, USA
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3
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Levin AD, Ragazzi A, Szot SL, Ning T. Extraction and assessment of diagnosis-relevant features for heart murmur classification. Methods 2021; 202:110-116. [PMID: 34245871 DOI: 10.1016/j.ymeth.2021.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 06/10/2021] [Accepted: 07/02/2021] [Indexed: 12/21/2022] Open
Abstract
This paper presents a heart murmur detection and multi-class classification approach via machine learning. We extracted heart sound and murmur features that are of diagnostic importance and developed additional 16 features that are not perceivable by human ears but are valuable to improve murmur classification accuracy. We examined and compared the classification performance of supervised machine learning with k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. We put together a test repertoire having more than 450 heart sound and murmur episodes to evaluate the performance of murmur classification using cross-validation of 80-20 and 90-10 splits. As clearly demonstrated in our evaluation, the specific set of features chosen in our study resulted in accurate classification consistently exceeding 90% for both classifiers.
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Affiliation(s)
- Alisa D Levin
- Department of Engineering, Trinity College, Hartford, Connecticut, United States.
| | - Anthony Ragazzi
- Department of Engineering, Trinity College, Hartford, Connecticut, United States.
| | - Skyler L Szot
- Department of Engineering, Trinity College, Hartford, Connecticut, United States.
| | - Taikang Ning
- Department of Engineering, Trinity College, Hartford, Connecticut, United States.
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Sagayam KM, Andrushia AD, Ghosh A, Deperlioglu O, Elngar AA. Recognition of Hand Gesture Image Using Deep Convolutional Neural Network. INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2021. [DOI: 10.1142/s0219467821400088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In recent technology, there is tremendous growth in computer applications that highlight human–computer interaction (HCI), such as augmented reality (AR), and Internet of Things (IoT). As a consequence, hand gesture recognition was highlighted as a very up-to-date research area in computer vision. The body language is a vital method to communicate between people, as well as emphasis on voice messages, or as a complete message on its own. Thus, automatic hand gestures recognition systems can be used to increase human–computer interaction. Therefore, many approaches for hand gesture recognition systems have been designed. However, most of these methods include hybrid processes such as image pre-processing, segmentation, and classification. This paper describes how to create hand gesture model easily and quickly with a well-tuned deep convolutional neural network. Experiments were performed using the Cambridge Hand Gesture data set for illustration of success and efficiency of the convolutional neural network. The accuracy was achieved as 96.66%, where sensitivity and specificity were found to be 85% and 98.12%, respectively, according to the average values obtained at the end of 20 times of operation. These results were compared with the existing works using the same dataset and it was found to have higher values than the hybrid methods.
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Affiliation(s)
- K. Martin Sagayam
- Department of Electronics and Communication Engineering, Kandiarunya Institute of Technology and Sciences, Coimbatore 641114, India
| | - A. Diana Andrushia
- Department of Electronics and Communication Engineering, Kandiarunya Institute of Technology and Sciences, Coimbatore 641114, India
| | - Ahona Ghosh
- Department of Computational Science, Brainware University, Kolkata 700125, India
| | - Omer Deperlioglu
- Department of Computer Programming, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Ahmed A. Elngar
- Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, 62511, Egypt
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Deperlioglu O, Kose U, Gupta D, Khanna A, Sangaiah AK. Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network. COMPUTER COMMUNICATIONS 2020; 162:31-50. [PMID: 32843778 PMCID: PMC7434639 DOI: 10.1016/j.comcom.2020.08.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/01/2020] [Accepted: 08/17/2020] [Indexed: 05/04/2023]
Abstract
Objective of this study is to introduce a secure IoHT system, which acts as a clinical decision support system with the diagnosis of cardiovascular diseases. In this sense, it was emphasized that the accuracy rate of diagnosis (classification) can be improved via deep learning algorithms, by needing no hybrid-complex models, and a secure data processing can be achieved with a multi-authentication and Tangle based approach. In detail, heart sounds were classified with Autoencoder Neural Networks (AEN) and the IoHT system was built for supporting doctors in real-time. For developing the diagnosis infrastructure by the AEN, PASCAL B-Training and Physiobank-PhysioNet A-Training heart sound datasets were used accordingly. For the PASCAL dataset, the AEN provided a diagnosis-classification performance with the accuracy of 100%, sensitivity of 100%, and the specificity of 100% whereas the rates were respectively 99.8%, 99.65%, and 99.13% for the PhysioNet dataset. It was seen that the findings by the developed AEN based solution were better than the alternative solutions from the literature. Additionally, usability of the whole IoHT system was found positive by the doctors, and according to the 479 real-case applications, the system was able to achieve accuracy rates of 96.03% for normal heart sounds, 91.91% for extrasystole, and 90.11% for murmur. In terms of security approach, the system was also robust against several attacking methods including synthetic data impute as well as trying to penetrating to the system via central system or mobile devices.
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Affiliation(s)
| | - Utku Kose
- Suleyman Demirel University, Isparta, Turkey
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, Delhi, India
| | - Ashish Khanna
- Maharaja Agrasen Institute of Technology, Delhi, India
| | - Arun Kumar Sangaiah
- School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India
- Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Taiwan
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Rujoie A, Fallah A, Rashidi S, Rafiei Khoshnood E, Seifi Ala T. Classification and evaluation of the severity of tricuspid regurgitation using phonocardiogram. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101688] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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7
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Cardiac valves disorder classification based on active valves appearance periodic sequences tree of murmurs. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101775] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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8
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Dong F, Qian K, Ren Z, Baird A, Li X, Dai Z, Dong B, Metze F, Yamamoto Y, Schuller B. Machine Listening for Heart Status Monitoring: Introducing and Benchmarking HSS - the Heart Sounds Shenzhen Corpus. IEEE J Biomed Health Inform 2019; 24:2082-2092. [PMID: 31765322 DOI: 10.1109/jbhi.2019.2955281] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Auscultation of the heart is a widely studied technique, which requires precise hearing from practitioners as a means of distinguishing subtle differences in heart-beat rhythm. This technique is popular due to its non-invasive nature, and can be an early diagnosis aid for a range of cardiac conditions. Machine listening approaches can support this process, monitoring continuously and allowing for a representation of both mild and chronic heart conditions. Despite this potential, relevant databases and benchmark studies are scarce. In this paper, we introduce our publicly accessible database, the Heart Sounds Shenzhen Corpus (HSS), which was first released during the recent INTERSPEECH 2018 ComParE Heart Sound sub-challenge. Additionally, we provide a survey of machine learning work in the area of heart sound recognition, as well as a benchmark for HSS utilising standard acoustic features and machine learning models. At best our support vector machine with Log Mel features achieves 49.7% unweighted average recall on a three category task (normal, mild, moderate/severe).
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Gharehbaghi A, Lindén M, Babic A. An artificial intelligent-based model for detecting systolic pathological patterns of phonocardiogram based on time-growing neural network. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105615] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Vennemann B, Obrist D, Rösgen T. Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning. PLoS One 2019; 14:e0222983. [PMID: 31557196 PMCID: PMC6762068 DOI: 10.1371/journal.pone.0222983] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 09/11/2019] [Indexed: 11/28/2022] Open
Abstract
The blood flow through the major vessels holds great diagnostic potential for the identification of cardiovascular complications and is therefore routinely assessed with current diagnostic modalities. Heart valves are subject to high hydrodynamic loads which render them prone to premature degradation. Failing native aortic valves are routinely replaced with bioprosthetic heart valves. This type of prosthesis is limited by a durability that is often less than the patient's life expectancy. Frequent assessment of valvular function can therefore help to ensure good long-term outcomes and to plan reinterventions. In this article, we describe how unsupervised novelty detection algorithms can be used to automate the interpretation of blood flow data to improve outcomes through early detection of adverse cardiovascular events without requiring repeated check-ups in a clinical environment. The proposed method was tested in an in-vitro flow loop which allowed simulating a failing aortic valve in a laboratory setting. Aortic regurgitation of increasing severity was deliberately introduced with tube-shaped inserts, preventing complete valve closure during diastole. Blood flow recordings from a flow meter at the location of the ascending aorta were analyzed with the algorithms introduced in this article and a diagnostic index was defined that reflects the severity of valvular degradation. The results indicate that the proposed methodology offers a high sensitivity towards pathological changes of valvular function and that it is capable of automatically identifying valvular degradation. Such methods may be a step towards computer-assisted diagnostics and telemedicine that provide the clinician with novel tools to improve patient care.
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Affiliation(s)
- Bernhard Vennemann
- Institute of Fluid Dynamics, ETH Zürich, Zürich, Switzerland
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Dominik Obrist
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Thomas Rösgen
- Institute of Fluid Dynamics, ETH Zürich, Zürich, Switzerland
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Ahmad MS, Mir J, Ullah MO, Shahid MLUR, Syed MA. An efficient heart murmur recognition and cardiovascular disorders classification system. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:733-743. [DOI: 10.1007/s13246-019-00778-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 06/24/2019] [Indexed: 11/24/2022]
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12
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Gharehbaghi A, Linden M. A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:4102-4115. [PMID: 29035230 DOI: 10.1109/tnnls.2017.2754294] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
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.
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13
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Moghaddasi H, Nourian S. Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos. Comput Biol Med 2016; 73:47-55. [DOI: 10.1016/j.compbiomed.2016.03.026] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Revised: 03/29/2016] [Accepted: 03/30/2016] [Indexed: 10/22/2022]
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14
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An Intelligent Phonocardiography for Automated Screening of Pediatric Heart Diseases. J Med Syst 2015; 40:16. [PMID: 26573653 DOI: 10.1007/s10916-015-0359-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2014] [Accepted: 09/30/2015] [Indexed: 10/22/2022]
Abstract
This paper presents a robust device for automated screening of pediatric heart diseases based on our unique processing method in murmur characterization; the Arash-Band method. The present study modifies the Arash-Band method and employs output of the modified method in conjunction with the two other original techniques to extract indicative feature vectors for the screening. The extracted feature vectors are classified by using the support vector machine method. Results show that the proposed modifications significantly enhances performance of the Arash-Band in terms of the both accuracy and sensitivity as the corresponding effect sizes are sufficiently large. The proposed algorithm has been incorporated into an Android-based tablet to constitute an intelligent phonocardiogram with the automatic screening capability. In order to obtain confidence interval of the accuracy and sensitivity, an inferable statistical test is applied on our database containing the phonocardiogram signals recorded from 263 of the referrals to a hospital. The expected value of the accuracy/sensitivity is estimated to be 87.45 % / 87.29 % with a 95 % confidence interval of (80.19 % - 92.47 %) / (76.01 % - 95.78 %) exhibiting superior performance than a pediatric cardiologist who relies on conventional or even computer-assisted auscultation.
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A Novel Method for Screening Children with Isolated Bicuspid Aortic Valve. Cardiovasc Eng Technol 2015; 6:546-56. [PMID: 26577485 DOI: 10.1007/s13239-015-0238-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Accepted: 07/15/2015] [Indexed: 10/23/2022]
Abstract
This paper presents a novel processing method for heart sound signal: the statistical time growing neural network (STGNN). The STGNN performs a robust classification by merging supervised and unsupervised statistical methods to overcome non-stationary behavior of the signal. By combining available preprocessing and segmentation techniques and the STGNN classifier, we build an automatic tool for screening children with isolated BAV, the congenital heart malformation which can lead to serious cardiovascular lesions. Children with BAV (22 individuals) and healthy condition (28 individuals) are subjected to the study. The performance of the STGNN is compared to that of a time growing neural network (CTGNN) and a conventional support vector (CSVM) machine, using balanced repeated random sub sampling. The average of the accuracy/sensitivity is estimated to be 87.4/86.5 for the STGNN, 81.8/83.4 for the CTGNN, and 72.9/66.8 for the CSVM. Results show that the STGNN offers better performance and provides more immunity to the background noise as compared to the CTGNN and CSVM. The method is implementable in a computer system to be employed in primary healthcare centers to improve the screening accuracy.
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