Classification of Heart Sounds Using Convolutional Neural Network

Fan, Li; Tang, Hong; Shang, Shang; Mathiak, Klaus; Cong, Fengyu

doi:10.3390/app10113956

Cited by 71 publications

(27 citation statements)

References 35 publications

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“…In recent years, many studies have been conducted to automatically predict cardiac diseases on the basis of the heartbeat [8,9]. Recently, with the development of deep learning, many deep learning-based approaches have been studied to detect abnormal heart sound using deep neural networks (DNN) [10], recurrent neural networks (RNN) [11], and convolutional neural networks (CNN) [12,13]. As CNN extracts and learns features autonomously, it has been employed in various fields, such as image classification and speech recognition.…”

Section: Related Workmentioning

confidence: 99%

“…It is also used to recognize abnormal heart sounds. In [12], CNN was used for feature extraction and classification function estimation from heart sound signals. The network of [13] was designed to classify normal and abnormal heart The main objective of this work is to detect cardiac abnormalities simply by the sound of the heart and prevent cardiovascular disease through early detection.…”

Section: Related Workmentioning

confidence: 99%

“…It is also used to recognize abnormal heart sounds. In [12], CNN was used for feature extraction and classification function estimation from heart sound signals. The network of [13] was designed to classify normal and abnormal heart sounds by using CNN with the mel-frequency cepstral coefficient (MFCC) heat map as input.…”

Section: Related Workmentioning

confidence: 99%

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Methods for Improving Deep Learning-Based Cardiac Auscultation Accuracy: Data Augmentation and Data Generalization

Jeong¹,

Kim²,

Kim³

et al. 2021

Applied Sciences

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Cardiac auscultation is a cost-effective and noninvasive technique for cardiovascular disease detection. Recently, various studies have been underway for cardiac auscultation using deep learning, not doctors. When training a deep learning network, it is important to secure large amount of high-quality data. However, medical data are difficult to obtain, and in most cases the number of abnormal classes is insufficient. In this study, data augmentation is used to supplement the insufficient amount of data, and data generalization to generate data suitable for convolutional neural networks (CNN) is proposed. We demonstrate performance improvements by inputting them into the CNN. Our method achieves an overall performance of 96%, 81%, and 90% for sensitivity, specificity, and F1-score, respectively. Diagnostic accuracy was improved by 18% compared to when it was not used. Particularly, it showed excellent detection success rate for abnormal heart sounds. The proposed method is expected to be applied to an automatic diagnosis system to detect heart abnormalities and help prevent heart disease through early detection.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Methods for Improving Deep Learning-Based Cardiac Auscultation Accuracy: Data Augmentation and Data Generalization

Jeong¹,

Kim²,

Kim³

et al. 2021

Applied Sciences

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“…The structure of CNN is directly used to obtain the sound characteristics able to minimize segmentation errors from the heart sound signal itself or the features extracted from it, when completing the segmentation of the heart sound [19]. [31,32,33]. Compared with the heart sound classification without segmentation, the heart sound classification including the segmentation can obtain the state mark of the heart sound, which enables the clinicians to locate the abnormality part of the heart sound, such as diastolic or systolic murmur, and contributes to further determining the position of the heart valve that results in the disease.…”

Section: Introductionmentioning

confidence: 99%

Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning

Zhang

et al. 2021

Applied Sciences

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The heart sound signal is one of the signals that reflect the health of the heart. Research on the heart sound signal contributes to the early diagnosis and prevention of cardiovascular diseases. As a commonly used deep learning network, convolutional neural network (CNN) has been widely used in images. In this paper, the method of analyzing heart sound through using CNN has been studied. Firstly, the original data set was preprocessed, and then the heart sounds were segmented on U-net, based on the deep CNN. Finally, the classification of heart sounds was completed through CNN. The data from 2016 PhysioNet/CinC Challenge was utilized for algorithm validation, and the following results were obtained. When the heart sound segmented, the overall accuracy rate was 0.991, the accuracy of the first heart sound was 0.991, the accuracy of the systolic period was 0.996, the accuracy of the second heart sound was 0.996, and the accuracy of the diastolic period was 0.997, and the average accuracy rate was 0.995; While in classification, the accuracy was 0.964, the sensitivity was 0.781, and the specificity was 0.873. These results show that deep learning based on CNN shows good performance in the segmentation and classification of the heart sound signal.

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“…As seen in the examples above, CNN is practically used as a deep learning method in classifying heart sounds. Recently, there is still great interest in running alternative heart sound classification solutions developed with CNN [71] , [72] , Recurrent CNN [73] , general CNN models [74] , [75] , Deep Neural Network (DNN) [76] , Long Short-Term Memory [77] , and AEN [78] , [79] , [80] . In this study, an alternative model of AEN was used to directly classify heart sound data without ever dealing with images.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network

Deperlioğlu

Köse

Gupta

et al. 2020

Computer Communications

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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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Classification of Heart Sounds Using Convolutional Neural Network

Cited by 71 publications

References 35 publications

Methods for Improving Deep Learning-Based Cardiac Auscultation Accuracy: Data Augmentation and Data Generalization

Methods for Improving Deep Learning-Based Cardiac Auscultation Accuracy: Data Augmentation and Data Generalization

Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning

Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network

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