S1 and S2 Heart Sound Recognition Using Deep Neural Networks

Chen, Tien-En; Yang, Shih-I; Ho, Li‐Ting; Tsai, Kun-Hsi; Chen, Yu-Hsuan; Chang, Yun-Fan; Lai, Ying‐Hui; Wu, Chau-Chung

doi:10.1109/tbme.2016.2559800

Cited by 137 publications

(28 citation statements)

References 26 publications

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“…For example, many algorithms that claim to automatically recognize and classify medical images have been developed using deep learning 20 – 22 . Recent efforts have shown significant advances using artificial neural networks (ANNs) or deep neural networks (DNNs) to detect and classify heart sounds 23 – 25 . Convolutional neural networks (CNNs) have also been used to identify heart murmurs 26 .…”

Section: Introductionmentioning

confidence: 99%

Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling

Wang

Chang²,

Tsai³

et al. 2020

Sci Rep

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Recognizing specific heart sound patterns is important for the diagnosis of structural heart diseases. However, the correct recognition of heart murmur depends largely on clinical experience. Accurately identifying abnormal heart sound patterns is challenging for young and inexperienced clinicians. This study is aimed at the development of a novel algorithm that can automatically recognize systolic murmurs in patients with ventricular septal defects (VSDs). Heart sounds from 51 subjects with VSDs and 25 subjects without a significant heart malformation were obtained in this study. Subsequently, the soundtracks were divided into different training and testing sets to establish the recognition system and evaluate the performance. The automatic murmur recognition system was based on a novel temporal attentive pooling-convolutional recurrent neural network (TAP-CRNN) model. On analyzing the performance using the test data that comprised 178 VSD heart sounds and 60 normal heart sounds, a sensitivity rate of 96.0% was obtained along with a specificity of 96.7%. When analyzing the heart sounds recorded in the second aortic and tricuspid areas, both the sensitivity and specificity were 100%. We demonstrated that the proposed TAP-CRNN system can accurately recognize the systolic murmurs of VSD patients, showing promising potential for the development of software for classifying the heart murmurs of several other structural heart diseases.

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Section: Introductionmentioning

confidence: 99%

Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling

Wang

Chang²,

Tsai³

et al. 2020

Sci Rep

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“…The recorded data was sampled with 4 kHz. Chen et al proposed that sampling rates above 5 kHz are not sufficient for heart sound recording, since for higher sampling rates, irrelevant sound components can be included [7]. The integrated microphone of the stethoscope amplifies frequencies between 20-200 Hz, since heart sounds are within this frequency range (see Section 2.1).…”

Section: Measurement Devices and Pc Setupmentioning

confidence: 99%

“…Only Springer et al used a larger database than the proposed one [12]. Furthermore, in many approaches very short recordings are included in their database, for example~1 s by Renna et al [14], or in total 87 heart sounds by Chen et al [7]. Moreover, many researchers use a database like PhysioNet and do not declare their study population or recording length [6,9,39].…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

“…Feature-based methods, as e.g., proposed by [6][7][8][9][10], extract features such as the Shannon entropy, discrete wavelet transform (DWT), continuous wavelet transform (CWT) or mel-frequency cepstral coefficients (MFCC) out of the PCG signal. With the use of classifiers (for example support vector machine (SVM), twin support vector machine (TWSVM) or deep neural networks (DNN)), it can be determined, if the features correspond to a heart sound [5].…”

Section: Introductionmentioning

confidence: 99%

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A Robust and Real-Time Capable Envelope-Based Algorithm for Heart Sound Classification: Validation under Different Physiological Conditions

Thalmayer

Zeising

Fischer

et al. 2020

Sensors

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This paper proposes a robust and real-time capable algorithm for classification of the first and second heart sounds. The classification algorithm is based on the evaluation of the envelope curve of the phonocardiogram. For the evaluation, in contrast to other studies, measurements on 12 probands were conducted in different physiological conditions. Moreover, for each measurement the auscultation point, posture and physical stress were varied. The proposed envelope-based algorithm is tested with two different methods for envelope curve extraction: the Hilbert transform and the short-time Fourier transform. The performance of the classification of the first heart sounds is evaluated by using a reference electrocardiogram. Overall, by using the Hilbert transform, the algorithm has a better performance regarding the F1-score and computational effort. The proposed algorithm achieves for the S1 classification an F1-score up to 95.7% and in average 90.5%. The algorithm is robust against the age, BMI, posture, heart rate and auscultation point (except measurements on the back) of the subjects.

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“…Many processing and classification methods about heart sounds have been proposed such as wavelet transform (WT), hidden semi-Markov model (HSMM), logistic regression (LR), Mel-Frequency Cepstral Coefficients (MFCC), ensemble empirical mode decomposition (EEMD), deep neural network (DNN), deep convolutional neural network (CNN), Multi-fractal decomposition, Shannon energy and SVM [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. The feature extraction methods were mainly based on these features, including short-time Fourier transform (STFT) features, kurtosis features, the wavelet features, deep structured features and the statistical features [ 3 , 5 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Heart Sounds Based on the Wavelet Fractal and Twin Support Vector Machine

2019

Entropy

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Heart is an important organ of human beings. As more and more heart diseases are caused by people’s living pressure or habits, the diagnosis and treatment of heart diseases also require technical improvement. In order to assist the heart diseases diagnosis, the heart sound signal is used to carry a large amount of cardiac state information, so that the heart sound signal processing can achieve the purpose of heart diseases diagnosis and treatment. In order to quickly and accurately judge the heart sound signal, the classification method based on Wavelet Fractal and twin support vector machine (TWSVM) is proposed in this paper. Firstly, the original heart sound signal is decomposed by wavelet transform, and the wavelet decomposition coefficients of the signal are extracted. Then the two-norm eigenvectors of the heart sound signal are obtained by solving the two-norm values of the decomposition coefficients. In order to express the feature information more abundantly, the energy entropy of the decomposed wavelet coefficients is calculated, and then the energy entropy characteristics of the signal are obtained. In addition, based on the fractal dimension, the complexity of the signal is quantitatively described. The box dimension of the heart sound signal is solved by the binary box dimension method. So its fractal dimension characteristics can be obtained. The above eigenvectors are synthesized as the eigenvectors of the heart sound signal. Finally, the twin support vector machine (TWSVM) is applied to classify the heart sound signals. The proposed algorithm is verified on the PhysioNet/CinC Challenge 2016 heart sound database. The experimental results show that this proposed algorithm based on twin support vector machine (TWSVM) is superior to the algorithm based on support vector machine (SVM) in classification accuracy and speed. The proposed algorithm achieves the best results with classification accuracy 90.4%, sensitivity 94.6%, specificity 85.5% and F1 Score 95.2%.

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S1 and S2 Heart Sound Recognition Using Deep Neural Networks

Abstract: The proposed DNN-based method can achieve reliable S1 and S2 recognition performance based on acoustic characteristics without using an ECG reference or incorporating the assumptions of the individual durations of S1 and S2 and time intervals of S1-S2 and S2-S1.

Cited by 137 publications

References 26 publications

Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling

Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling

A Robust and Real-Time Capable Envelope-Based Algorithm for Heart Sound Classification: Validation under Different Physiological Conditions

Classification of Heart Sounds Based on the Wavelet Fractal and Twin Support Vector Machine

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