Heart Sound Segmentation—An Event Detection Approach Using Deep Recurrent Neural Networks

Messner, Elmar; Zöhrer, Matthias; Pernkopf, Franz

doi:10.1109/tbme.2018.2843258

Cited by 120 publications

(79 citation statements)

References 39 publications

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“…There have been reports on the use of deep learning for digitizing visual examination results in dermatology, as well as in analyzing heart sounds for the estimation of heart disease [ 21 , 22 , 23 ]. However, this approach requires that only useful heart sounds should be extracted for a consultation record, and the preprocessing required for a trained classifier also hinders use in clinical settings.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Hemodialysis Arteriovenous Bruit by Deep Learning

Ota

Nishiura

Ishihara

et al. 2020

Sensors

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Physical findings of auscultation cannot be quantified at the arteriovenous fistula examination site during daily dialysis treatment. Consequently, minute changes over time cannot be recorded based only on subjective observations. In this study, we sought to supplement the daily arteriovenous fistula consultation for hemodialysis patients by recording the sounds made by the arteriovenous fistula and evaluating the sounds using deep learning methods to provide an objective index. We sampled arteriovenous fistula auscultation sounds (192 kHz, 24 bits) recorded over 1 min from 20 patients. We also extracted arteriovenous fistula sounds for each heartbeat without environmental sound by using a convolutional neural network (CNN) model, which was made by comparing these sound patterns with 5000 environmental sounds. The extracted single-heartbeat arteriovenous fistula sounds were sent to a spectrogram and scored using a CNN learning model with bidirectional long short-term memory, in which the degree of arteriovenous fistula stenosis was assigned to one of five sound types (i.e., normal, hard, high, intermittent, and whistling). After 100 training epochs, the method exhibited an accuracy rate of 70–93%. According to the receiver operating characteristic (ROC) curve, the area under the ROC curves (AUC) was 0.75–0.92. The analysis of arteriovenous fistula sound using deep learning has the potential to be used as an objective index in daily medical care.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Hemodialysis Arteriovenous Bruit by Deep Learning

Ota

Nishiura

Ishihara

et al. 2020

Sensors

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“…Hidden semi Markov model-based temporal modeling is applied to the output of the DNN for classification of FHSS. In [11] seven different features extracted from PCG are fed to DNN for classification of FHSS. In [12] nine different feature selection algorithms used for choosing the effective features for classification of S1 and S2 sound.…”

Section: Motivationmentioning

confidence: 99%

Automatic Recognition of Fundamental Heart Sound Segments From PCG Corrupted With Lung Sounds and Speech

Babu

Ramkumar

2020

IEEE Access

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Automated recognition of fundamental heart sound segments (FHSS) from Phonocardiogram (PCG) is the preliminary step before clinical parameters extraction to detect the presence of abnormality if any. PCG acquisition systems are usually based on microphones. These microphones apart from cardiac sounds will also pick up non-cardiac sounds like lung sounds and speech. The recognition of FHSS is challenging in the presence of non-cardiac events. Deep learning techniques like convolutional neural network (CNN) and recurrent neural network (RNN) are suitable for automated FHSS. However, it will be shown that their performance is degraded in the presence of interference like lung sounds, and speech. Hence in this work, a combination of conventional signal processing technique with deep neural network (DNN) is proposed to enhance the accuracy of automated FHSS. The conventional signal processing technique is based on EWT which can adaptively design the filter banks based on the type of interference. For DNN, U-Net is considered. The method involves the segmentation of PCG using EWT and recognition of FHSS using U-Net based DNN. Envelope features are extracted from the EWT based reconstructed signal and used for training the U-Net based DNN to recognize FHSS. To further improve the recognition accuracy of FHSS, delineation parameters obtained from EWT are incorporated for temporal modeling with the outcomes of U-Net based DNN. The performance of the proposed method is analyzed using both real-time signals and signals taken from standard databases like the Physionet database, and Littmann's lung sound library. Realtime PCG is acquired using an in-house developed PCG acquisition system. The proposed U-Net based DNN with the EWT method achieves FHSS recognition accuracy of 91.17% for PCG with lung sound interference and 90.78% for PCG with speech interference. The proposed method significantly improves the accuracy of FHSS recognition compared to long short term memory (LSTM), and gated recurrent unit (GRU). INDEX TERMS Recognition, segmentation, fundamental heart sound, lung sound, speech, empirical wavelet transform (EWT), deep neural network (DNN).

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“…In the past few decades, the study of HS abnormality arXiv:1910.00498v1 [eess.SP] 29 Sep 2019 detection has been mostly focused on PCG segmentation [6], and binary classification of PCG as pathologic (Abnormal) or physiologic (Normal) [7]. In 2016, the Physionet/CinC Challenge was organized and an archive of 4430 PCG recordings, acquired using seven different stethoscope models, was released for binary classification of heart sounds (HS).…”

Section: Introductionmentioning

confidence: 99%

Towards Domain Invariant Heart Sound Abnormality Detection Using Learnable Filterbanks

Humayun

Ghaffarzadegan

Ansari

et al. 2020

IEEE J. Biomed. Health Inform.

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Objective: Cardiac auscultation is the most practiced non-invasive and cost-effective procedure for the early diagnosis of heart diseases. While machine learning based systems can aid in automatically screening patients, the robustness of these systems is affected by numerous factors including the stethoscope/sensor, environment and data collection protocol. This paper studies the adverse effect of domain variability on heart sound classification and develops strategies to address this problem. Methods: We propose a novel Convolutional Neural Network (CNN) layer, consisting of time-convolutional (tConv) units, that emulate Finite Impulse Response (FIR) filters. The filter coefficients can be updated via backpropagation and be stacked in the front-end of the network as a learnable filterbank. These filters can incorporate properties such as linear/zero phase-response and symmetry while improving robustness due to domain variability. Results: Our methods are evaluated using multi-domain heart sound recordings obtained from publicly available phonocardiogram (PCG) datasets. On multi-domain evaluation tasks, the proposed method surpasses the top-scoring systems found in the literature for heart sound classification. Our systems achieved relative improvements of up to 11.84% in terms of a modified accuracy (Macc) metric, compared to state-of-theart methods. Conclusion: The results demonstrate the effectiveness of the proposed learnable filterbank CNN architecture in achieving robustness towards sensor/domain variability in PCG signals. Significance: The proposed methods pave the way for deploying automated cardiac screening systems in diversified and underserved communities.

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Heart Sound Segmentation—An Event Detection Approach Using Deep Recurrent Neural Networks

Abstract: In this work, we introduce a new methodology for the segmentation of heart sounds, suggesting an event detection approach with DRNNs using spectral or envelope features.

Cited by 120 publications

References 39 publications

Evaluation of Hemodialysis Arteriovenous Bruit by Deep Learning

Evaluation of Hemodialysis Arteriovenous Bruit by Deep Learning

Automatic Recognition of Fundamental Heart Sound Segments From PCG Corrupted With Lung Sounds and Speech

Towards Domain Invariant Heart Sound Abnormality Detection Using Learnable Filterbanks

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