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The Effect of Signal Duration on the Classification of Heart Sounds: A Deep Learning Approach

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article number2261
Number of pages14
JournalSENSORS
Volume22
Issue number6
DOIs
Accepted/In press12 Mar 2022
Published15 Mar 2022

Bibliographical note

Funding Information: This research was funded by The King?s?China Scholarship Council. Funding Information: Funding: This research was funded by The King’s–China Scholarship Council. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. Funding Information: Funding: This research was funded by The King’s–China Scholarship Council. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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Abstract

Deep learning techniques are the future trend for designing heart sound classification methods, making conventional heart sound segmentation dispensable. However, despite using fixed signal duration for training, no study has assessed its effect on the final performance in detail. Therefore, this study aims at analysing the duration effect on the commonly used deep learning methods to provide insight for future studies in data processing, classifier, and feature selection. The results of this study revealed that (1) very short heart sound signal duration (1 s) weakens the performance of Recurrent Neural Networks (RNNs), whereas no apparent decrease in the tested Convolutional Neural Network (CNN) model was found. (2) RNN outperformed CNN using Mel-frequency cepstrum coefficients (MFCCs) as features. There was no difference between RNN models (LSTM, BiLSTM, GRU, or BiGRU). (3) Adding dynamic information (∆ and ∆ 2 MFCCs) of the heart sound as a feature did not improve the RNNs’ performance, and the improvement on CNN was also minimal (≤2.5% in MAcc). The findings provided a theoretical basis for further heart sound classification using deep learning techniques when selecting the input length.

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