Automatic Detection of Heartbeats in Heart Sound Signals Using Deep Convolutional Neural Networks

Abstract

The analysis of non-stationary signals commonly includes the signal segmentation process, dividing such signals into smaller time series, which are considered stationary and thus easier to process. Most commonly, the methods for signal segmentation utilize complex filtering, transformation and feature extraction techniques together with various kinds of classifiers, which especially in the field of biomedical signals, do not perform very well and are generally prone to poor performance when dealing with signals obtained in highly variable environments. In order to address these problems, we designed a new method for the segmentation of heart sound signals using deep convolutional neural networks, which works in a straightforward automatic manner and does not require any complex pre-processing. The proposed method was tested on a set of heartbeat sound clips, collected by non-experts with mobile devices in highly variable environments with excessive background noise. The obtained results show that the proposed method outperforms other methods, which are taking advantage of using domain knowledge for the analysis of the signals. Based on the encouraging experimental results, we believe that the proposed method can be considered as a solid basis for the further development of the automatic segmentation of highly variable signals using deep neural networks.

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Transfer Learning With Adaptive Fine-Tuning

“…. The training parameters' values were picked based on previous experiences utilizing CNNs for various image recognition tasks[10],[57],[58].VOLUME 4, 2016This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification

“…In particular, different feature maps (C1/C2: 12/6, 6/6, 6/12, 3/6, and 6/3) were convoluted with different sizes of kernels (3×9, 3×22, 5×9, and 5×11) [18]; a CNN with many different convolutional layers was applied, in which authors proposed to setup parameters of the model using the kernel size of 5 for all layers [19]; there were many kernels with different lengths for convolutional layers, particularly the first convolutional layer used 20 kernels with the same size of 661 and different features for 20 convolutional times. This first layer was connected to the second convolutional layer using 50 kernels with the same size of 440 [20].…”

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