P204 Automated detection of atrial fibrillation based on stationary wavelet transform and artificial neural network targeted for embedded system-on-chip technology

Hau, Yuan Wen; Lim, Huey Woan; Lim, C.W.; Kasim, Sazzli

doi:10.1093/ehjci/ehz872.075

Cited by 1 publication

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“…Most classical machine learning methods based on the extraction of features from single-channel ECG signal have been proposed, such as random forest [7,8] , support vector machines (SVMs) [9,10], artificial neural networks [11,12,13], KNN [14,15,16], and hidden Markov models [17,18]. All these studies have achieved much better performances.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Automatic Detection of Arrhythmias Using the Unsupervised Convolutional Neural Network

Zhang

Yao

Gao

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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In recent years, various models based on convolutional neural networks (CNN) have been proposed to solve the cardiac arrhythmia detection problem and achieved saturated accuracy. However, these models are often viewed as “blackbox” and lack of interpretability, which hinders the understanding of cardiologists, and ultimately hinders the clinical use of intelligent terminals. At the same time, most of these approaches are supervised learning and require label data. It is a time-consuming and expensive process to obtain label data. Furthermore, in human visual cortex, the importance of lateral connection is same as feed-forward connection. Until now, CNN based on lateral connection have not been studied thus far. Consequently, in this paper, we combines CNNs, lateral connection and autoencoder (AE) to propose the building blocks of lateral connection convolutional autoencoder neural networks (LCAN) for cardiac arrhythmia detection, which learn representations in an unsupervised manner. Concretely, the LCAN contains a convolution layer, a lateral connection layer, an AE layer, and a pooling layer. The LCAN detects salient wave features through the lateral connection layer. The AE layer and competitive learning is used to update the filters of the convolution network—an unsupervised process that ensures similar weight distribution for all adjacent filters in each convolution layer and realizes the neurons’ semantic arrangement in the LCAN. To evaluate the performances of the proposed model, we have implemented the experiments on the well-known MIT–BIH Arrhythmia Database. The proposed model yields total accuracies and kappa coefficients of 98% and 0.95, respectively. The experiment results show that the LCAN is not only effective, but also a useful tool for arrhythmia detection.

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