Automated cardiac condition diagnosis using AI based ECG analysis system for school children

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI:10.1109/ICSCC51209.2021.9528098

Praveen Mohandas, A. R, Antony John, Midhun K. Madhu, Gylson Thomas, Venugopalan Kurupath

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引用次数: 2

Abstract

Major focus of this paper is in the development and testing of a prototype of Electrocardiogram (ECG) machine intended for automatic analysis of cardiovascular diseases by applying artificial intelligence. The objective of the work is in cardiac screening of school children at rural areas, in order to detect the cardiac diseases at its early stages. This work has focused to differentiate ECG signals of people into arrhythmia affected, congestive heart failure, and normal sinus rhythm. For feature extraction from ECG signal, wavelet time scattering methodology has been used and a Support Vector Machine (SVM) classifier is employed to accurately distinguish between ECG signals, which were carried out in MATLAB toolbox. A hardware system of interfaced ARDUINO UNO and ECG sensor AD8232 has been developed and the entire system is tested on group members and predictions were made accurately. Testing with school children is pending due to concerns about COVID-19 safety issues.

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基于人工智能的学童心电分析系统的自动心脏病诊断

本文主要研究了一种应用人工智能自动分析心血管疾病的心电图机样机的开发和测试。这项工作的目的是对农村地区的学龄儿童进行心脏筛查，以便在早期阶段发现心脏病。这项工作的重点是区分人的心电图信号为心律失常影响，充血性心力衰竭，和正常的窦性心律。在心电信号的特征提取中，采用小波时间散射方法，利用支持向量机分类器对心电信号进行准确区分，并在MATLAB工具箱中实现。开发了ARDUINO UNO接口和心电传感器AD8232的硬件系统，并对整个系统在组成员身上进行了测试，预测准确。由于担心COVID-19的安全问题，对学龄儿童的测试正在等待。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 8th International Conference on Smart Computing and Communications (ICSCC)

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