Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features.

IF 2.3 4区 医学 Q3 BIOPHYSICS Physiological measurement Pub Date : 2024-05-24 DOI:10.1088/1361-6579/ad46e1
Mingfeng Jiang, Feibiao Bian, Jucheng Zhang, Tianhai Huang, Ling Xia, Yonghua Chu, Zhikang Wang, Jun Jiang
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Abstract

Objective.Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).Approach.A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result.Main results.According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset.Significance.This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.

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基于连续 T 波区域特征和多导联融合深度特征的心肌梗塞检测方法
目的:心肌梗塞(MI)是威胁最大的心血管疾病之一。本文旨在探索一种基于心电图(ECG)的自主心肌梗死分类算法:方法:本文提出了一种融合连续 T 波区域(C_TWA)特征和心电图深度特征的心肌梗死检测方法。该方法主要由三部分组成:(1)心肌梗死的发生往往伴随着心电图中 T 波形状的变化,因此不同心搏所显示的 T 波区域会有很大差异。自适应滑动窗口法用于检测 T 波的起始和终止,并计算同一心电图记录上的 C_TWA。此外,C_TWA 的变异系数 (CV) 被定义为心电图的 C_TWA 特征。(2) 采用多导联融合卷积神经网络(Multi-lead-fusion CNN)提取心电图的深层特征。(3) 通过软关注融合心电图的 C_TWA 特征和深层特征,然后输入多层感知器,得到检测结果:根据患者间范例,提出的方法在 PTB 数据集上达到了 97.67% 的准确率、96.59% 的精确率和 98.96% 的召回率,而提出的方法在临床数据集上达到了 93.15% 的准确率、93.20% 的精确率和 95.14% 的召回率:意义:所提出的方法准确提取了C_TWA的特征,并结合了信号的深层特征,从而提高了检测精度,在临床数据集上取得了理想的效果。
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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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