医学智能心电分析: 数据、方法与应用

doi:10.24920/004160

摘要/Abstract

摘要：

心电图（electrocardiogram，ECG）是一种低成本、简单、快速、无创的检查方法。它可以反映心脏的电活动，为整个身体的健康状况提供有价值的诊断线索。因此，ECG已广泛应用于各种生物医学领域，如心律失常检测、疾病特异性检测、死亡率预测、生物特征识别等。近年来，ECG相关的研究使用了各种公开可用的数据集，在使用的数据集、数据预处理方法、有针对性的挑战以及建模和分析技术方面存在许多差异。本文系统地总结和分析了基于心电图的自动分析方法和应用。具体而言，我们首先回顾了部分常用的ECG公共数据集，并概述了数据预处理过程。然后，我们介绍了心电信号的一些最广泛的应用，并分析了这些应用中所涉及的先进方法。最后，我们阐述了心电图分析的一些挑战，并提出了进一步研究的建议。

关键词: 心电图, 数据库, 预处理, 机器学习, 医疗大数据分析

Abstract:

Electrocardiogram (ECG) is a low-cost, simple, fast, and non-invasive test. It can reflect the heart's electrical activity and provide valuable diagnostic clues about the health of the entire body. Therefore, ECG has been widely used in various biomedical applications such as arrhythmia detection, disease-specific detection, mortality prediction, and biometric recognition. In recent years, ECG-related studies have been carried out using a variety of publicly available datasets, with many differences in the datasets used, data preprocessing methods, targeted challenges, and modeling and analysis techniques. Here we systematically summarize and analyze the ECG-based automatic analysis methods and applications. Specifically, we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes. Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications. Finally, we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

Key words: Electrocardiogram, database, preprocessing, machine learning, medical big data analysis

Yu-Xia Guan, Ying An, Feng-Yi Guo, Wei-Bai Pan, Jian-Xin Wang. Intelligent Electrocardiogram Analysis in Medicine: Data, Methods, and Applications[J].Chinese Medical Sciences Journal, 2023, 38(1): 38-48.

图/表 2

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Datasets	Records	Length	Frequency (Hz)	Leads	Labeling method	Applications
CU Ventricular Tachyarrhythmia^[4]	35	8 min	250	1	BBL; ML	Arrhythmia detection
MIT-BIH Arrhythmia^[4]	48	30 min	360	2	BBL; ML	Arrhythmia detection
MIT-BIH Long Term^[4]	7	14-22 h	128	2	BBL; ML	Arrhythmia detection
MIT-BIH Supraventricular Arrhythmia^[4]	78	30 min	128	2	BBL; ML	Arrhythmia detection
Sudden Cardiac Death Holter^[4]	23	Up to 24 h	250	2	BBL; ML	Arrhythmia detection
BIDMC Congestive Heart Failure^[4]	15	20 h	250	2	BBL; ML	Arrhythmia detection
American Heart Association (AHA)^[4]	155	3 h	250	2	BBL; ML	Arrhythmia detection
St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia (INCART)^[4]	75	30 min	257	12	BBL; ML	Arrhythmia detection
The China Physiological Signal Challenge 2018^[4]	6877	6-60 s	500	12	RRL; ML	Arrhythmia detection
Shaoxing People's Hospital's 10,000 patients arrhythmia database^[5]	10,646	10 s	500	12	RRL; SL	Arrhythmia detection
Ningbo First Hospital (Ningbo)^[6]	34,905	10 s	500	12	RRL; ML	Arrhythmia detection
MIT-BIH Atrial Fibrillation (AF)^[4]	25	10 h	250	2	BBL; ML	AF detection
Intracardiac Atrial Fibrillation^[4]	8	1 min	128	2	RRL; ML	AF detection
Long Term Atrial Fibrillation (AF)^[4]	84	24 h	128	2	BBL; ML	AF detection
PTB-XL^[7]	21,837	10 s	1000	12	RRL; ML	MI detection
PhysioNet Apnea-ECG^[4]	70	401-587 min	100	N/A	ML	Apnea detection
MIT-BIH Noise Stress Test (NST)^[4]	15	30 min	360	2	-	Signal denoising
QT^[4]	100	15 min	250	2	-	Signal denoising
Abdominal and Direct Fetal ECG (ADFE)^[4]	5	10 min	1000	5	-	Fetal ECG extraction
Fetal electrocardiograms - B2_Labour_dataset^[4]	12	5min	500-1000	5	-	Fetal ECG extraction
CinC Challenge 2013^[4]	175	1min	1000	1	-	Fetal ECG extraction
MIMIC^[4]	67830	/	1000	12	-	Respiration extraction

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