基于心电信号堆叠时频尺度图图像的心律失常分类深度学习模型
Deep Learning Models for Arrhythmia Classification Using Stacked Time-frequency Scalogram Images from ECG Signals
AI总结：
针对人工分析大量ECG数据诊断心律失常效率低的问题，提出将12导联ECG信号转换为堆叠时频尺度图图像，输入多种深度学习模型分类，微调CNN准确率达98%。
摘要：
心电图（ECG）是一种记录心脏活动的医学监测技术，被广泛用于心律失常的诊断。该诊断基于对与心脏疾病相关的不规则心率所导致的信号形状形变的分析。由于人工检查大量ECG数据不具备可行性，本文旨在提出一种基于AI的自动化系统，用于基于ECG的心律失常分类。为此，本文提出了一种基于深度学习的ECG心律失常分类解决方案。研究使用了来自PhysioNet的绍兴人民医院（SPH）数据集中45152名个体的时长10秒的12导联心电图，涵盖四种不同类型的心律失常，所采用的采样频率为500Hz。研究使用中值滤波对ECG信号进行预处理。对于每1秒的ECG信号，估计其时频（TF）尺度图，并按行堆叠以从12个通道中得到单张图像，最终每个ECG信号对应10张堆叠的时频尺度图。这些堆叠的时频尺度图被输入预训练卷积神经网络（CNN）、一维CNN以及一维CNN-LSTM（长短期记忆）模型，以进行心律失常分类。微调后的CNN模型取得了约98%的最佳测试准确率，其次是基础CNN-LSTM模型，其心律失常分类测试准确率为95%。
英文摘要：
Electrocardiograms (ECGs), a medical monitoring technology recording cardiac activity, are widely used for diagnosing cardiac arrhythmia. The diagnosis is based on the analysis of the deformation of the signal shapes due to irregular heart rates associated with heart diseases. Due to the infeasibility of manual examination of large volumes of ECG data, this paper aims to propose an automated AI based system for ECG-based arrhythmia classification. To this front, a deep learning based solution has been proposed for ECG-based arrhythmia classification. Twelve lead electrocardiograms (ECG) of length 10 sec from 45, 152 individuals from Shaoxing People's Hospital (SPH) dataset from PhysioNet with four different types of arrhythmias were used. The sampling frequency utilized was 500 Hz. Median filtering was used to preprocess the ECG signals. For every 1 sec of ECG signal, the time-frequency (TF) scalogram was estimated and stacked row wise to obtain a single image from 12 channels, resulting in 10 stacked TF scalograms for each ECG signal. These stacked TF scalograms are fed to the pretrained convolutional neural network (CNN), 1D CNN, and 1D CNN-LSTM (Long short-term memory) models, for arrhythmia classification. The fine-tuned CNN models obtained the best test accuracy of about 98% followed by 95% test accuracy by basic CNN-LSTM in arrhythmia classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
