A deep convolutional neural network model to classify heartbeats
A deep convolutional neural network model to classify heartbeats
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DOI：
10.1016/j.compbiomed.2017.08.022
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发表时间：
2017-10-01
影响因子：
7.7
通讯作者：
Tan, Ru San
中科院分区：
文献类型：
--
作者：
Acharya, U. Rajendra;Oh, Shu Lih;Tan, Ru San
心电图（ECG）是用于监测心脏活动的标准测试。许多心脏异常都会在心电图中表现出来，包括心律失常，心律失常是指心律异常的总称。心律失常诊断的基础是识别正常与异常的个体心跳，并基于ECG形态将其正确分类为不同的诊断。心跳可细分为五类，即非异位、室上性异位、室性异位、融合和未知心跳。区分ECG上的这些心跳是具有挑战性和耗时的，因为这些信号通常被噪声破坏。我们开发了一个9层深度卷积神经网络（CNN）来自动识别ECG信号中的5种不同类别的心跳。我们的实验是在原始和噪声衰减的ECG信号集来自一个公开的数据库。这个集合被人为地增强，以均匀5类心跳的实例数量，并过滤以去除高频噪声。CNN使用增强数据进行训练，在原始和无噪声ECG中的心跳诊断分类中分别达到94.03%和93.47%的准确率。当CNN用高度不平衡的数据（原始数据集）训练时，CNN的准确度在有噪声和无噪声ECG中分别降低到89.07%和89.3%。当经过适当的训练时，所提出的CNN模型可以作为ECG筛选的工具，以快速识别不同类型和频率的心跳。
The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be subdivided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats.