论文标题
使用卷积神经网络检测心律不齐的心电图心跳分类
Electrocardiogram Heartbeat Classification Using Convolutional Neural Networks for the Detection of Cardiac Arrhythmia
论文作者
论文摘要
心电图（ECG）信号的分类对识别与心脏有关的疾病的影响至关重要。这可以确保过早发现心脏病以及适当选择患者的定制治疗方法。但是，心律不齐的检测是手动执行的具有挑战性的任务。这证明了自动检测异常心脏信号的技术的必要性。因此，我们的工作基于对Physionet的MIT-BIH心律失常数据集的五类心电图心律失常信号的分类。人工神经网络（ANN）在ECG信号分类中表现出了显着成功。我们提出的模型是定制的卷积神经网络（CNN），以对ECG信号进行分类。我们的结果证明，计划中的CNN模型可以成功地将心律不齐分类为95.2％。所提出模型的平均精度和召回分别为95.2％和95.4％。该模型可有效地用于检测早期心律的不规则性。
The classification of the electrocardiogram (ECG) signal has a vital impact on identifying heart-related diseases. This can ensure the premature finding of heart disease and the proper selection of the patient's customized treatment. However, the detection of arrhythmia is a challenging task to perform manually. This justifies the necessity of a technique for automatic detection of abnormal heart signals. Therefore, our work is based on the classification of five classes of ECG arrhythmic signals from Physionet's MIT-BIH Arrhythmia Dataset. Artificial Neural Networks (ANN) have demonstrated significant success in ECG signal classification. Our proposed model is a Convolutional Neural Network (CNN) customized to categorize the ECG signals. Our result testifies that the planned CNN model can successfully categorize arrhythmia with an overall accuracy of 95.2%. The average precision and recall of the proposed model are 95.2% and 95.4%, respectively. This model can effectively be used to detect irregularities of heart rhythm at an early stage.
