论文标题
基于CNN-BilstM的心房颤动检测和ECG分类
Atrial Fibrillation Detection and ECG Classification based on CNN-BiLSTM
论文作者
论文摘要
从心电图（ECG）信号中检测到心脏病是一项挑战。实施自动的心电图信号检测系统可以帮助诊断心律不齐，以提高诊断的准确性。在本文中，我们提出，实施和比较了一个自动化系统，使用卷积神经网络（CNN）和长期术语记忆（LSTM）组合的两个不同框架，以对正常的窦信号，心房颤动和其他噪声信号进行分类。我们使用的数据集来自MIT - 位心律失常生理学。我们的方法表明，两个深度学习网络的级联对它们的串联的性能高，而加权F1得分为0.82。实验结果已成功验证了CNN和LSTM的级联对于区分ECG信号的性能令人满意。
It is challenging to visually detect heart disease from the electrocardiographic (ECG) signals. Implementing an automated ECG signal detection system can help diagnosis arrhythmia in order to improve the accuracy of diagnosis. In this paper, we proposed, implemented, and compared an automated system using two different frameworks of the combination of convolutional neural network (CNN) and long-short term memory (LSTM) for classifying normal sinus signals, atrial fibrillation, and other noisy signals. The dataset we used is from the MIT-BIT Arrhythmia Physionet. Our approach demonstrated that the cascade of two deep learning network has higher performance than the concatenation of them, achieving a weighted f1 score of 0.82. The experimental results have successfully validated that the cascade of CNN and LSTM can achieve satisfactory performance on discriminating ECG signals.
