论文标题
ECG使用机器学习和预训练的卷积神经网络进行分类
ECG beat classification using machine learning and pre-trained convolutional neural networks
论文作者
论文摘要
心电图（ECG）通常在医院中用于分析一个人的心血管状况和健康。心律异常可能是更严重的疾病，包括心脏突然死亡的前体。对异常节奏进行分类是一个容易出错的过程。因此，高精度执行自动分类的工具非常需要。介绍的工作根据AAMI EC57标准并使用MIT-BIH数据集对五种不同类型的心电图心律失常进行了分类。这些包括非分解（正常），上，室，融合和未知的节拍。通过适当地将预处理的ECG波形转换为丰富的特征空间，并在适当的后处理以及使用深度卷积神经网络进行微调和超参数选择后，可以获得可以获得五种波形类型的高度准确分类。测试集的性能表明总体准确性更高（98.62％），并且在对五个波形中的每个波形分类中的性能都比文献中报道的更好。
The electrocardiogram (ECG) is routinely used in hospitals to analyze cardiovascular status and health of an individual. Abnormal heart rhythms can be a precursor to more serious conditions including sudden cardiac death. Classifying abnormal rhythms is a laborious process prone to error. Therefore, tools that perform automated classification with high accuracy are highly desirable. The work presented classifies five different types of ECG arrhythmia based on AAMI EC57 standard and using the MIT-BIH data set. These include non-ectopic (normal), supraventricular, ventricular, fusion, and unknown beat. By appropriately transforming pre-processed ECG waveforms into a rich feature space along with appropriate post-processing and utilizing deep convolutional neural networks post fine-tuning and hyperparameter selection, it is shown that highly accurate classification for the five waveform types can be obtained. Performance on the test set indicated higher overall accuracy (98.62%), as well as better performance in classifying each of the five waveforms than hitherto reported in literature.
