Anomaly Detection in Time Series with Triadic Motif Fields and Application in At

Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification

In the time-series analysis, the time series motifs and the order patterns in time series can reveal general temporal patterns and dynamic features. Triadic Motif Field (TMF) is a simple and effective time-series image encoding method based on triadic time series motifs.Electrocardiography (ECG) signals are time-series data widely used to diagnose various cardiac anomalies. The TMF images contain the features characterizing the normal and Atrial Fibrillation (AF) ECG signals. Considering the quasi-periodic characteristics of ECG signals, the dynamic features can be extracted from the TMF images with the transfer learning pre-trained convolutional neural network (CNN) models. With the extracted features, the simple classifiers, such as the Multi-Layer Perceptron (MLP), the logistic regression, and the random forest, can be applied for accurate anomaly detection. With the test dataset of the PhysioNet Challenge 2017 database, the TMF classification model with the VGG16 transfer learning model and MLP classifier demonstrates the best performance with the 95.50% ROC-AUC and 88.43% F1 score in the AF classification. Besides, the TMF classification model can identify AF patients in the test dataset with high precision. The feature vectors extracted from the TMF images show clear patient-wise clustering with the t-distributed Stochastic Neighbor Embedding technique. Above all, the TMF classification model has very good clinical interpretability. The patterns revealed by symmetrized Gradient-weighted Class Activation Mapping have a clear clinical interpretation at the beat and rhythm levels.

三重基序场的时间序列异常检测及其在房颤心电图分类中的应用

在时间序列分析中，时间序列的主题和时间序列中的顺序模式可以揭示一般的时间模式和动态特征。Triadic Motif Field（TMF）是一种基于三重时间序列主题的简单有效的时间序列图像编码方法。.. 心电图（ECG）信号是广泛用于诊断各种心脏异常的时间序列数据。TMF图像包含表征正常和房颤（AF）ECG信号的特征。考虑到ECG信号的准周期性特征，可以使用传递学习预训练卷积神经网络（CNN）模型从TMF图像中提取动态特征。利用提取的功能，可以将简单的分类器（例如多层感知器（MLP），逻辑回归和随机森林）应用于准确的异常检测。借助PhysioNet Challenge 2017数据库的测试数据集，带有VGG16转移学习模型和MLP分类器的TMF分类模型在AF分类中以95.50％的ROC-AUC和88.43％的F1分数展示了最佳性能。此外，TMF分类模型可以在测试数据集中高精度地识别房颤患者。从TMF图像中提取的特征向量通过t分布随机邻居嵌入技术显示出清晰的按患者分类。最重要的是，TMF分类模型具有很好的临床解释性。对称的梯度加权类激活映射所揭示的模式在节律和节奏水平上具有清晰的临床解释。 （阅读更多）