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In the time-series analysis, the time series motifs and the order patterns intime series can reveal general temporal patterns and dynamic features. TriadicMotif Field (TMF) is a simple and effective time-series image encoding methodbased on triadic time series motifs. Electrocardiography (ECG) signals aretime-series data widely used to diagnose various cardiac anomalies. The TMFimages contain the features characterizing the normal and Atrial Fibrillation(AF) ECG signals. Considering the quasi-periodic characteristics of ECGsignals, the dynamic features can be extracted from the TMF images with thetransfer learning pre-trained convolutional neural network (CNN) models. Withthe extracted features, the simple classifiers, such as the Multi-LayerPerceptron (MLP), the logistic regression, and the random forest, can beapplied for accurate anomaly detection. With the test dataset of the PhysioNetChallenge 2017 database, the TMF classification model with the VGG16 transferlearning model and MLP classifier demonstrates the best performance with the95.50% ROC-AUC and 88.43% F1 score in the AF classification. Besides, the TMFclassification model can identify AF patients in the test dataset with highprecision. The feature vectors extracted from the TMF images show clearpatient-wise clustering with the t-distributed Stochastic Neighbor Embeddingtechnique. Above all, the TMF classification model has very good clinicalinterpretability. The patterns revealed by symmetrized Gradient-weighted ClassActivation Mapping have a clear clinical interpretation at the beat and rhythmlevels.

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