  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.

Machine Learning;Signal Processing