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

9 Dec 2020arXiv:2012.04936archive 2025-07-28

Yadong Zhang, Xin Chen

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.

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Code

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Tasks

Anomaly Detection Atrial Fibrillation Detection Classification ECG Classification Electrocardiography (ECG)General Classification Interpretable Machine Learning Rhythm Time Series Time Series Analysis Time Series Classification Transfer Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Atrial Fibrillation Detection	PhysioNet Challenge 2017	TMF(VGG16-MLP)	F1	0.8843	#1 of 2	Archive leaderboard	report
Atrial Fibrillation Detection	PhysioNet Challenge 2017	TMF(VGG16-MLP)	PR-AUC	0.9584	#1 of 2	Archive leaderboard	report
Atrial Fibrillation Detection	PhysioNet Challenge 2017	TMF(VGG16-MLP)	ROC-AUC	0.9550	#1 of 2	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Convolution Interpretability

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