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

12/09/2020
by   Yadong Zhang, et al.
0

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 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.

READ FULL TEXT
research
01/21/2020

Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification

Time series motifs play an important role in the time series analysis. T...
research
06/02/2023

A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy

Background: Cardiac resynchronization therapy (CRT) has emerged as an ef...
research
03/17/2020

Construe: a software solution for the explanation-based interpretation of time series

This paper presents a software implementation of a general framework for...
research
10/07/2021

Generative Pre-Trained Transformer for Cardiac Abnormality Detection

ECG heartbeat classification plays a vital role in diagnosis of cardiac ...
research
07/13/2016

Feature Extraction and Automated Classification of Heartbeats by Machine Learning

We present algorithms for the detection of a class of heart arrhythmias ...

Please sign up or login with your details

Forgot password? Click here to reset