A Convolutional Autoencoder framework demonstrates high accuracy in detecting anomalies in 12-lead ECG signals, offering potential decision support for physicians.
Electrocardiographic (ECG) signals are used to evaluate heart activity and to identify disease-related anomalies. Reliable support systems are useful for analyzing ECG signals, for instance, in long-term data acquisition and evaluation (e. g. , 24-hour holter recording) or to support physicians in reading ECGs. Analysis of time varying signals may be done by using autoencoders (AEs) deep neural networks. AE specialized for signal data, named Convolutional Autoencoder (CAE), showed the best performances in the analysis of ECG signals. This paper presents a CAE-based framework for ECG signal analysis and anomaly identification. The trained phase is performed on synthetic data signals. The trained neural network obtained is used for the detection of anomalies in ECG signals. The trained framework has been tested on 12 lead ECG signals on a benchmark dataset and applied in scenarios where anomalies are related to cardiological risks and pathologies. The results show interesting results in automatic anomaly detection to support physicians in the decision process. The results show that the CAE-based framework is able to identify anomalies in ECG signals with a ROC AUC of 97. 82% on simulated test set and a ROC AUC of 99. 75% using a real test set. Finally, the proposed method has been enriched by means of reconstruction error based explainability modules and time-windows based preprocessing modules. Explainability results have been validated using abnormalities annotated by a cardiologist as ground truth and compared with explainations results. System with both code and data, is available at https: //github. com/UgoLomoio/ECGDSSCAE.
Lomoio et al. (Wed,) studied this question.