A dynamic learning-based ECG feature extraction method for myocardial infarction detection

In this study, a dynamic learning algorithm is applied to discover prominent features for identifying MI patients via mining the hidden inherent dynamics in ECG signals. Firstly, the distinctive dynamic features extracted from the multi-scale decomposition of dynamic modeling of the ECG signals effectively and comprehensibly represent the pathological ECG changes. Secondly, a few most important dynamic features are filtered through a hybrid feature selection algorithm based on filter and wrapper to form a representative reduced feature set. Finally, different classifiers based on the reduced feature set are trained and tested on the public PTB dataset and an independent clinical data set. Main results. Our proposed method achieves a significant improvement in detecting MI patients under the inter-patient paradigm, with an accuracy of 94.75%, sensitivity of 94.18%, and specificity of 96.33% on the PTB dataset. Furthermore, classifiers trained on PTB are verified on the test data set collected from 200 patients, yielding a maximum accuracy of 84.96%, sensitivity of 85.04%, and specificity of 84.80%. Significance. The experimental results demonstrate that our method performs distinctive dynamic feature extraction and may be used as an effective auxiliary tool to diagnose MI patients.
Source: Physiological Measurement - Category: Physiology Authors: Source Type: research