Samenvatting
Nowadays, even with all the tremendous advances in medicine and health protocols, cardiovascular diseases (CVD) continue to be one of the major causes of death. In the present work, we focus on a specific abnormality: ST-segment deviation, which occurs regularly in highperformance athletes and elderly people, serving as a myocardial infarction (MI) indicator. It is usually diagnosed manually by experts, through visual interpretation of the printed electrocardiography (ECG) signal. We propose a methodology to detect ST-segment deviation and quantify its scale up to 1 mV by extracting statistical, point-to-point beat characteristics and signal quality indexes (SQIs) from single-lead ECG.We do so by applying automated machine learning methods to find the best hyperparameter configuration for classification and regression models. For validation of our method, we use the ST-T database from Physionet; the results show that our method obtains 98.30% accuracy in the case of a multiclass problem and 99.87% accuracy in the case of binarization.
Originele taal-2 | Engels |
---|---|
Artikelnummer | 4919 |
Aantal pagina's | 24 |
Tijdschrift | Sensors |
Volume | 22 |
Nummer van het tijdschrift | 13 |
DOI's | |
Status | Gepubliceerd - 1 jul. 2022 |
Bibliografische nota
Publisher Copyright:© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Financiering
Funding: We are grateful for the support and funding of this work to ITEA INNO4HEALTH 19008 Project. This work made use of the Dutch national e-infrastructure with the support of the SURF Cooperative using grant no. EINF-2491. This research was partially supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.