Identification of R-peak occurrences in compressed ECG signals
- 1 June 2020
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)
Abstract
Heart Rate (HR) is one of the mostly used electrocardiogram (ECG) feature in many automatic detectors of anomalies. This paper deals with a preliminary study on a novel approach which, through the combination of Machine Learning (ML) and Compressed Sensing (CS), aims at retrieving vital information from a digital compressed single-lead electrocardiogram (ECG) signal. As a potential key information to estimate the heart rate, this study focuses on the identification of R-peak occurrences. The study has been conducted on two different types of signal both obtained from the compressed samples provided by a CS algorithm, already available in literature. The results demonstrate that the use of CS in combination with a ML technique can find high competitiveness when compared to a state of the art method working on the uncompressed ECG signal.Keywords
This publication has 13 references indexed in Scilit:
- A Novel Method for Compressed Sensing based Sampling of ECG Signals in Medical-IoT eraPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2019
- Matched Filtering for Heart Rate Estimation on Compressive Sensing ECG MeasurementsIEEE Transactions on Biomedical Engineering, 2017
- Low-complexity detection of atrial fibrillation in continuous long-term monitoringComputers in Biology and Medicine, 2015
- A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart RatePLOS ONE, 2015
- Network design and characterization of a Wireless Active Guardrail SystemPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Compressive Sensing [Lecture Notes]IEEE Signal Processing Magazine, 2007
- Compressed sensingIEEE Transactions on Information Theory, 2006
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency informationIEEE Transactions on Information Theory, 2006
- Random ForestsMachine Learning, 2001
- A Real-Time QRS Detection AlgorithmIEEE Transactions on Biomedical Engineering, 1985