A Wearable Device for Real-Time ECG Monitoring and Cardiovascular Arrhythmia Detection for Resource Constrained Regions

Electrocardiography is a non-invasive technique of recording the electrical activity of heart by placing electrodes around the heart. The electrical signals are then used to detect various cardiovascular abnormalities. In this work, we propose a wearable device that runs an intuitive algorithm processing the data from a single lead ECG front end and reports various arrhythmias. The algorithm uses Pan Tompkins Algorithm as the basic R-R detection scheme and extends to further detection of various arrhythmias. To validate the effectiveness of the proposed algorithm, the MIT/BIH arrhythmia database is used as a source for the ECG signals and the reference for R-R peak annotations. The algorithm provides the metric of False Detection Rate to be 1.289%, Sensitivity to be 99.492% and Positive Predictivity to be 99.293%. The proposed wearable device design is affordable due to the low cost off the shelf components and can be an efficient tool within the resource constraint regions in Asia and Africa where health care is a major concern and remote monitoring can be a useful alternative.