A wearable ECG monitor with a machine learning smartphone application discriminated normal and abnormal ECG signals in older adults with 97% accuracy, 100% sensitivity, and 96.6% specificity.
Cross-Sectional (n=100)
Does a wearable ECG monitor with a machine learning-based smartphone application accurately classify normal and abnormal ECG signals in older adults?
A novel wearable ECG monitor paired with a machine learning smartphone application demonstrated high diagnostic accuracy for detecting abnormal ECG signals in older adults.
Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). With further verification, the system could be useful for detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and effective treatment of cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons.
Mena et al. (Tue,) conducted a cross-sectional in Cardiac abnormalities (n=100). Wearable ECG monitor with machine learning smartphone application was evaluated on Discrimination of normal and abnormal ECG signals. A wearable ECG monitor with a machine learning smartphone application discriminated normal and abnormal ECG signals in older adults with 97% accuracy, 100% sensitivity, and 96.6% specificity.