Author/Authors :
Mena, Luis J Universidad Politecnica de Sinaloa - Mazatlan, Mexico , Félix, Vanessa G Universidad Politecnica de Sinaloa - Mazatlan, Mexico , Ochoa, Alberto Department of Electronic - Faculty of Mechanical and Electrical Engineering - Universidad de Colima - Colima, Mexico , Ostos, Rodolfo Universidad Politecnica de Sinaloa - Mazatlan, Mexico , González, Eduardo Universidad Politecnica de Sinaloa - Mazatlan, Mexico , Aspuru, Javier Department of Electronic - Faculty of Mechanical and Electrical Engineering - Universidad de Colima - Colima, Mexico , Velarde, Pablo Universidad Autonoma de Nayarit - Tepic, Mexico , Maestre, Gladys E Department of Biomedical Sciences - Division of Neurosciences and Department of Human Genetics - University of Texas Rio Grande Valley School of Medicine - Brownsville, USA
Abstract :
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 classifcation 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 specifcity (96.6%). With further verifcation, the system could be useful for
detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and efective treatment of
cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons.
