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 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.
Highlights
Cardiovascular diseases (CVD) have remained the leading cause of death globally during the last 15 years
Technological innovations, including mobile and wireless technologies, are being developed to improve prevention and control of CVD, and other aspects of healthcare, for older people residing in low and middle-income countries (LMIC) [7,8,9]
We developed a complete mobile personal health monitor (PHM) system, integrated with a self-designed wireless sensor for ECG signal acquisition, and a native purposely designed smartphone application to be user-friendly to elderly, based on machine learning techniques, for automated classification of captured ECG beats
Summary
Cardiovascular diseases (CVD) have remained the leading cause of death globally during the last 15 years. An estimated 17.7 million people died from CVD in 2015, representing 31% of all global mortality. Of these deaths, approximately 6.9 million were in people aged 60 years and older, and over 75% occurred in low and middle-income countries (LMIC) [1, 2]. LMIC are more greatly affected than high-income countries [3,4,5], largely because people with low socioeconomic status have poor access to healthcare for early diagnosis and treatment of CVD [5]. Technological innovations, including mobile and wireless technologies, are being developed to improve prevention and control of CVD, and other aspects of healthcare, for older people residing in LMIC [7,8,9]
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