Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly
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...
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2018-01-01
|
Series: | Computational and Mathematical Methods in Medicine |
Online Access: | http://dx.doi.org/10.1155/2018/9128054 |
id |
doaj-6ea452d57bab43948aafdef88de34df1 |
---|---|
record_format |
Article |
spelling |
doaj-6ea452d57bab43948aafdef88de34df12020-11-24T22:14:24ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182018-01-01201810.1155/2018/91280549128054Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in ElderlyLuis J. Mena0Vanessa G. Félix1Alberto Ochoa2Rodolfo Ostos3Eduardo González4Javier Aspuru5Pablo Velarde6Gladys E. Maestre7Academic Unit of Computing, Master Program in Applied Sciences, Universidad Politecnica de Sinaloa, Mazatlan 82199, MexicoAcademic Unit of Computing, Master Program in Applied Sciences, Universidad Politecnica de Sinaloa, Mazatlan 82199, MexicoDepartment of Electronic, Faculty of Mechanical and Electrical Engineering, Universidad de Colima, Colima 28400, MexicoAcademic Unit of Computing, Master Program in Applied Sciences, Universidad Politecnica de Sinaloa, Mazatlan 82199, MexicoAcademic Unit of Computing, Master Program in Applied Sciences, Universidad Politecnica de Sinaloa, Mazatlan 82199, MexicoDepartment of Electronic, Faculty of Mechanical and Electrical Engineering, Universidad de Colima, Colima 28400, MexicoAcademic Program of Electronic Engineering, Universidad Autonoma de Nayarit, Tepic 63000, MexicoDepartment of Biomedical Sciences, Division of Neurosciences and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville 78520, USAMobile 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.http://dx.doi.org/10.1155/2018/9128054 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Luis J. Mena Vanessa G. Félix Alberto Ochoa Rodolfo Ostos Eduardo González Javier Aspuru Pablo Velarde Gladys E. Maestre |
spellingShingle |
Luis J. Mena Vanessa G. Félix Alberto Ochoa Rodolfo Ostos Eduardo González Javier Aspuru Pablo Velarde Gladys E. Maestre Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly Computational and Mathematical Methods in Medicine |
author_facet |
Luis J. Mena Vanessa G. Félix Alberto Ochoa Rodolfo Ostos Eduardo González Javier Aspuru Pablo Velarde Gladys E. Maestre |
author_sort |
Luis J. Mena |
title |
Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly |
title_short |
Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly |
title_full |
Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly |
title_fullStr |
Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly |
title_full_unstemmed |
Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly |
title_sort |
mobile personal health monitoring for automated classification of electrocardiogram signals in elderly |
publisher |
Hindawi Limited |
series |
Computational and Mathematical Methods in Medicine |
issn |
1748-670X 1748-6718 |
publishDate |
2018-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2018/9128054 |
work_keys_str_mv |
AT luisjmena mobilepersonalhealthmonitoringforautomatedclassificationofelectrocardiogramsignalsinelderly AT vanessagfelix mobilepersonalhealthmonitoringforautomatedclassificationofelectrocardiogramsignalsinelderly AT albertoochoa mobilepersonalhealthmonitoringforautomatedclassificationofelectrocardiogramsignalsinelderly AT rodolfoostos mobilepersonalhealthmonitoringforautomatedclassificationofelectrocardiogramsignalsinelderly AT eduardogonzalez mobilepersonalhealthmonitoringforautomatedclassificationofelectrocardiogramsignalsinelderly AT javieraspuru mobilepersonalhealthmonitoringforautomatedclassificationofelectrocardiogramsignalsinelderly AT pablovelarde mobilepersonalhealthmonitoringforautomatedclassificationofelectrocardiogramsignalsinelderly AT gladysemaestre mobilepersonalhealthmonitoringforautomatedclassificationofelectrocardiogramsignalsinelderly |
_version_ |
1725798994399985664 |