TY - GEN
T1 - Smart ECG Holter monitoring system using smartphone
AU - Mahdy, Lamia Nabil
AU - Ezzat, Kadry Ali
AU - Tan, Qing
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - The Prevention of cardiovascular disease requires continuous monitoring of cross-clock ECG signals along with the activity status. Traditional ECG Holter has numerous electrodes connected to the chest, which is heavy, so it is very difficult to carry by the patient, so ECG monitoring usually requires the patient to stay in the hospital for a long time. This paper presented a small ECG Holter device that was developed to detect arrhythmias in real-time based on Android mobile application. The ECG signals are obtained directly through ECG's three-electrode sensor then transmitted through a Bluetooth module to Android smartphone. Prepossessing ECG signal algorithm is implemented on Arduino Device. Android mobile application analysis and classify patient's ECG data to detect abnormal signs. Data used in testing and training was 303 cases acquired from El-Monofia University, 162 cases were normal, and 141 cases were abnormally divided into 57 cases were Coronary Artery Disease, 36 cases were Old Anterior Myocardial Infarction, and 48 cases were Sinus tachycardia. The experimental results show that the presented system's performance has been improved in the accuracy of diagnosis of arrhythmias and the identification of the most widely recognized anomalies in various activities.
AB - The Prevention of cardiovascular disease requires continuous monitoring of cross-clock ECG signals along with the activity status. Traditional ECG Holter has numerous electrodes connected to the chest, which is heavy, so it is very difficult to carry by the patient, so ECG monitoring usually requires the patient to stay in the hospital for a long time. This paper presented a small ECG Holter device that was developed to detect arrhythmias in real-time based on Android mobile application. The ECG signals are obtained directly through ECG's three-electrode sensor then transmitted through a Bluetooth module to Android smartphone. Prepossessing ECG signal algorithm is implemented on Arduino Device. Android mobile application analysis and classify patient's ECG data to detect abnormal signs. Data used in testing and training was 303 cases acquired from El-Monofia University, 162 cases were normal, and 141 cases were abnormally divided into 57 cases were Coronary Artery Disease, 36 cases were Old Anterior Myocardial Infarction, and 48 cases were Sinus tachycardia. The experimental results show that the presented system's performance has been improved in the accuracy of diagnosis of arrhythmias and the identification of the most widely recognized anomalies in various activities.
KW - ECG Signal Monitoring
KW - K-Nearest Neighbors Classifier
KW - QRS Complex
UR - http://www.scopus.com/inward/record.url?scp=85061757532&partnerID=8YFLogxK
U2 - 10.1109/IOTAIS.2018.8600891
DO - 10.1109/IOTAIS.2018.8600891
M3 - Published Conference contribution
AN - SCOPUS:85061757532
T3 - Proceedings - 2018 IEEE International Conference on Internet of Things and Intelligence System, IOTAIS 2018
SP - 80
EP - 84
BT - Proceedings - 2018 IEEE International Conference on Internet of Things and Intelligence System, IOTAIS 2018
T2 - 2018 IEEE International Conference on Internet of Things and Intelligence System, IOTAIS 2018
Y2 - 1 November 2018 through 3 November 2018
ER -