TY - GEN
T1 - ECG signal processing for early detection of atrial and ventricular fibrillation based on R-R interval
AU - Siwindarto, Ponco
AU - DIanisma, Ana Bella
AU - Abidin, Zainul
AU - Mahmadov, Sharif Safaralievich
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8/26
Y1 - 2020/8/26
N2 - Atrial and ventricular fibrillation are considered as frequent cardiac arrhythmias. Therefore, early detection of both fibrillation is important to support medical treatment. In this paper, design of detector circuit for atrial and ventricular fibrillation using R-R interval of electrocardiogram (ECG) signals is reported and considered as a solution for early detection. The detector circuit was designed using easy buy components. The ECG signals were classified as Normal Sinus Rhythm (NSR) when the R-R interval is detected regularly, Atrial Fibrillation (AF) when the R-R interval is detected irregularly, and Ventricular Fibrillation (VF) when the R-R interval is not detected. The system was directly tested using three human bodies for NSR category. Due to some limitations, three recorded ECG signals for each AF and VF were taken from PhysioNet database as samples. The system obtained square waves with amplitude of 5 V where every rising edges indicate R peaks. Experiment results show that the system can classify correctly.
AB - Atrial and ventricular fibrillation are considered as frequent cardiac arrhythmias. Therefore, early detection of both fibrillation is important to support medical treatment. In this paper, design of detector circuit for atrial and ventricular fibrillation using R-R interval of electrocardiogram (ECG) signals is reported and considered as a solution for early detection. The detector circuit was designed using easy buy components. The ECG signals were classified as Normal Sinus Rhythm (NSR) when the R-R interval is detected regularly, Atrial Fibrillation (AF) when the R-R interval is detected irregularly, and Ventricular Fibrillation (VF) when the R-R interval is not detected. The system was directly tested using three human bodies for NSR category. Due to some limitations, three recorded ECG signals for each AF and VF were taken from PhysioNet database as samples. The system obtained square waves with amplitude of 5 V where every rising edges indicate R peaks. Experiment results show that the system can classify correctly.
KW - Early detection
KW - Electrocardiogram
KW - Fibrillation
KW - R-R interval
UR - https://www.scopus.com/pages/publications/85099531160
U2 - 10.1109/EECCIS49483.2020.9263454
DO - 10.1109/EECCIS49483.2020.9263454
M3 - Conference contribution
AN - SCOPUS:85099531160
T3 - EECCIS 2020 - 2020 10th Electrical Power, Electronics, Communications, Controls, and Informatics Seminar
SP - 142
EP - 146
BT - EECCIS 2020 - 2020 10th Electrical Power, Electronics, Communications, Controls, and Informatics Seminar
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th Electrical Power, Electronics, Communications, Controls, and Informatics Seminar, EECCIS 2020
Y2 - 26 August 2020 through 28 August 2020
ER -