Classification of Premature Ventricular Contraction (PVC) based on ECG Signal using Convolutional Neural Network

Jondri Jondri, Achmad Rizal

Abstract


This study observes one of the ECG signal abnormalities, which is the Premature Ventricular Contraction (PVC). Many studies applied a machine learning technique to develop a computer-aided diagnosis to classify normal and PVC conditions of ECG signals. The common process to obtain information from the ECG signal is by performing a feature extraction process. Since the ECG signal is a complex signal, there is a need to reduce the signal dimension to produce an optimal feature set. However, these processes can remove the information contained in the signal. Therefore, this study process the original ECG signal using a Convolutional Neural Network to avoid losing information. The input data were in the form of both one beat of normal ECG signal or PVC with size 1x200. The classification used four layers of convolutional neural network (CNN). There were eight 1x1 filters used in the input. Simultaneously, 16 and 32 of 1x1 filters were used in the second and the fourth convolutional layers, respectively. Thus the system produced a fully connected layer consisted of 512 neurons, while the output layer consisted of 2 neurons. The system is tested using 11361 beats of ECG data and achieved the highest accuracy of 99.59%, with the 10-fold cross-validation. This study emphasizes an opportunity to develop a wearable device to detect PVC since CNN can be implemented into an embedded system or an IoT based system.

Keywords


Electrocardiogram; PVC; CNN; Deep learning; Signal processing

Full Text: PDF

Refbacks

  • There are currently no refbacks.


 

Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

Creative Commons Licence

This work is licensed under a Creative Commons Attribution 4.0 International License.

web analytics
View IJEEI Stats

https://jlt.ac/https://jgaa.info/public/www/idn/https://jgaa.info/public/www/mpo/https://mitrasmart.co.id/akun-pro-thailand/http://103.165.243.97/doc/unsign/psrtgl2/https://lnx.gatm.it/analiticaojs/https://viguera.com/slot-thailand/https://www.cienciaecuador.com.ec/https://ejournal.aibpmjournals.com/scatter-hitam/https://pijarpemikiran.com/https://hr.tarunabakti.or.id/zeus-slot/https://www.vertitech.gr/wp-content/situs/https://ube.edu.ec/depo10k/https://ejournal.aibpmjournals.com/gates-of-olympus/https://fjot.anfe.fr/https://www.viguera.com/slot-gacor/http://revista.tce.gob.ec/ojs-3.1.2-4/sweet-bonanza/http://citaitb.com/wp-content/document/https://rdsp.msp.gob.do/sgm/https://www.remap.ugto.mx/https://thepab.org/public/pro/https://ojs.co.id/wp-content/cache/https://ktadigitalpgri.org/assets/dist/img/scatter-hitam/http://controlvisible.auditoria.gov.co/public/https://isbrmj.org/starlight-princess/https://fjot.anfe.fr/https://journalofhealthandcaringsciences.org/atm88/https://journalofhealthandcaringsciences.org/idn/https://masonhq.org/http://www.inmedsur.cfg.sld.cu//https://iojpe.org/jepang/https://ojs.ukscip.com/pages/2024/https://www.journalprenatalife.com/public/http://citaitb.com/idn/https://journalofhealthandcaringsciences.org/mpo/https://asianmedjam.com/slot-deposit-pulsa/https://asianmedjam.com/akun-pro-kamboja/https://isbrmj.org/public/https://caet.inspirees.com/slot-luar/https://isnujatim.org/slot-dana/https://journal.shamlands.sy/pages/io/https://www.viguera.com/slot-kamboja/https://kpmsurabaya.id/akun-pro-kamboja/https://www.remap.ugto.mx/pages/slot-luar-negeri-winrate-tertinggi/http://www.inmedsur.cfg.sld.cu/docs/https://algede.org/