eprintid: 18757
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/87/57
datestamp: 2024-06-04 14:11:09
lastmod: 2024-06-04 14:11:09
status_changed: 2024-06-04 14:04:00
type: article
metadata_visibility: show
creators_name: Mandala, S.
creators_name: Pratiwi Wibowo, A.R.
creators_name: Adiwijaya, 
creators_name: Suyanto, 
creators_name: Zahid, M.S.M.
creators_name: Rizal, A.
title: The Effects of Daubechies Wavelet Basis Function (DWBF) and Decomposition Level on the Performance of Artificial Intelligence-Based Atrial Fibrillation (AF) Detection Based on Electrocardiogram (ECG) Signals
ispublished: pub
note: cited By 9
abstract: Featured Application: A potential application developed from the results obtained by this study is a monitoring system of atrial fibrillation for stroke early warning in both healthy people and heart disease patients. This research studies the effects of both Daubechies wavelet basis function (DWBF) and decomposition level (DL) on the performance of detecting atrial fibrillation (AF) based on electrocardiograms (ECGs). ECG signals (consisting of 23 AF data and 18 normal data from MIT-BIH) were decomposed at various levels using several types of DWBF to obtain four wavelet coefficient features (WCFs), namely, minimum (min), maximum (max), mean, and standard deviation (stdev). These features were then classified to detect the presence of AF using a support vector machine (SVM) classifier. Distribution of training and testing data for the SVM uses the 5-fold cross-validation (CV) principle to produce optimum detection performance. In this study, AF detection performance is measured and analyzed based on accuracy, sensitivity, and specificity metrics. The results of the analysis show that accuracy tends to decrease with increases in the decomposition level. In addition, it becomes stable in various types of DWBF. For both sensitivity and specificity, the results of the analysis show that increasing the decomposition level also causes a decrease in both sensitivity and specificity. However, unlike the accuracy, changing the DWBF type causes both two metrics to fluctuate over a wider range. The statistical results also indicate that the highest AF accuracy detection (i.e., 94.17) is obtained at the Daubechies 2 (DB2) function with a decomposition level of 4, whereas the highest sensitivity, 97.57, occurs when the AF detection uses DB6 with a decomposition level of 2. Finally, DB2 with decomposition level 4 results in 96.750 for specificity. The finding of this study is that selecting the appropriate DL has a more significant effect than DWBF on AF detection using WCF. © 2023 by the authors.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149967566&doi=10.3390%2fapp13053036&partnerID=40&md5=655baa164f887453245ab9f4be230865
id_number: 10.3390/app13053036
full_text_status: none
publication: Applied Sciences (Switzerland)
volume: 13
number: 5
refereed: TRUE
citation:   Mandala, S. and Pratiwi Wibowo, A.R. and Adiwijaya and Suyanto and Zahid, M.S.M. and Rizal, A.  (2023) The Effects of Daubechies Wavelet Basis Function (DWBF) and Decomposition Level on the Performance of Artificial Intelligence-Based Atrial Fibrillation (AF) Detection Based on Electrocardiogram (ECG) Signals.  Applied Sciences (Switzerland), 13 (5).