eprintid: 17232
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/72/32
datestamp: 2023-12-19 03:23:39
lastmod: 2023-12-19 03:23:39
status_changed: 2023-12-19 03:07:42
type: conference_item
metadata_visibility: show
creators_name: Abdullah, T.A.A.
creators_name: Zahid, M.S.B.M.
creators_name: Tang, T.B.
creators_name: Ali, W.
creators_name: Nasser, M.
title: Explainable Deep Learning Model for Cardiac Arrhythmia Classification
ispublished: pub
keywords: Diseases; Lead compounds; Learning systems; Lime; Recurrent neural networks; Seismic waves, Arrhythmia; Arrhythmia classification; Cardiac arrhythmia; CNN-gated recurrent unit; Explanation; Heatmaps; Lead II; Learning models; Neural-networks; One-dimensional, Electrocardiograms
note: cited By 2; Conference of 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:186671
abstract: In this work, we proposed a hybrid deep learning model that (CNN-GRU) combines a One-Dimensional Neural Network (1D CNN) and a Gated Recurrent Unit (GRU) to classify four types of cardiac arrhythmia and applied LIME to provide explanations for its predictions. LIME is a well-known local explanation method that can explain any machine learning model by simulating its behaviours to generate explanations. However, LIME can only explain tabular, text, and image datasets. Therefore, we proposed a visual presentation of LIME on signal dataset by applying a heatmap to highlight important areas on the heartbeat signals. Moreover, we propose an effective method to segment heartbeats from ECG records, ensuring that all key features are extracted correctly, such as QRS Complex, P Wave, and T Wave. The proposed hybrid model was trained using ECG lead II from the MIT-BIH dataset and evaluated based on accuracy, precision, recall, f1 score, and AUC-ROC performance matrix. To highlight the proposed model's validity, we compare it against the standalone CNN and GRU models and prove its superiority in terms of accuracy and ROC. © 2022 IEEE.
date: 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149152929&doi=10.1109%2fICFTSC57269.2022.10039860&partnerID=40&md5=b7858607c7e6532e0dd8b5184cb946a1
id_number: 10.1109/ICFTSC57269.2022.10039860
full_text_status: none
publication: 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022
pagerange: 87-92
refereed: TRUE
isbn: 9798350334548
citation:   Abdullah, T.A.A. and Zahid, M.S.B.M. and Tang, T.B. and Ali, W. and Nasser, M.  (2022) Explainable Deep Learning Model for Cardiac Arrhythmia Classification.  In: UNSPECIFIED.