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primary topic: Explainable Deep Learning Model for Cardiac Arrhythmia Classification

Explainable Deep Learning Model for Cardiac Arrhythmia Classification
type: AcademicArticle
type: Article
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
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

Institute of Electrical and Electronics Engineers Inc.
type: Organization

2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022
type: Collection

T.B. Tang
type: Person
given name: T.B.
family name: Tang

M. Nasser
type: Person
given name: M.
family name: Nasser

M.S.B.M. Zahid
type: Person
given name: M.S.B.M.
family name: Zahid

T.A.A. Abdullah
type: Person
given name: T.A.A.
family name: Abdullah

W. Ali
type: Person
given name: W.
family name: Ali

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primary topic: Explainable Deep Learning Model for Cardiac Arrhythmia Classification