eprintid: 11821
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/18/21
datestamp: 2023-11-10 03:26:21
lastmod: 2023-11-10 03:26:21
status_changed: 2023-11-10 01:16:13
type: conference_item
metadata_visibility: show
creators_name: Qayyum, A.
creators_name: Meriaudeau, F.
creators_name: Chan, G.C.Y.
title: Classification of atrial fibrillation with pretrained convolutional neural network models
ispublished: pub
keywords: Biomedical engineering; Deep learning; Diseases; Electrocardiography; Extraction; Heuristic methods; Neural networks; Support vector machines, AF detction; Classification algorithm; CNN models; Convolutional neural network; Ensemble classifiers; Sensitivity and specificity; Short time Fourier transforms; State-of-the-art techniques, Feature extraction
note: cited By 22; Conference of 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 ; Conference Date: 3 December 2018 Through 6 December 2018; Conference Code:144644
abstract: Atrial Fibrillation (AF) is the most common chronic arrhythmia. Effective detection of the AF would avoid serious consequences like stroke. Conventional AF detection methods need heuristic or hand-crafted features extraction. Recently, deep learning (DL) techniques with massive data have been used on image, voice and other field widely with impressive results. The ECG rhythms such as AF, normal, Noisy and other rhythms have been segmented into 3 segments per rhythm, converted into 2D images using short time Fourier transform (STFT) and fed into pretrained models. The pre-trained CNN models are used for transfer learning or are fine tuned for the detection and classification of the AF rhythm. The features extracted from the last layer of the pre-trained models are used as input to classical classification algorithms such as Ensemble classifier and support vector machine (SVM) for AF detection. The proposed approach would be have great potential on real-time monitoring of atrial fibrillation signal in electrocardiogram. Overall, our approaches achieved accuracy, sensitivity and specificity as of 97.89, 97.12 and 96.99 similar to the latest state of the art techniques but with more flexibility. © 2018 IEEE
date: 2019
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062798021&doi=10.1109%2fIECBES.2018.8626624&partnerID=40&md5=2690246934319c896f1a609ca447f2bc
id_number: 10.1109/IECBES.2018.8626624
full_text_status: none
publication: 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings
pagerange: 594-599
refereed: TRUE
isbn: 9781538624715
citation:   Qayyum, A. and Meriaudeau, F. and Chan, G.C.Y.  (2019) Classification of atrial fibrillation with pretrained convolutional neural network models.  In: UNSPECIFIED.