DICTIONARY LEARNING FOR THE SPARSE MODELLING OF ATRIAL FIBRILLATION IN ECG SIGNALS

被引：12

作者：

Mailhe, B. ^{[1
]}

Gribonval, R. ^{[1
]}

Bimbot, F. ^{[1
]}

Lemay, M. ^{[2
]}

Vandergheynst, P. ^{[2
]}

Vesin, J. -M. ^{[2
]}

机构：

[1] IRISA, INRIA, Projet METISS, Campus Beaulieu, F-35042 Rennes, France

[2] Ecole Polytech Fed Lausanne, Signal Proc Lab Sch Engn, CH-1015 Lausanne, Switzerland

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS | 2009年

关键词：

ECG; atrial fibrillation; monochannel source separation; dictionary learning; sparse approximation; K-SVD;

D O I：

10.1109/ICASSP.2009.4959621

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

We propose a new method for ventricular cancellation and atrial modelling in the ECG of patients suffering from atrial fibrillation. Our method is based on dictionary learning. It extends both the average beat subtraction and the sparse source separation approaches. Experiments on synthetic data show that this method can almost completely suppress the ventricular activity, but it generates some artifacts. Contrary to other ventricular cancellations methods, our approach also learns a model for the atrial activity.

引用

页码：465 / +

页数：2

共 50 条

[1] Automated Classification of Atrial Fibrillation and Atrial Flutter in ECG Signals based on Deep Learning
Zhang, Huimin
Qiu, Lishen
Zhu, Wenliang
Cai, Wenqiang
Wang, Lirong
2021 IEEE 20TH INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (TRUSTCOM 2021), 2021, : 1498 - 1502
[2] The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals
Cervigon, Raquel
McGinley, Brian
Craven, Darren
Glavin, Martin
Jones, Edward
APPLIED SCIENCES-BASEL, 2021, 11 (13):
[3] Early Detection of Atrial Fibrillation Based on ECG Signals
Ahmed, Nuzhat
Zhu, Yong
BIOENGINEERING-BASEL, 2020, 7 (01):
[4] Reliable Atrial Activity Extraction from ECG Atrial Fibrillation Signals
Donoso, Felipe
Lecannelier, Eduardo
Pino, Esteban
Rojas, Alejandro
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, 2011, 7042 : 621 - +
[5] Greedy double sparse dictionary learning for sparse representation of speech signals
Abrol, V.
Sharma, P.
Sao, A. K.
SPEECH COMMUNICATION, 2016, 85 : 71 - 82
[6] Automatic atrial fibrillation detection from short ECG signals: A hybrid deep learning approach
Wu, Xiaodan
Sui, Zeyu
Chu, Chao-Hsien
Huang, Guanjie
IISE TRANSACTIONS ON HEALTHCARE SYSTEMS ENGINEERING, 2022, 12 (01) : 1 - 19
[7] Atrial Fibrillation Onset Prediction Using Variability of ECG Signals
Costin, Hariton
Rotariu, Cristian
Pasarica, Alexandru
2013 8TH INTERNATIONAL SYMPOSIUM ON ADVANCED TOPICS IN ELECTRICAL ENGINEERING (ATEE), 2013,
[8] Fast Dictionary Learning for Sparse Representations of Speech Signals
Jafari, Maria G.
Plumbley, Mark D.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2011, 5 (05) : 1025 - 1031
[9] Deep Learning of ECG for the Prediction of Postoperative Atrial Fibrillation
Tohyama, Takeshi
Ide, Tomomi
Ikeda, Masataka
Nagata, Takuya
Tagawa, Koshiro
Hirose, Masayuki
Funakoshi, Kouta
Sakamoto, Kazuo
Kishimoto, Junji
Todaka, Koji
Nakashima, Naoki
Tsutsui, Hiroyuki
CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY, 2023, 16 (02): : 110 - 112
[10] Atrial Fibrillation: Evaluation of the ESC/ICA method on simulated ECG signals
Taralunga, Dragos D.
Ungureanu, Mihaela
Wolf, Werner
Strungaru, Rodica
2011 E-HEALTH AND BIOENGINEERING CONFERENCE (EHB), 2011,

← 1 2 3 4 5 →