Antidictionary-Based Cardiac Arrhythmia Classification for Smart ECG Sensors

Duforest, Julien; Larras, Benoit; Frappe, Antoine; Deepu, Chacko John; Märtens, Olev

Document type :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

DOI :

10.1109/ISCAS48785.2022.9937853

Title :

Antidictionary-Based Cardiac Arrhythmia Classification for Smart ECG Sensors

Author(s) :

Duforest, Julien [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Larras, Benoit [Auteur]

Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Frappe, Antoine [Auteur]

JUNIA [JUNIA]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Deepu, Chacko John [Auteur]
University College Dublin [Dublin] [UCD]
Märtens, Olev [Auteur]
Tallinn University of Technology [TalTech]

Conference title :

2022 IEEE International Symposium on Circuits and Systems (ISCAS)

City :

Austin, TX

Country :

Etats-Unis d'Amérique

Start date of the conference :

2022-05-28

Publisher :

IEEE

English keyword(s) :

Cardiac Arrhythmia Classification
Electrocardiogram
Event-driven
Antidictionary

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

Cardiovascular diseases can be detected early by analyzing the electrocardiogram of a patient using wearable systems. In the context of smart sensors, detecting arrhythmias with good accuracy and ultra-low power consumption ...
Show more >Cardiovascular diseases can be detected early by analyzing the electrocardiogram of a patient using wearable systems. In the context of smart sensors, detecting arrhythmias with good accuracy and ultra-low power consumption is required for long-term monitoring. This paper presents a novel cardiac arrhythmia classification method based on antidictionaries. The features are sequences of consecutive slopes that are generated from event-driven processing of the input signal. The proposed system shows an average detection accuracy of 98% while offering an ultra-low complexity. This antidictionary-based method is also particularly suited to imbalanced datasets since the antidictionaries are created exclusively from heartbeats classified as normal beats.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Event Driven Artificial Intelligence Hardware for Biomedical Sensors

Collections :