Title :

Event-driven ECG classification using an open-source, LC-ADC based non-uniformly sampled dataset

Author(s) :

Saeed, M. []
University College Dublin [Dublin] [UCD]
Wang, Q. []
University College Dublin [Dublin] [UCD]
Märtens, O. [Auteur]
Tallinn University of Technology [TTÜ]
Larras, Benoit [Auteur]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Frappé, Antoine [Auteur]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Cardiff, B. [Auteur]
University College Dublin [Dublin] [UCD]
John, D. [Auteur]
University College Dublin [Dublin] [UCD]

Conference title :

53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021

City :

Daegu

Country :

Corée du Sud

Start date of the conference :

2021-05-22

Book title :

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Publication date :

2021

English keyword(s) :

Artificial neural networks
Cardiac arrhythmia classification
Event-driven data
LC-ADC
Wearable sensors

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

In this article, non-uniformly sampled electrocardiogram (ECG) signals obtained from level-crossing analog-to-digital converters (LC-ADCs) are analyzed for event-driven classification and compression performance. The signal ...
Show more >In this article, non-uniformly sampled electrocardiogram (ECG) signals obtained from level-crossing analog-to-digital converters (LC-ADCs) are analyzed for event-driven classification and compression performance. The signal compression results show that it is important to assess the distortion in event-driven signals when simulating LC-ADC models, especially at lower resolutions and larger quantization steps. The effects of varying the LC-ADC parameters for the application of cardiac arrhythmia classifiers are also assessed using an artificial neural network (ANN) and the MIT-BIH Arrhythmia Database. In comparison with uniformly-sampled data, it is possible to achieve comparable classification accuracy at a much lower complexity with event-driven ECG signals. The results show the best event-driven model achieves over 97% accuracy with 79% reduction in ANN complexity with signal-to-distortion ratio (S/D)≥21dB. For S/D<21dB, the best event-driven model achieves 93% accuracy with a 96% reduction in ANN complexity. An open-source event-driven arrhythmia database is also presented.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Event Driven Artificial Intelligence Hardware for Biomedical Sensors

Comment :

The open-source event-driven ECG dataset is available at https://github.com/jedaiproject/Open-Source-Event-Driven-ECG-Dataset

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL