Titre :

Sampling modulation: An energy efficient novel feature extraction for biosignal processing

Auteur(s) :

Causo, M. [Auteur]
Benatti, S. [Auteur]
Centro di Ateneo di Studi e Attività Spaziali “Giuseppe Colombo” [CISAS]
Frappe, Antoine [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Cathelin, A. [Auteur]
STMicroelectronics [Crolles] [ST-CROLLES]
Farella, E. [Auteur]
Kaiser, Andreas [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Benini, L. [Auteur]
Institut für Automatik - ETH Zurich
Rabaey, J. [Auteur]

Titre de la manifestation scientifique :

2016 IEEE Biomedical Circuits and Systems Conference (BioCAS)

Ville :

Shanghai

Pays :

Chine

Date de début de la manifestation scientifique :

2016-10-17

Titre de la revue :

Proceedings of 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS)

Éditeur :

IEEE

Date de publication :

2016

Discipline(s) HAL :

Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Extracting useful information from human bio potentials is an essential component of many wearable health applications. Yet the feature extraction itself can be computationally demanding, and may rapidly exhaust the meager ...
Lire la suite >Extracting useful information from human bio potentials is an essential component of many wearable health applications. Yet the feature extraction itself can be computationally demanding, and may rapidly exhaust the meager energy supply available to the sensor node. General-purpose time-frequency analysis techniques, such as the Discrete Wavelet Transform (DWT) are widely used, but are computationally demanding and may represent overkill. This work presents a feature extraction technique for biopotential time-frequency analysis, based on the modulation of finite sample differences. The technique is applied to EEG-based seizure detection (feeding a Support Vector Machine (SVM) classifier) and reaches the performance of a DWT implementation, while offering a gain of 5× in power efficiency and 41× in execution.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet Européen :

Multiscale Thermal Management of Computing Systems

Collections :

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

Source :

Harvested from HAL