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 5Ã in power efficiency and 41Ã in execution.
Sampling modulation: An energy efficient novel feature extraction for biosignal processing / Causo, M.; Benatti, Simone; Frappe, A.; Cathelin, A.; Farella, Elisabetta; Kaiser, A.; Benini, Luca; Rabaey, J. M.. - ELETTRONICO. - (2016), pp. 7833803.348-7833803.351. (Intervento presentato al convegno 12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016 tenutosi a chn nel 2016) [10.1109/BioCAS.2016.7833803].
Sampling modulation: An energy efficient novel feature extraction for biosignal processing
Benatti, S.;Farella, E.;Benini, L.;
2016
Abstract
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 5Ã in power efficiency and 41Ã in execution.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
