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Communication Dans Un Congrès Année : 2016

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

Résumé

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.
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Dates et versions

hal-03377041 , version 1 (13-10-2021)

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Citer

M. Causo, S. Benatti, Antoine Frappé, A. Cathelin, E. Farella, et al.. Sampling modulation: An energy efficient novel feature extraction for biosignal processing. 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS), Oct 2016, Shanghai, China. pp.348-351, ⟨10.1109/BioCAS.2016.7833803⟩. ⟨hal-03377041⟩
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