Comput Methods Programs Biomed - Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data.

Tópicos

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Resumo

The objective is to develop a non-invasive automatic method for detection of epileptic seizures with motor manifestations. Ten healthy subjects who simulated seizures and one patient participated in the study. Surface electromyography (sEMG) and motion sensor features were extracted as energy measures of reconstructed sub-bands from the discrete wavelet transformation (DWT) and the wavelet packet transformation (WPT). Based on the extracted features all data segments were classified using a support vector machine (SVM) algorithm as simulated seizure or normal activity. A case study of the seizure from the patient showed that the simulated seizures were visually similar to the epileptic one. The multi-modal intelligent seizure acquisition (MISA) system showed high sensitivity, short detection latency and low false detection rate. The results showed superiority of the multi-modal detection system compared to the uni-modal one. The presented system has a promising potential for seizure detection based on multi-modal data.

Resumo Limpo

object develop noninvas automat method detect epilept seizur motor manifest ten healthi subject simul seizur one patient particip studi surfac electromyographi semg motion sensor featur extract energi measur reconstruct subband discret wavelet transform dwt wavelet packet transform wpt base extract featur data segment classifi use support vector machin svm algorithm simul seizur normal activ case studi seizur patient show simul seizur visual similar epilept one multimod intellig seizur acquisit misa system show high sensit short detect latenc low fals detect rate result show superior multimod detect system compar unimod one present system promis potenti seizur detect base multimod data

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