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

• Published in: Computer methods and programs in biomedicine Vol. 107; no. 2; pp. 97 - 110
• Main Authors: Conradsen, Isa, Beniczky, Sándor, Wolf, Peter, Kjaer, Troels W., Sams, Thomas, Sorensen, Helge B.D.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01.08.2012; Elsevier
• Subjects: Actigraphy - methods; Adult; Algorithms; Artificial Intelligence; Biological and medical sciences; Diagnosis, Computer-Assisted - methods; Electromyography - methods; Epilepsy; Epilepsy, Partial, Motor - diagnosis; Female; Humans; Internal Medicine; Male; Medical sciences; Movement sensors; Other; Pattern Recognition, Automated - methods; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Reproducibility of Results; Seizure detection; Sensitivity and Specificity; Support vector machine learning; Surface EMG sensors; Technology. Biomaterials. Equipments. Material. Instrumentation; Wavelet Analysis; Wavelet packet; Seizure detection; Surface EMG sensors; Movement sensors; Support vector machine learning; Epilepsy; Wavelet packet; Electromyography; Biomedical engineering
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2011.06.005

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.