Detection of epileptic convulsions from accelerometry signals through machine learning approach

A seizure detection system in the non-clinical environment would enable long-term monitoring and give better insights into the number of seizures and their characteristics. Moreover, an alarm at seizure onset is important for alerting the parents or care-givers so they could comfort the child and optionally give the treatment. Therefore, we developed a patient-independent automatic algorithm for registration and detection of (tonic-)clonic seizures based on four accelerometers attached to the wrists and ankles. The objective is to classify two second epochs as seizure or non-seizure epochs employing supervised learning techniques. Starting from 140 features found in similar publications, a filter method based on mutual information is applied to remove irrelevant and redundant features. A least-squares support vector machine classifier is used to distinguish seizure and non-seizure epochs based on the selected features. For seizures longer than 30 seconds, median sensitivity of 100%, false detection rate of 0.39 h-1 and alarm delay of 15.2 s over all patients are reached.

[1]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .

[2]  Sándor Beniczky,et al.  Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data , 2012, Comput. Methods Programs Biomed..

[3]  Pierre J. M. Cluitmans,et al.  Detection of Subtle Nocturnal Motor Activity From 3-D Accelerometry Recordings in Epilepsy Patients , 2007, IEEE Transactions on Biomedical Engineering.

[4]  Robert S. Fisher,et al.  Detection of seizure-like movements using a wrist accelerometer , 2011, Epilepsy & Behavior.

[5]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[7]  J. H. Cross,et al.  Revised terminology and concepts for organization of seizures and epilepsies: Report of the ILAE Commission on Classification and Terminology, 2005–2009 , 2010, Epilepsia.

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  Svetlana Kipervasser,et al.  A Novel Portable Seizure Detection Alarm System: Preliminary Results , 2011, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[10]  Josemir W Sander,et al.  The global burden and stigma of epilepsy , 2008, Epilepsy & Behavior.

[11]  Lojini Logesparan,et al.  Optimal features for online seizure detection , 2012, Medical & Biological Engineering & Computing.

[12]  Sabine Van Huffel,et al.  Accelerometry-Based Home Monitoring for Detection of Nocturnal Hypermotor Seizures Based on Novelty Detection , 2014, IEEE Journal of Biomedical and Health Informatics.

[13]  Rosalind W. Picard,et al.  Convulsive seizure detection using a wrist‐worn electrodermal activity and accelerometry biosensor , 2012, Epilepsia.

[14]  Elske Ammenwerth,et al.  Measurement and quantification of generalized tonic–clonic seizures in epilepsy patients by means of accelerometry—An explorative study , 2011, Epilepsy Research.

[15]  Philippe Kahane,et al.  Classification of Epileptic Motor Manifestations and Detection of Tonic–Clonic Seizures With Acceleration Norm Entropy , 2013, IEEE Transactions on Biomedical Engineering.