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Automated detection of convulsive seizures using a wearable accelerometer device
journal contribution
posted on 2019-02-01, 00:00 authored by Shitanshu Kusmakar, Chandan KarmakarChandan Karmakar, Bernard Yan, Terence Obrien, Ramanathan Muthuganapathy, Marimuthu PalaniswamiEpileptic seizure detection requires specialized approaches such as video/electroencephalography monitoring. However, these approaches are restricted mainly to hospital setting and requires video/EEG analysis by experts, which makes these approaches resource- and labor-intensive. In contrast, we aim to develop a wireless remote monitoring system based on a single wrist-worn accelerometer device, which is sensitive to multiple types of convulsive seizures and is capable of detecting seizures with short duration. Simple time domain features including a new set of Poincar´e plot based features were extracted from the active movement events recorded using a wrist-worn accelerometer device. The best features were then selected using the area under the ROC curve analysis. Kernelized support vector data description (SVDD) was then used to classify non-seizure and seizure events. The proposed algorithm was evaluated on 5;576h of recordings from 79 patients and detected 40 (86:95%) of 46 convulsive seizures (generalized tonic-clonic (GTCS), psychogenic non-epileptic (PNES), and complex partial seizures (CPS)) from twenty patients with a total of 270 false alarms (1:16=24h). Furthermore, the algorithm showed a comparable performance (sensitivity 95:23% and false alarm rate 0:64=24h) with respect to existing unimodal and multi-modal methods for GTCS detection. The promising results shows the potential to build an ambulatory monitoring convulsive seizure detection system. A wearable accelerometer based seizure detection system would aid in continuous assessment of convulsive seizures in a timely and non-invasive manner.
History
Journal
IEEE transations on biomedical engineeringVolume
66Issue
2Pagination
421 - 432Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
eISSN
1558-2531Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2017, IEEEUsage metrics
Keywords
Accelerometergeneralized tonic-clonic seizures (GTCS)psychogenic non-epileptic seizures (PNES)complex partial seizures (CPS)support vector data description (SVDD)wearable deviceScience & TechnologyTechnologyEngineering, BiomedicalEngineeringPSYCHOGENIC NONEPILEPTIC SEIZURESTONIC-CLONIC SEIZURESMOTOR SEIZURESEPILEPSYELECTROMYOGRAPHYCLASSIFICATIONPREDICTIONSYSTEMArtificial Intelligence and Image Processing
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