Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device
Abstract
Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.
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In epilepsy, wearables are used to detect and differentiate seizures in hospital settings.
“In epilepsy, wearables are used to detect and differentiate seizures in hospital settings.”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [18]
Differentiation of psychogenic non-epileptic seizures from epileptic seizures showed sensitivity of 93-100% with different machine learning approaches.
“Differentiation of psychogenic non-epileptic seizures from epileptic seizures showed a sensitivity of 93–100% with different machine learning approaches, while the specificity ranged from 75 to 91% [17, 18].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [18]
Specificity for differentiating psychogenic non-epileptic seizures from epileptic seizures ranged from 75 to 91%.
“Differentiation of psychogenic non-epileptic seizures from epileptic seizures showed a sensitivity of 93–100% with different machine learning approaches, while the specificity ranged from 75 to 91% [17, 18].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [18]
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- Authors
- Gubbi, J., Kusmakar, S., Rao, A. S., Yan, B., OBrien, T., & Palaniswami, M.
- Affiliations
- The University of Melbourne (AU)
- The Royal Melbourne Hospital (AU)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2016-07
- Volume
- 20
- Issue
- 4
- Pages
- 1061-1072
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W639409994
- Cited by
- 41
- References
- 25
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 3 · 2022: 3 · 2021: 3 · 2020: 5 · 2019: 8 · 2018: 6 · 2017: 1 · 2016: 4
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- psychogenic non-epileptic seizures, convulsions, seizure detection, seizure classification, wearable devices, acceleration sensors, epilepsy diagnosis
- MeSH terms
- Adult, Algorithms, Clothing, Electroencephalography, Epilepsy, Female, Humans, Male, Middle Aged, Seizures, Signal Processing, Computer-Assisted, Cluster Analysis, Monitoring, Ambulatory, Young Adult, Support Vector Machine, Accelerometry
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