Time‐frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures
Open accessAbstract
Summary: Purpose: A definite diagnosis of psychogenic nonepileptic seizures (PNES) usually requires in‐patient video–electroencephalography (EEG) monitoring. Previous research has shown that convulsive psychogenic nonepileptic seizures (PNES) demonstrate a characteristic pattern of rhythmic movement artifact on the EEG. Herein we sought to examine the potential for time‐frequency mapping of data from a movement‐recording device (accelerometer) worn on the wrist as a diagnostic tool to differentiate between convulsive epileptic seizures and PNES. Methods: Time‐frequency mapping was performed on accelerometer traces obtained during 56 convulsive seizure‐like events from 35 patients recorded during in‐patient video‐EEG monitoring. Twenty‐six patients had PNES, eight had epileptic seizures, and one had both seizure types. The time‐frequency maps were derived from fast Fourier transformations to determine the dominant frequency for sequential 2.56‐s blocks for the course of each event. Key Findings: The coefficient of variation (C o V) of limb movement frequency for the PNES events was less than for the epileptic seizure events (median, 17.18% vs. 52.23%; p < 0.001). A blinded review of the time‐frequency maps by an epileptologist was accurate in differentiating between the event types, that is, 38 (92.7%) of 41 and 6 (75%) of 8 nonepileptic and epileptic seizures, respectively, were diagnosed correctly, with seven events classified as “nondiagnostic.” Using a C o V cutoff score of 32% resulted in similar classification accuracy, with 42 (93%) of 45 PNES and 10 (91%) of 11 epileptic seizure events correctly diagnosed. Significance: Time‐frequency analysis of data from a wristband movement monitor could be utilized as a diagnostic tool to differentiate between epileptic and nonepileptic convulsive seizure‐like events.
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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 [17]
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 [17]
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 [17]
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- Authors
- Bayly, J., Carino, J., Petrovski, S., Smit, M., Fernando, D. A., Vinton, A., Yan, B., Gubbi, J. R., Palaniswami, M. S., & O'Brien, T. J.
- Affiliations
- The Royal Melbourne Hospital (AU)
- The University of Melbourne (AU)
- Corresponding author
- O'Brien, Terence J.
- Venue
- Epilepsia
- Publisher
- Wiley
- Published
- 2013-05-03
- Volume
- 54
- Issue
- 8
- Pages
- 1402-1408
- ISSN
- 0013-9580 (ISSN-L)
- 1528-1167
- OpenAlex
- W1985768052
- Cited by
- 50
- References
- 13
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 1 · 2022: 2 · 2021: 5 · 2020: 4 · 2019: 3 · 2018: 9 · 2017: 4 · 2016: 7 · 2015: 4 · 2014: 4
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Epilepsia (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Psychosomatic Disorders and Their Treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- psychogenic non-epileptic seizures, time-frequency analysis, wrist accelerometer, coefficient of variation, motion artifacts, video-EEG monitoring
- MeSH terms
- Adult, Aged, Aged, 80 and over, Brain Mapping, Conversion Disorder, Electroencephalography, Epilepsy, Extremities, Female, Humans, Kinetocardiography, Male, Middle Aged, Movement, Periodicity, Psychophysiologic Disorders, Retrospective Studies, Time Factors, Young Adult
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