Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device

Jayavardhana Gubbi, Shitanshu Kusmakar, Aravinda S. Rao, Bernard Yan, Terence O'Brien, Marimuthu Palaniswami

Research output: Contribution to journalArticleResearchpeer-review

26 Citations (Scopus)


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.

Original languageEnglish
Article number7126915
Pages (from-to)1061-1072
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number4
Publication statusPublished - 1 Jul 2016
Externally publishedYes


  • Accelerometry
  • epileptic seizure (ES)
  • psychogenic nonepileptic seizure (PNES)
  • support vector machines (SVMs)
  • wavelets

Cite this