Brain Topography

, Volume 28, Issue 1, pp 172–183 | Cite as

Detection of Interictal Epileptiform Discharges Using Signal Envelope Distribution Modelling: Application to Epileptic and Non-Epileptic Intracranial Recordings

  • Radek Janca
  • Petr Jezdik
  • Roman Cmejla
  • Martin Tomasek
  • Gregory A. Worrell
  • Matt Stead
  • Joost Wagenaar
  • John G. R. Jefferys
  • Pavel Krsek
  • Vladimir Komarek
  • Premysl Jiruska
  • Petr Marusic
Original Paper

Abstract

Interictal epileptiform discharges (spikes, IEDs) are electrographic markers of epileptic tissue and their quantification is utilized in planning of surgical resection. Visual analysis of long-term multi-channel intracranial recordings is extremely laborious and prone to bias. Development of new and reliable techniques of automatic spike detection represents a crucial step towards increasing the information yield of intracranial recordings and to improve surgical outcome. In this study, we designed a novel and robust detection algorithm that adaptively models statistical distributions of signal envelopes and enables discrimination of signals containing IEDs from signals with background activity. This detector demonstrates performance superior both to human readers and to an established detector. It is even capable of identifying low-amplitude IEDs which are often missed by experts and which may represent an important source of clinical information. Application of the detector to non-epileptic intracranial data from patients with intractable facial pain revealed the existence of sharp transients with waveforms reminiscent of interictal discharges that can represent biological sources of false positive detections. Identification of these transients enabled us to develop and propose secondary processing steps, which may exclude these transients, improving the detector’s specificity and having important implications for future development of spike detectors in general.

Keywords

Spike detection Interictal epileptiform discharges Intracranial recording Automatic detection Hilbert transform Principal component analysis 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Radek Janca
    • 1
  • Petr Jezdik
    • 1
  • Roman Cmejla
    • 1
  • Martin Tomasek
    • 2
  • Gregory A. Worrell
    • 3
  • Matt Stead
    • 3
  • Joost Wagenaar
    • 4
  • John G. R. Jefferys
    • 7
    • 8
  • Pavel Krsek
    • 5
  • Vladimir Komarek
    • 5
  • Premysl Jiruska
    • 2
    • 6
  • Petr Marusic
    • 2
  1. 1.Department of Circuit Theory, Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic
  2. 2.Department of Neurology, 2nd Faculty of Medicine, Motol University HospitalCharles University in PraguePragueCzech Republic
  3. 3.Mayo Systems Electrophysiology LaboratoryMayo ClinicRochesterUSA
  4. 4.Department of Neurology and BioengineeringUniversity of PennsylvaniaPhiladelphiaUSA
  5. 5.Department of Paediatric Neurology, 2nd Faculty of Medicine, Motol University HospitalCharles University in PraguePragueCzech Republic
  6. 6.Department of Developmental Epileptology, Institute of PhysiologyAcademy of Sciences of Czech RepublicPragueCzech Republic
  7. 7.Neuronal Networks Group, School of Clinical and Experimental MedicineUniversity of BirminghamBirminghamUnited Kingdom
  8. 8.Department of PharmacologyUniversity of OxfordOxfordUnited Kingdom

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