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Audio-Based Epileptic Seizure Detection

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2019 27th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Number of pages5
ISBN (Electronic)978-9-0827-9703-9
ISBN (Print)978-1-5386-7300-3
DOIs
Publication statusPublished - Sep 2019
Publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference -
Duration: 1 Jan 1900 → …

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
Period1/01/00 → …

Abstract

This paper investigates automatic epileptic seizure detection from audio recordings using convolutional neural networks. The labeling and analysis of seizure events are necessary in the medical field for patient monitoring, but the manual annotation by expert annotators is time-consuming and extremely monotonous. The proposed method treats all seizure vocalizations as a single target event class, and models the seizure detection problem in terms of detecting the target vs non-target classes. For detection, the method employs a convolutional neural network trained to detect the seizure events in short time segments, based on mel-energies as feature representation. Experiments carried out with different seizure types on 900 hours of audio recordings from 40 patients show that the proposed approach can detect seizures with over 80% accuracy, with a 13% false positive rate and a 22.8% false negative rate.

Keywords

  • Epileptic seizure detection, convolutional neural network (CNN), sound event detection, audio processing and analysis.

Publication forum classification

Field of science, Statistics Finland