@conference{e0e89e8852684673a473128803059a52,
author = "{M. N. Istiaq} Ahsan and  C. Kertesz and  A. Mesaros and  T. Heittola and  A. Knight and  T. Virtanen",
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.",
booktitle = "2019 27th European Signal Processing Conference (EUSIPCO)",
doi = "10.23919/EUSIPCO.2019.8902840",
isbn = "978-1-5386-7300-3",
keywords = "Epileptic seizure detection; convolutional neural network (CNN); sound event detection; audio processing and analysis.",
month = "9",
publisher = "IEEE",
series = "European Signal Processing Conference",
title = "{A}udio-{B}ased {E}pileptic {S}eizure {D}etection",
year = "2019",
}