Abstract
Patients that undergo treatment in the epilepsy
clinic Kempenhaeghe in the Netherlands are being monitored
with different sensory signals, including audio. In this paper a
new patient monitoring system for the detection of epileptic
seizures through audio classification is proposed. The proposed
system enables automated detection of epileptic seizures,
which can have a large positive impact on the daily care of
epilepsy patients. This system includes three stages. First, the
signal is enhanced by means of a microphone array, followed
by a noise subtraction procedure. Secondly, the signal is analyzed
by audio event detection and audio classification. When
an audio event is detected, features are extracted from the
signal. Bayesian decision theory is used to classify the feature
vector based on a discriminant analysis. Finally, it is decided
whether or not to trigger an alarm. The performance is tested
with audio signals obtained from measurements with epileptic
patients. The results show that, with a limited set of features,
good classification results can be achieved.
Original language | English |
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Title of host publication | 4th European conference of the international federation for medical and biological engineering : ECIFMBE 2008, 23-27 November 2008, Antwerp, Belgium |
Editors | Jos Vander Sloten, Pascal Verdonck, Marc Nyssen |
Place of Publication | Berlin |
Publisher | Springer |
Pages | 1450-1454 |
ISBN (Print) | 9783540892076 |
Publication status | Published - 2008 |