Please cite this article in press as: Ayoubian L, et al. Automatic seizure detection in SEEG using high frequency activities in wavelet domain. Med Eng Phys (2012), http://dx.doi.org/10.1016/j.medengphy.2012.05.005 ARTICLE IN PRESS G Model JJBE-2116; No. of Pages 10 Medical Engineering & Physics xxx (2012) xxx–xxx Contents lists available at SciVerse ScienceDirect Medical Engineering & Physics jou rnal h omepa g e: www.elsevier.com/locate/medengphy Automatic seizure detection in SEEG using high frequency activities in wavelet domain L. Ayoubian ∗ , H. Lacoma, J. Gotman Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada a r t i c l e i n f o Article history: Received 18 November 2011 Received in revised form 13 April 2012 Accepted 6 May 2012 Keywords: Epilepsy EEG Automatic seizure detection Wavelet High frequency EMG removal a b s t r a c t Existing automatic detection techniques show high sensitivity and moderate specificity, and detect seizures a relatively long time after onset. High frequency (80–500 Hz) activity has recently been shown to be prominent in the intracranial EEG of epileptic patients but has not been used in seizure detection. The purpose of this study is to investigate if these frequencies can contribute to seizure detection. The system was designed using 30 h of intracranial EEG, including 15 seizures in 15 patients. Wavelet decom- position, feature extraction, adaptive thresholding and artifact removal were employed in training data. An EMG removal algorithm was developed based on two features: Lack of correlation between frequency bands and energy-spread in frequency. Results based on the analysis of testing data (36 h of intracranial EEG, including 18 seizures) show a sensitivity of 72%, a false detection of 0.7/h and a median delay of 5.7 s. Missed seizures originated mainly from seizures with subtle or absent high frequencies or from EMG removal procedures. False detections were mainly due to weak EMG or interictal high frequency activities. The system performed sufficiently well to be considered for clinical use, despite the exclusive use of frequencies not usually considered in clinical interpretation. High frequencies have the potential to contribute significantly to the detection of epileptic seizures. Crown Copyright © 2012 Published by Elsevier Ltd on behalf of IPEM. All rights reserved. 1. Introduction Epilepsy is a neurological disorder that affects 1% of the world’s population. Epileptic seizures are clinical manifestations of abnor- mal and excessive neural discharges in the brain [1,2]. These discharges are often referred as “paroxysmal activity” and appear either during seizure (ictal period) or between seizures (interic- tal periods). Since most patients cannot anticipate seizure events, life threatening situations may arise [3]. Most epileptic patients are treated with antiepileptic medications. In the case of pharma- coresistant focal epilepsy, surgical removal of brain tissue involved in the seizure onset generation is a possible treatment. In order to determine the seizure onset zone, Stereo Electroencephalogra- phy (SEEG) is used which acquire intracranial data using implanted depth electrodes within desired brain tissue. Due to the unpre- dictability of seizures, patients undergo long-term monitoring at hospital, which takes several days to weeks and is accompanied by the collection of large amounts of EEG data. Automatic detection of seizures can facilitate long-term moni- toring for diagnostic purposes and enables physicians to monitor ∗ Corresponding author at: Montreal Neurological Institute and Hospital, 3801 University Street, Montreal, Quebec, Canada H3A 2B4. Tel.: +1 514 625 1354. E-mail address: leilayou 54@yahoo.com (L. Ayoubian). epileptic patients while testing and assessing the benefit of differ- ent medications in order to provide quantitative measure of seizure activity [4,5]. High Frequency (HF) activities which consist of high frequency oscillations (HFOs) and other activities above 80 Hz are visible in many seizures and occur from the very onset [6,7]. HFOs are grouped into ripples (80–250 Hz) and fast ripple (250–500 Hz) and have been associated to seizure genesis [8–10]. HFOs occur dur- ing seizures and interictally, and most often during non-REM sleep [11]. It has been suggested that HFOs increase pre-ictally and the number of HFOs increases a few seconds before seizure onset [6,12]. The number of HFOs during the ictal period is higher than the pre- ictal period and is longer in duration [7]. It is important to note that the HFO oscillations are subgroup of HF activities and the intention of this work is to exploit automatic seizure detection through HF activities. 2. State of the art Most existing automatic seizure detection techniques are divided in two stages; data transformation to a particular domain for the purpose of feature extraction and classification. Features were extracted in time domain [13], frequency domain [14–16], both time and frequency domain [17] or time–frequency domain like wavelet transform. Since EEG is non-stationary, the methods 1350-4533/$ – see front matter. Crown Copyright © 2012 Published by Elsevier Ltd on behalf of IPEM. All rights reserved. http://dx.doi.org/10.1016/j.medengphy.2012.05.005