Automatic ictal HFO detection for determination of initial seizure spread Andreas Graef, Christoph Flamm, Susanne Pirker, Christoph Baumgartner, Manfred Deistler, Fellow, IEEE, and Gerald Matz, Senior Member, IEEE Abstract—High-frequency oscillations (HFOs) are a reliable indicator for the epileptic seizure onset zone (SOZ) in ECoG recordings. We propose a novel method for the automatic detection of ictal HFOs in the ripple band (80-250Hz) based on CFAR matched sub-space filtering. This allows to track the early propagation of ictal HFOs, revealing initial and follow-up epileptic activity on the electrodes. We apply this methodology to two seizures from one patient suffering from focal epilepsy. The electrodes identified are in very good accordance with the visual HFO analysis by clinicians. Furthermore the electrodes with initial HFO activity are correlated well with the SOZ (conventional ϑ-activity). I. I NTRODUCTION A. Background Epilepsies, defined as disorders with recurrent unprovoked seizures, affect approximately 0.7% of the general population [1]. Seizures are characterized by abnormal synchronized neu- ronal discharge of networks in both hemispheres (generalized seizures) or in circumscribed networks in one hemisphere (focal seizures). About one third of focal epilepsy patients suffer from drug resistance, and epilepsy surgery has become a valuable treatment option for them [2]. If a clear-cut definition of the seizure onset zone is possible, the goal is the removal of the epileptogenic tissue in order to abolish the seizures [3]. Up to 70% of patients with drug resistant focal epilepsy be- come seizure free following surgery [4]. Presurgical evaluation comprises prolonged video-EEG recording, high resolution brain imaging as well as neuropsychological tests. If scalp EEG cannot provide sufficient information on the seizure onset zone (SOZ), invasive recording using subdural strip electrodes (electrocorticography, ECoG), which are placed directly on the cortex, is performed in order to determine the epileptic focus [5]. Clinical experts perform the analysis of initial seizure propagation including the determination of the SOZ by visual inspection of the raw ECoG recordings. This current gold standard (cf. [6] and [7] for two recent studies) is based Manuscript received April 12th, 2013 A. Graef (e-mail: andreas.graef@tuwien.ac.at), C. Flamm and M. Deistler are with Vienna University of Technology, Institute for Mathematical Methods in Economics; Vienna, Austria. G. Matz is with Vienna University of Technology, Institute of Telecommuni- cations; Vienna, Austria S. Pirker and C. Baumgartner are with Hospital Hietzing with Neurological Center Rosenhügel; Vienna, Austria. on the onset of ictal rhythmic activity (mostly in the ϑ- or δ-frequency band, [8]). In the last years, a new class of biomarkers has received growing attention [9]: high-frequency oscillations (HFOs), which are low-amplitude EEG correlates commonly observed in two sub-bands, as ripples (80-250Hz) and fast ripples (250-500Hz). They occur interictally as well as ictally [10], and interictal HFOs are excellent markers of the SOZ (higher sensitivity and specificity than spikes, cf. e.g. [11], [12]). In the following we will be interested in ictal HFOs only. They correlate well with the SOZ as well (cf. e.g. [13]) and typically occur several seconds before conventional EEG onset, with a range between 8s [14] and 20s [15]. B. Contributions and state of the art The visual inspection of ECoG data for HFO analysis is a time-demanding, highly subjective task which depends heavily on the individual experience of the investigator. Therefore we suggest a complementary computational approach based on classical signal detection methodology, compare subsection II-A. Several automated HFO detection algorithms have already been proposed in literature. First attempts included rather simple approaches based on band-pass filtering and subsequent use of detection statistics like RMS [16], Teager Energy [17], Line Length ([18] and [19]) or the Hilbert transform [20]. In order to decrease false-positives, increasingly compli- cated multi-step approaches based on the above ideas have come up in the last years, e.g. [21], [22] and [23], and very recently [24]. While these studies focus on the automatic detection of HFOs in interictal EEG (or in databases including ictal and interictal phases), we limit ourselves to the analysis of ictal ECoG recordings. In contrast to the aforementioned approaches, we are not interested in the generation of statistics of HFO rates, but want to track the spread of initial HFOs. For this analysis, seizure onset time is provided by clinicians. Therefore, our contributions to the area of HFO analysis are twofold: 1) We propose a novel algorithm for automatic detection of HFOs. 2) We track the propagation of initial ictal HFOs. Channels showing first HFOs indicate the SOZ (of conventional ictal activity). 35th Annual International Conference of the IEEE EMBS Osaka, Japan, 3 - 7 July, 2013 978-1-4577-0216-7/13/$26.00 ©2013 IEEE 2096