Hannes Perko

Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units

OBJECTIVE A method for automatic detection of epileptic seizures in long-term scalp-EEG recordings called EpiScan will be presented. EpiScan is used as alarm device to notify medical staff of epilepsy monitoring units (EMUs) in case of a seizure. METHODS A prospective multi-center study was performed in three EMUs including 205 patients. A comparison between EpiScan and the Persyst seizure detector on the prospective data will be presented. In addition, the detection results of EpiScan on retrospective EEG data of 310 patients and the public available CHB-MIT dataset will be shown. RESULTS A detection sensitivity of 81% was reached for unequivocal electrographic seizures with false alarm rate of only 7 per day. No statistical significant differences in the detection sensitivities could be found between the centers. The comparison to the Persyst seizure detector showed a lower false alarm rate of EpiScan but the difference was not of statistical significance. CONCLUSIONS The automatic seizure detection method EpiScan showed high sensitivity and low false alarm rate in a prospective multi-center study on a large number of patients. SIGNIFICANCE The application as seizure alarm device in EMUs becomes feasible and will raise the efficiency of video-EEG monitoring and the safety levels of patients.

EpiScan: Online seizure detection for epilepsy monitoring units

An online seizure detection algorithm for long-term EEG monitoring is presented, which is based on a periodic waveform analysis detecting rhythmic EEG patterns and an adaptation module automatically adjusting the algorithm to patient-specific EEG properties. The algorithm was evaluated using 4.300 hours of unselected EEG recordings from 48 patients with temporal lobe epilepsy. For 66% of the patients the algorithm detected 100% of the seizures. A mean sensitivity of 83% was achieved. An average of 7.2 false alarms within 24 hours for unselected EEG makes the algorithm attractive for epilepsy monitoring units.

Combining time series and frequency domain analysis for a automatic seizure detection

The detection of epileptic seizures in long-term electroencephalographic (EEG) recordings is a time-consuming and tedious task requiring specially trained medical experts. The EpiScan [1-4] seizure detection algorithm developed by the Austrian Institute of Technology (AIT) has proven to achieve high detection performance with a robust false alarm rate in the clinical setting. This paper introduces a novel time domain method for detection of epileptic seizure patterns with focus on irregular and distorted rhythmic activity. The method scans the EEG for sequences of similar epileptiform discharges and uses a combination of duration and similarity measure to decide for a seizure. The resulting method was tested on an EEG database with 275 patients including over 22000h of unselected and uncut EEG recording and 623 seizures. Used in combination with the EpiScan algorithm we increased the overall sensitivity from 70% to 73% while reducing the false alarm rate from 0.33 to 0.30 alarms per hour.

Automatic detection of the seizure onset zone based on ictal EEG

In this paper we show a proof of concept for novel automatic seizure onset zone detector. The proposed approach utilizes the Austrian Institute of Technology (AIT) seizure detection system EpiScan extended by a frequency domain source localization module. EpiScan was proven to detect rhythmic epileptoform seizure activity often seen during the early phase of epileptic seizures with reasonable high sensitivity and specificity. Additionally, the core module of EpiScan provides complex coefficients and fundamental frequencies representing the rhythmic activity of the ictal EEG signal. These parameters serve as input to a frequency domain version of the Minimum Variance Beamformer to estimate the most dominant source. The position of this source is the detected seizure onset zone. The results are compared to a state of the art wavelet transformation approach based on a manually chosen frequency band. Our first results are encouraging since they coincide with those obtained with the wavelet approach and furthermore show excellent accordance with the medical report for the majority of analyzed seizures. In contrast to the wavelet approach our method has the advantage that it does not rely on a manual selection of the frequency band.

Spatial correlation based artifact detection for automatic seizure detection in EEG

Automatic EEG-processing systems such as seizure detection systems are more and more in use to cope with the large amount of data that arises from long-term EEG-monitorings. Since artifacts occur very often during the recordings and disturb the EEG-processing, it is crucial for these systems to have a good automatic artifact detection. We present a novel, computationally inexpensive automatic artifact detection system that uses the spatial distribution of the EEG-signal and the location of the electrodes to detect artifacts on electrodes. The algorithm was evaluated by including it into the automatic seizure detection system EpiScan and applying it to a very large amount of data including a large variety of EEGs and artifacts.

High density wireless EEG prototype: Design and evaluation against reference equipment.

A high density wireless electroencephalographic (EEG) platform has been designed. It is able to record up to 64 EEG channels with electrode to tissue impedance (ETI) monitoring. The analog front-end is based on two kinds of low power ASICs implementing the active electrodes and the amplifier. A power efficient compression algorithm enables the use of continuous wireless transmission of data through Bluetooth for real-time monitoring with an overall power consumption of about 350 mW. EEG acquisitions on five subjects (one healthy subject and four patients suffering from epilepsy) have been recorded in parallel with a reference system commonly used in clinical practice and data of the wireless prototype and reference system have been processed with an automatic tool for seizure detection and localization. The false alarm rates (0.1-0.5 events per hour) are comparable between the two system and wireless prototype also detected the seizure correctly and allowed its localization.

Automatic ictal onset source localization in presurgical epilepsy evaluation

OBJECTIVE To test the diagnostic accuracy of a new automatic algorithm for ictal onset source localization (IOSL) during routine presurgical epilepsy evaluation following STARD (Standards for Reporting of Diagnostic Accuracy) criteria. METHODS We included 28 consecutive patients with refractory focal epilepsy (25 patients with temporal lobe epilepsy (TLE) and 3 with extratemporal epilepsy) who underwent resective epilepsy surgery. Ictal EEG patterns were analyzed with a novel automatic IOSL algorithm. IOSL source localizations on a sublobar level were validated by comparison with actual resection sites and seizure free outcome 2 years after surgery. RESULTS Sensitivity of IOSL was 92.3% (TLE: 92.3%); specificity 60% (TLE: 50%); positive predictive value 66.7% (TLE: 66.7%); and negative predictive value 90% (TLE: 85.7%). The likelihood ratio was more than ten times higher for concordant IOSL results as compared to discordant results (p = 0.013). CONCLUSIONS We demonstrated the clinical feasibility of our IOSL approach yielding reasonable high performance measures on a sublobar level. SIGNIFICANCE Our IOSL method may contribute to a correct localization of the seizure onset zone in temporal lobe epilepsy and can readily be used in standard epilepsy monitoring settings. Further studies are needed for validation in extratemporal epilepsy.

F08. Automatic spike detection in intracerebral depth electrode recordings

Introduction Intracranial recordings, like intracerebral depth electrode recordings are considered to be the best choice for preoperative invasive evaluation when standard electro-clinical examinations are not conclusive. These recordings reflect a vast amount of interictal epileptic discharges, the so called spikes, which are in general abundant compared to seizure activity. Manually reviewing these recordings to find the spatial origin of seizures and spikes is still state of the art, which is a very exhausting task due to the large number of signals recorded. In order to enable a time-efficient evaluation of such recordings, we developed a spike detection algorithm which performs automatic detection, spatial clustering and smart visualization of spike clusters. Methods The automatic spike detection algorithm for depth electrode recordings is based on our existing spike detection algorithm for surface EEG. The spike detection algorithm assesses morphological and topographical potential field features. Both feature groups had to be adapted for depth electrode recordings. In these recordings spikes are shorter in duration, often have higher amplitude and the properties of the potential field compared to surface EEG are very different. Due to the morphological similarity of individual waves of the posterior alpha rhythm and spikes, the detection within such rhythmic periods is suppressed. In order to speed up the review, we developed a spatial clustering and a smart visualization of spike clusters. In the so-called overlay plot the signals of all individual spikes of each cluster are displayed one above the other in one figure, allowing for quickly distinguishing between true spikes and artifacts. In order to assess the sensitivity and precision of the developed algorithms, 1400 spikes in 3 datasets from different patients recorded at the Academic Center of Epileptology, Kempenhaeghe/MUMC were annotated by an experienced biomedical assistant. Results The automatic spike detection algorithm detected 54% of all spikes on average. The overall precision was 39%. Sensitivity values reach from 18% to 81%. However, also for the patient with 18% sensitivity the algorithm was able to detect several spikes on all those electrodes where also the human reviewer detected them. For one patient a very low precision of 6% was measured. This small precision value came from repetitive artifacts which are very similar to spikes and are even difficult to distinguish for human experts. Removing the most evident artifact clusters by using the overlay plot, the precision increases from 6% to 30%. Doing the same for all patients an average precision of 68% is achieved. Conclusion Our automatic method was able to detect spikes with a high sensitivity on average. Moreover, our algorithm always detected spikes on all the electrodes where the expert also identified them allowing for a profound diagnosis. The smart combination of detection, clustering and visualization potentially enables a more time-efficient review.

Automatic seizure detection based on the activity of a set of current dipoles: First steps

In this paper we show advantages of using an advanced montage scheme with respect to the performance of automatic seizure detection systems. The main goal is to find the best performing montage scheme for our automatic seizure detection system. The new virtual montage is a fix set of dipoles within the brain. The current density signals for these dipoles are derived from the scalp EEG signals based on a smart linear transformation. The reason for testing an alternative approach is that traditional montages (reference, bipolar) have some limitations, e.g. the detection performance depends on the choice of the reference electrode and an extraction of spatial information is often demanding. In this paper we explain the detailed setup of how to adapt a modern seizure detection system to use current density signals. Furthermore, we show results concerning the detection performance of different montage schemes and their combination.