Hannes Perko

Hannes Perko
AIT Austrian Institute of Technology | ait · Center for Digital Safety & Security

About

28
Publications
4,492
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179
Citations

Publications

Publications (28)
Article
Ultra long‐term EEG registration using minimally invasive low‐channel devices is an emerging technology to assess sporadic seizure events. Highly sensitive automatic seizure detection algorithms are needed for semi‐automatic evaluation of these prolonged recordings. We describe the design and validation of a deep neural network for two‐channel seiz...
Article
Full-text available
Objective: To evaluate the diagnostic performance of artificial intelligence (AI) based algorithms for identifying the presence of interictal epileptiform discharges (IEDs) in routine (20-min) EEG recordings. Methods: We evaluated two approaches: a fully automated one and a hybrid approach, where three human raters applied an operational IED def...
Conference Paper
EEG monitoring of early brain function and development in neonatal intensive care units may help to identify infants with high risk of serious neurological impairment and to assess brain maturation for evaluation of neurodevelopmental progress. Automated analysis of EEG data makes continuous evaluation of brain activity fast and accessible. A convo...
Presentation
Full-text available
Purpose: To systematically gather knowledge about seizure timepoints and their relationship to periods with increased rates of interictal epileptiform discharges (IEDs) from patients during video-EEG monitoring. Method: We retrospectively analyzed continuous long-term video-EEGs of 58 patients (total: 6623 hours, average: 114 hours) recorded using...
Poster
Full-text available
Our computer algorithm automatically detects seizures including rhythmic EEG patterns that show an increased amplitude compared to baseline. EMG signal is extracted by bandpass filtering EEG (30 - 60 Hz) to measure line length (LL) for detection of sustained and excessive ictal EMG activity of generalized tonic-clonic seizures (GTCS). High absolute...
Article
Full-text available
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 c...
Article
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 ep...
Article
Full-text available
Diagnosis of epilepsy is based on the analysis of electroencephalogram (EEG) recordings. Essential epileptiform transients in the EEG are spikes, which are commonly marked manually by biomedical technical assistants which is very time-consuming and error-prone. Automatic spike detectors already exist but still have to be improved to better meet the...
Article
Full-text available
Diagnosis of epilepsy is based on the analysis of electroencephalogram (EEG) recordings. Essential epileptiform transients in the EEG are spikes, which are commonly marked manually by biomedical technical assistants which is very time-consuming and error-prone. Automatic spike detectors already exist but still have to be improved to better meet the...
Article
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. Method A prospective multi-center study was performed in three EMUs including 205 patients. A comparis...
Conference Paper
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...
Article
Full-text available
The contamination of EEG by artifacts requires automatic artifact detection for EEG processing systems. It is particularly important for automatic seizure detection systems since artifacts can mimic rhythmical pathological EEG. In this paper we present a novel approach to artifact detection by considering the spatial distribution of the rhythmicity...
Article
WCN 2013 No: 1167 Topic: 4 — Neuro-critical care Computational EEG analysis for critically ill patients based on the standardized terminology of the ACNS Continuous EEG monitoring is an important tool to recognize clinically invisible deteriorations in critically ill patients. However, manually reviewing continuous EEG recordings requires substanti...
Article
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...
Article
Full-text available
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 r...
Article
Full-text available
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 d...
Article
Full-text available
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 earl...
Article
Full-text available
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...
Conference Paper
Full-text available
Rationale. We propose a novel method for the automatic detection of epileptic seizures and apply it to more than 1.000 hours of EEG-recordings from 17 patients. Automatic detection of epileptic seizures with low latency would be of great benefit in clinical practice. A reliable detection system would relieve personal from continuously monitoring th...
Article
In this paper we analyze the capability of two different signal synchronization measures to detect synchronized periodic signal components. We compare the periodic synchronization index (PSI), which was first introduced for the detection of epileptic seizures in EEG signals, with the widely known phase locking value (PLV). The PSI automatically det...
Conference Paper
Full-text available
In this paper, we propose a novel signal synchronization measure termed phase-synchronization-index (PSI). The PSI can be utilized for the detection of epileptic seizures from EEG recordings. It exploits the fact that synchronous discharges during a seizure often manifest themselves in synchronous, periodic wave-forms in EEG signals recorded from e...
Article
Full-text available
In this paper we introduce a novel method for the characterization of synchronziation and coupling effects in multivariate time series that can be used for the analysis of EEG or ECoG signals recorded during epileptic seizures. The method allows to visualize the spatio-temporal evolution of synchronization and coupling effects that are characterist...

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