Feasibility of an Integrated Telemonitoring System for Home-Recorded EEG with Automated AI-Based EEG Analysis.

BACKGROUND: Self-recorded EEG by patients at home might present a viable alternative to inpatient epilepsy evaluations. OBJECTIVES AND METHODS: We developed a novel telemonitoring system comprising seamlessly integrated hard- and software with automated AI-based EEG analysis. RESULTS: The first complete study participation results demonstrate feasibility and clinical utility. CONCLUSION: Our telemonitoring solution potentially improves treatment of patients with epilepsy and moreover might help to better distribute resources in the healthcare system.

Idiopathic generalized epilepsies in the epilepsy monitoring unit: Systematic quantification of focal EEG and semiological signs.

OBJECTIVE: Focal seizure symptoms (FSS) and focal interictal epileptiform discharges (IEDs) are common in patients with idiopathic generalized epilepsies (IGEs), but dedicated studies systematically quantifying them both are lacking. We used automatic IED detection and localization algorithms and correlated these EEG findings with clinical FSS for the first time in IGE patients. METHODS: 32 patients with IGEs undergoing long-term video EEG monitoring were systematically analyzed regarding focal vs. generalized IEDs using automatic IED detection and localization algorithms. Quantitative EEG findings were correlated with FSS. RESULTS: We observed FSS in 75% of patients, without significant differences between IGE subgroups. Mostly varying/shifting lateralizations of FSS across successive recorded seizures were seen. We detected a total of 81,949 IEDs, whereof 19,513 IEDs were focal (23.8%). Focal IEDs occurred in all patients (median 13% focal IEDs per patient, range 1.1 - 51.1%). Focal IED lateralization and localization predominance had no significant effect on FSS. CONCLUSIONS: All included patients with IGE showed focal IEDs and three-quarter had focal seizure symptoms irrespective of the specific IGE subgroup. Focal IED localization had no significant effect on lateralization and localization of FSS. SIGNIFICANCE: Our findings may facilitate diagnostic and treatment decisions in patients with suspected IGE and focal signs.

First Clinical Experiences with the New DICOM Neurophysiology Standard.

BACKGROUND: Exchange of EEG data among institutions is complicated due to vendor-specific proprietary EEG file formats. The DICOM standard, which has long been used for storage and exchange of imaging studies, was expanded to store neurophysiology data in 2020. OBJECTIVES: To implement DICOM as an interoperable and vendor-independent storage format for EEG recordings in the Clinic Hietzing. METHODS: A pilot implementation for automated conversion of EEG data from a proprietary to standardized DICOM format was developed. Additionally, EEG review based on a central DICOM archive in a DICOM EEG viewer (encevis by AIT) was implemented. RESULTS: More than 200 long-term video EEG recordings and over 3000 routine EEGs were archived to the central DICOM archive of the WIGEV. CONCLUSION: Using DICOM as a storage format for EEG data is feasible and leads to a substantial improvement of interoperability and facilitates data exchange between institutions.

Seizure detection with deep neural networks for review of two-channel electroencephalogram.

Ultra-long-term electroencephalographic (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 semiautomatic evaluation of these prolonged recordings. We describe the design and validation of a deep neural network for two-channel seizure detection. The model is trained using EEG recordings from 590 patients in a publicly available seizure database. These recordings are based on the full 10-20 electrode system and include seizure annotations created by reviews of the full set of EEG channels. Validation was performed using 48 scalp EEG recordings from an independent epilepsy center and consensus seizure annotations from three neurologists. For each patient, a three-electrode subgroup (two channels with a common reference) of the full montage was selected for validation of the two-channel model. Mean sensitivity across patients of 88.8% and false positive rate across patients of 12.9/day were achieved. The proposed training approach is of great practical relevance, because true recordings from low-channel devices are currently available only in small numbers, and the generation of gold standard seizure annotations in two EEG channels is often difficult. The study demonstrates that automatic seizure detection based on two-channel EEG data is feasible and review of ultra-long-term recordings can be made efficient and effective.

Deep learning for estimation of functional brain maturation from EEG of premature neonates.

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 convolutional neural network (CNN) for regression of EEG maturational age of premature neonates from marginally preprocessed serial EEG recordings is proposed. The CNN was trained and validated using 141 EEG recordings from 43 preterm neonates born below 28 weeks of gestation with normal neurodevelop-mental outcome at 12 months of corrected age. The estimated functional brain maturation between the first and last EEG recording increased in each patient. On average over 96% of repeated measures within an infant had an increasing EEG maturational age according to the post menstrual age at EEG recording time. Our algorithm has potential to be deployed to support neonatologists for accurate estimation of functional brain maturity in premature neonates.

An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard.

OBJECTIVE: To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy. METHODS: We developed an algorithm for automatic detection of EDs, based on a novel deep learning method that requires a low amount of labeled EEG data for training. Detected EDs are automatically grouped into clusters, consisting of the same type of EDs, for rapid visual inspection. We validated the algorithm on an independent dataset of 100 patients with sharp transients in their EEG recordings (54 with epilepsy and 46 with non-epileptic paroxysmal events). The diagnostic gold standard was derived from the video-EEG recordings of the patients' habitual events. RESULTS: The algorithm had a sensitivity of 89% for identifying EEGs with EDs recorded from patients with epilepsy, a specificity of 70%, and an overall accuracy of 80%. CONCLUSIONS: Automated detection of EDs using an artificial intelligence-based computer algorithm had a high sensitivity. Human (expert) supervision is still necessary for confirming the clusters of detected EDs and for describing clinical correlations. Further studies on different patient populations will be needed to confirm our results. SIGNIFICANCE: The automated algorithm we describe here is a useful tool, assisting neurophysiologist in rapid assessment of EEG recordings.

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.

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.

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.