Sebastian Hafner’s research while affiliated with Karl Landsteiner Institut and other places

Automatic detection of epileptic seizures is clinically important for seizure documentation to objectively evaluate the effectiveness of epilepsy therapies, to prevent sudden unexpected death in epilepsy (SUDEP), to avoid seizure-related injuries, to warn patients about upcoming seizures, and to develop novel, seizure-triggered on-demand therapies. Automatic seizure detection can be performed by analysis of the EEG (scalp-EEG, intracranial EEG, subcutaneous EEG), of motor manifestations during epileptic seizures (surface EMG, accelerometry, video-detection systems, mattress sensors) and of physiologic autonomic parameters (heart and respiration rate, oxygen saturation, sweat secretion, body temperature). Certain parameters can detect exclusively or especially well certain seizure types, but fail to recognize other seizure forms. In any case, there is no one-fits-all solution. Therefore, multimodal seizure detection systems capturing several complementary parameters are increasingly used which can be tailored to the individual patients and their seizures. At present, the use of clinically validated devices for the detection of generalized tonic–clonic and of focal to bilateral tonic–clonic seizures can be recommended, especially in unsupervised patients, where alarms can result in rapid intervention.

Automatic computer-based algorithms for the detection of epileptiform potentials and seizure patterns on EEG facilitate a time-saving, objective method of quantitative EEG interpretation which is available 7/24. For the automatic detection of interictal epileptiform potentials sensitivities range from 65 to 99% with false positive detections of 0,09 to 13,4 per minute. Recent studies documented equal or even better performance of automatic spike detection programs compared with experienced human EEG readers. The seizure detection problem–one of the major problems in clinical epileptology–consists of the fact that the majority of focal onset seizures with impaired awareness and of seizures arising out of sleep occur unnoticed by patients and their caregivers. Automatic seizure detection systems could facilitate objective seizure documentation and thus help to solve the seizure detection problem. Furthermore, seizure detection systems may help to prevent seizure-related injuries and sudden unexpected death in epilepsy (SUDEP), and could be an integral part of novel, seizure-triggered on-demand therapies in epilepsy. During long-term video-EEG monitoring seizure detection systems could improve patient safety, provide a time-saving objective and reproducible analysis of seizure patterns and facilitate automatic computer-based patient testing during seizures. Sensitivities of seizure detection systems range from 75 to 90% with extratemporal seizures being more difficult to detect than temporal seizures. The false positive alarm rate ranges from 0,1 to 5 per 24 hours. Finally, machine learning algorithms, especially deep learning approaches, open a new highly promising era in automatic spike and seizure detection.

Background: Hyposmia is frequently reported as an initial symptom in coronavirus disease 2019 (COVID-19). Objective: As hyposmia accompanies cognitive impairment in several neurological disorders, we aimed to study whether hyposmia represents a clinical biomarker for both neurological involvement and cognitive impairment in mild CO-VID-19. We aimed to study whether olfactory dysfunction (OD) represents a clinical biomarker for both neurological involvement and cognitive impairment in mild COVID-19. Methods: Formal olfactory testing using the Sniffin'Sticks® Screening test, neuropsychological assessment using the Montreal Cognitive Assessment (MoCA), and detailed neurological examination were performed in 7 COVID-19 patients with mild disease course and no history of olfactory or cognitive impairment, and 7 controls matched for age, sex, and education. Controls were initially admitted to a dedicated COVID-19 screening ward but tested negative by real-time PCR. Results: The number of correctly identified odors was significantly lower in COVID-19 than in controls (6 ± 3, vs. 10 ± 1 p = 0.028, r = 0.58). Total MoCA score was significantly lower in COVID-19 patients than in controls (20 ± 5 vs. 26 ± 3, p = 0.042, r = 0.54). Cognitive performance indicated by MoCA was associated with number of correctly identified odors in COVID-19 patients and controls (COVID-19: p = 0.018, 95% CI = 9-19; controls: p = 0.18, r = 0.63, 95% CI = 13-18.5 r = 0.64). Discussion/conclusion: OD is associated with cognitive impairment in controls and mild COVID-19. OD may represent a potentially useful clinical biomarker for subtle and even subclinical neurological involvement in severe acute respiratory distress syndrome coronavirus-2 infection.

Objective To determine if three different commercially available seizure‐detection software packages (Besa 2.0, Encevis 1.7, and Persyst 13) accurately detect seizures with high sensitivity, high specificity, and short detection delay in epilepsy patients undergoing long‐term video–electroencephalography (EEG) monitoring (VEM). Methods Comparison of sensitivity (detection rate), specificity (false alarm rate), and detection delay of three commercially available seizure‐detection software packages in 81 randomly selected patients with epilepsy undergoing long‐term VEM. Results Detection rates on a per‐patient basis were not significantly different between Besa (mean 67.6%, range 0–100%), Encevis (77.8%, 0–100%) and Persyst (81%, 0–100%; P = .059). False alarm rate (per hour) was significantly different between Besa (mean 0.7/h, range 0.01–6.2/h), Encevis (0.2/h, 0.01–0.5/h), and Persyst (0.9/h, 0.04–6.5/h; P < .001). Detection delay was significantly different between Besa (mean 30 s, range 0–431 s), Encevis (25 s, 2–163 s), and Persyst (20 s, 0–167 s; P = .007). Kappa statistics showed moderate to substantial agreement between the reference standard and each seizure‐detection software (Besa: 0.47, 95% confidence interval [CI] 0.36–0.59; Encevis: 0.59, 95% CI 0.47–0.7; Persyst: 0.63, 95% CI 0.51–0.74). Significance Three commercially available seizure‐detection software packages showed similar, reasonable sensitivities on the same data set, but differed in false alarm rates and detection delay. Persyst 13 showed the highest detection rate and false alarm rate with the shortest detection delay, whereas Encevis 1.7 had a slightly lower sensitivity, the lowest false alarm rate, and longer detection delay.

Automatic computer-based algorithms for the detection of epileptiform potentials and seizure patterns on EEG facilitate a time-saving, objective method of quantitative EEG interpretation which is available 7/24. For the automatic detection of interictal epileptiform potentials sensitivities range from 65 to 99% with false positive detections of 0,09 to 13,4 per minute. Recent studies documented equal or even better performance of automatic spike detection programs compared with experienced human EEG readers. The seizure detection problem – one of the major problems in clinical epileptology – consists of the fact that the majority of focal onset seizures with impaired awareness and of seizures arising out of sleep occur unnoticed by patients and their caregivers. Automatic seizure detection systems could facilitate objective seizure documentation and thus help to solve the seizure detection problem. Furthermore, seizure detection systems may help to prevent seizure-related injuries and sudden unexpected death in epilepsy (SUDEP), and could be an integral part of novel, seizure-triggered on-demand therapies in epilepsy. During long-term video-EEG monitoring seizure detection systems could improve patient safety, provide a time-saving objective and reproducible analysis of seizure patterns and facilitate automatic computer-based patient testing during seizures. Sensitivities of seizure detection systems range from 75 to 90% with extratemporal seizures being more difficult to detect than temporal seizures. The false positive alarm rate ranges from 0,1 to 5 per hour. Finally, machine learning algorithms, especially deep learning approaches, open a new highly promising era in automatic spike and seizure detection.