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Phase 2 studies showed that focal seizures could be detected by algorithms using heart rate variability (HRV) in patients with marked autonomic ictal changes. However, wearable surface electrocardiographic (ECG) devices use electrode patches that need to be changed often and may cause skin irritation. We report the first study of automated seizure detection using a subcutaneously implantable cardiac monitor (ICM; Confirm Rx, Abbott). For this proof-of-concept (phase 1) study, we recruited six patients admitted to long-term video-electroencephalographic monitoring. Fifteen-minute epochs of ECG signals were saved for each seizure and for control (nonseizure) epochs in the epilepsy monitoring unit (EMU) and in the patients' home environment (1–8 months). We analyzed the ICM signals offline, using a previously developed HRV algorithm. Thirteen seizures were recorded in the EMU, and 41 seizures were recorded in the home-monitoring period. The algorithm accurately identified 50 of 54 focal seizures (sensitivity = 92.6%, 95% confidence interval [CI] = 85.6%–99.6%). Twelve of the 13 seizures in the EMU were detected (sensitivity = 92.3%, 95% CI = 77.2%–100%), and 38 of the 41 seizures in the out-of-hospital setting were detected (sensitivity = 92.7%, 95% CI = 84.7%–100%). Four false detections were found in the 141 control (nonseizure) epochs (false alarm rate = 2.7/24 h). Our results suggest that automated seizure detection using a long-term, subcutaneous ICM device is feasible and accurate in patients with focal seizures and autonomic ictal changes.

Objective: Description of typical kinds of EEG reactivity (EEG-R) in post-anoxic coma using a quantitative method. Methods: Study of 101 out-of-hospital cardiac arrest patients, 71 with good outcome (cerebral performance category scale ≤ 2). EEG was recorded 12–24 hours after cardiac arrest and four noxious, one auditory, and one visual stimulation were applied for 30 seconds each. Individual reference intervals for the power in the delta, theta, alpha, and beta bands were calculated based on six 2-second resting epochs just prior to stimulations. EEG-R in consecutive 2-second epochs after stimulation was expressed in Z-scores. Results: EEG-R occurred roughly equally frequent as an increase or as a decrease in EEG activity. Sternal rub and sound stimulation were most provocative with the most pronounced changes as an increase in delta activity 4.5–8.5 seconds after stimulation and a decrease in theta activity 0.5–4.5 seconds after stimulation. These parameters predicted good outcome with an AUC of 0.852 (95 % CI: 0.771–0.932). Conclusions: Quantitative EEG-R is a feasible method for identification of common types of reactivity, for evaluation of stimulation methods, and for prognostication. Significance: This method provides an objective measure of EEG-R revealing knowledge about the nature of EEG-R and its use as a diagnostic tool.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

The International League Against Epilepsy and the International Federation of Clinical Neurophysiology developed a clinical practice guideline on the use of automated seizure detection with wearable devices. They recommend using clinically validated devices for automated detection of generalized tonic-clonic seizures and focal to bilateral tonic-clonic seizures, especially in unsupervised patients, where alarms can result in rapid intervention. In this review, we investigate the published evidence behind the guideline, and we outline the need for future research.

In accordance with Nordzucker’s energy policy, Nordzucker has launched two projects to exploit the energy potential in side streams from sugar production: 1 Enhanced biogas production from anaerobic wastewater treatment with enzymatically hydrolyzed sugar beet pulp 2 Biogas production from tops and tails Before the two projects were started, the biogas potential in beet pulp and tops and tails was tested at Teknologisk Institut, Aarhus, Denmark. The biogas potential in both beet pulp and tops and tails was almost at the same level namely 324 and 313m3 (S.T.P.) CH4/t VSS (volatile suspended solids) respectively. The test also showed that app. 90% of the biogas was realized after app. 9 days. After 35 days, the degradation was completed.

OBJECTIVE: To assess the feasibility and accuracy of seizure detection based on heart rate variability (HRV) using a wearable electrocardiography (ECG) device. Noninvasive devices for detection of convulsive seizures (generalized tonic-clonic and focal to bilateral tonic-clonic seizures) have been validated in phase 2 and 3 studies. However, detection of nonconvulsive seizures still needs further research, since currently available methods have either low sensitivity or an extremely high false alarm rate (FAR). METHODS: In this phase 2 study, we prospectively recruited patients admitted to long-term video-EEG monitoring (LTM). ECG was recorded using a dedicated wearable device. Seizures were automatically detected using HRV parameters computed off-line, blinded to all other data. We compared the performance of 26 automated algorithms with the seizure time-points marked by experts who reviewed the LTM recording. Patients were classified as responders if >66% of their seizures were detected. RESULTS: We recruited 100 consecutive patients and analyzed 126 seizures (108 nonconvulsive and 18 convulsive) from 43 patients who had seizures during monitoring. The best-performing HRV algorithm combined a measure of sympathetic activity with a measure of how quickly HR changes occurred. The algorithm identified 53.5% of the patients with seizures as responders. Among responders, detection sensitivity was 93.1% (95% CI: 86.6%-99.6%) for all seizures and 90.5% (95% CI: 77.4%-97.3%) for nonconvulsive seizures. FAR was 1.0/24 h (0.11/night). Median seizure detection latency was 30 s. Typically, patients with prominent autonomic nervous system changes were responders: An ictal change of >50 heartbeats per minute predicted who would be responder with a positive predictive value of 87% and a negative predictive value of 90%. SIGNIFICANCE: The automated HRV algorithm, using ECG recorded with a wearable device, has high sensitivity for detecting seizures, including the nonconvulsive ones. FAR was low during the night. This approach is feasible in patients with prominent ictal autonomic changes.

This study aims at defining objective parameters reflecting the severity of peri-ictal autonomic changes and their relation to post-ictal generalized electroencephalography (EEG) suppression (PGES), with the view that such changes could be detected by wearable seizure detection systems and prove useful to assess the risk of sudden unexpected death in epilepsy (SUDEP). To this purpose, we assessed peri-ictal changes in heart rate variability (HRV) and correlated them with seizure duration, intensity of electromyography-based ictal muscle activity, and presence and duration of post-ictal generalized EEG suppression (PGES). We evaluated 75 motor seizures from 40 patients, including 61 generalized tonic-clonic seizures (GTCS) and 14 other major motor seizure types. For all major motor seizures, HRV measurements demonstrated a significantly decreased parasympathetic activity and increased sympathetic activity in the post-ictal period. The post-ictal increased sympathetic activity was significantly higher for GTCS as compared with non-GTCS. The degree of peri-ictal decreased parasympathetic activity and increased sympathetic activity was associated with longer PGES (>20 s), longer seizure duration, and greater intensity of ictal muscle activity. Mean post-ictal heart rate (HR) was an independent predictor of PGES duration, seizure duration, and intensity of ictal muscle contraction. Our results indicate that peri-ictal changes in HRV are potential biomarkers of major motor seizure severity.

Although several validated seizure detection algorithms are available for convulsive seizures, detection of nonconvulsive seizures remains challenging. In this phase 2 study, we have validated a predefined seizure detection algorithm based on heart rate variability (HRV) using patient-specific cutoff values. The validation data set was independent from the previously published data set. Electrocardiography (ECG) was recorded using a wearable device (ePatch) in prospectively recruited patients. The diagnostic gold standard was inferred from video–EEG monitoring. Because HRV-based seizure detection is suitable only for patients with marked ictal autonomic changes, we defined responders as the patients who had a>50 beats/min ictal change in heart rate. Eleven of the 19 included patients with seizures (57.9%) fulfilled this criterion. In this group, the algorithm detected 20 of the 23 seizures (sensitivity: 87.0%). The algorithm detected all but one of the 10 recorded convulsive seizures and all of the 8 focal impaired awareness seizures, and it missed 2 of the 4 focal aware seizures. The median sensitivity per patient was 100% (in nine patients all seizures were detected). The false alarm rate was 0.9/24 h (0.22/night). Our results suggest that HRV-based seizure detection has high performance in patients with marked autonomic changes.

Purpose of reviewThere is need for automated seizure detection using mobile or wearable devices, for objective seizure documentation and decreasing morbidity and mortality associated with seizures. Due to technological development, a high number of articles have addressed non-electroencephalography (EEG)-based seizure detection. However, the quality of study-design and reporting is extremely heterogeneous. We aimed at giving the reader a clear picture on the current state of seizure detection, describing the level of evidence behind the various devices.Recent findingsFifteen studies of phase-2 or above, demonstrated that non-EEG-based devices detected generalized tonic-clonic seizures (GTCS) with high sensitivity (≥90%) and low false alarm rate (FAR) (down to 0.2/day). We found limited evidence for detection of motor seizures other than GTCS, mostly from subgroups in larger studies, targeting GTCS. There is little evidence for non-EEG-based detection of nonmotor seizures: sensitivity is low (19-74%) with extremely high FAR (50-216/day).SummaryDetection of GTCS is reliable and there are several, validated devices on the market. However, detection of other seizure types needs further research.

Besides triggering alarms, wearable seizure detection devices record a variety of biosignals that represent biomarkers of seizure severity. There is a need for automated seizure characterization, to identify high-risk seizures. Wearable devices can automatically identify seizure types with the highest associated morbidity and mortality (generalized tonic-clonic seizures), quantify their duration and frequency, and provide data on postictal position and immobility, autonomic changes derived from electrocardiography/heart rate variability, electrodermal activity, respiration, and oxygen saturation. In this review, we summarize how these biosignals reflect seizure severity, and how they can be monitored in the ambulatory outpatient setting using wearable devices. Multimodal recording of these biosignals will provide valuable information for individual risk assessment, as well as insights into the mechanisms and prevention of sudden unexpected death in epilepsy.

Pathological involvement of the noradrenergic locus coeruleus occurs early in Parkinson's disease, and widespread noradrenaline reductions are found at post-mortem. Rapid eye movement sleep behaviour disorder (RBD) accompanies Parkinson's disease and its presence predicts an unfavourable disease course with a higher propensity to cognitive impairment and orthostatic hypotension. MRI can detect neuromelanin in the locus coeruleus while 11C-MeNER PET is a marker of noradrenaline transporter availability. Here, we use both imaging modalities to study the association of RBD, cognition and autonomic dysfunction in Parkinson's disease with loss of noradrenergic function. Thirty non-demented Parkinson's disease patients [16 patients with RBD and 14 without RBD, comparable across age (66.6 ± 6.7 years), sex (22 males), and disease stage (Hoehn and Yahr, 2.3 ± 0.5)], had imaging of the locus coeruleus with neuromelanin sensitive MRI and brain noradrenaline transporter availability with 11C-MeNER PET. RBD was confirmed with polysomnography; cognitive function was assessed with a neuropsychological test battery, and blood pressure changes on tilting were documented; results were compared to 12 matched control subjects. We found that Parkinson's disease patients with RBD showed decreased locus coeruleus neuromelanin signal on MRI (P < 0.001) and widespread reduced binding of 11C-MeNER (P < 0.001), which correlated with amount of REM sleep without atonia. Parkinson's disease with RBD was also associated with a higher incidence of cognitive impairment, slowed EEG activity, and orthostatic hypotension. Reduced 11C-MeNER binding correlated with EEG slowing, cognitive performance, and orthostatic hypotension. In conclusion, reduced noradrenergic function in Parkinson's disease was linked to the presence of RBD and associated with cognitive deterioration and orthostatic hypotension. Noradrenergic impairment may contribute to the high prevalence of these non-motor symptoms in Parkinson's disease, and may be of relevance when treating these conditions in Parkinson's disease.

OBJECTIVES: Rapid eye movement (REM) sleep behavior disorder (RBD) is defined by dream enactment due to a failure of normal muscle atonia. Visual assessment of this muscle activity is time consuming and rater-dependent.METHODS: An EMG computer algorithm for scoring 'tonic', 'phasic' and 'any' submental muscle activity during REM sleep was evaluated compared with human visual ratings. Subsequently, 52 subjects were analyzed with the algorithm. Duration and maximal amplitude of muscle activity, and self-awareness of RBD symptoms were assessed.RESULTS: The computer algorithm showed high congruency with human ratings and all subjects with RBD were correctly identified by excess of submental muscle activity, when artifacts were removed before analysis. Subjects with RBD exhibited prolonged bouts of 'phasic' muscle activity with high amplitude. Self-awareness of RBD symptoms correlated with amount of REM sleep without atonia.CONCLUSIONS: Our proposed algorithm was able to detect and rate REM sleep without atonia allowing identification of RBD. Increased duration and amplitude of muscle activity bouts were characteristics of RBD. Quantification of REM sleep without atonia represents a marker of RBD severity.SIGNIFICANCE: Our EMG computer algorithm can support a diagnosis of RBD while the quantification of altered muscle activity provides a measure of its severity.

Summary Evidence for seizure-induced cardiac dysrhythmia leading to sudden unexpected death in epilepsy (SUDEP) has been elusive. We present a patient with focal cortical dysplasia who has had epilepsy for 19 years and was undergoing presurgical evaluation. The patient did not have any cardiologic antecedents. During long-term video–electroencephalography (EEG) monitoring, following a cluster of secondarily generalized tonic–clonic seizures (GTCS), the patient had prolonged postictal generalized EEG suppression, asystole, followed by arrhythmia, and the patient died despite cardiopulmonary resuscitation. Analysis of heart rate variability showed a marked increase in the parasympathetic activity during the period preceding the fatal seizures, compared with values measured 1 day and 7 months before, and also higher than the preictal values in a group of 10 patients with GTCS without SUDEP. The duration of the QTc interval was short (335–358 msec). This unfortunate case documented during video-EEG monitoring indicates that autonomic imbalance and seizure-induced cardiac dysrhythmias contribute to the pathomechanisms leading to SUDEP in patients at risk (short QT interval). A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.

Introduction Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG is a promising biomarker for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. Methods We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the video-EEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients (356 recording hours) were used as training set of data to optimize the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify and optimize an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Q- and S-peaks can create in the tachogram, which causes error in short-term HRV-analysis. Results The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value ( P + = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch®ECG device, when testing the same dataset. Conclusion The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy. The R-peak detection algorithm is the first important step in creating a portable fully automatic real-time seizure detection for these patients.

Background So far, only generalized tonic-clonic seizures can be reliably detected with non-invasive wearable devices. We aimed to develop an automated seizure detection algorithm using a wearable ECG-device for detecting both GTC and focal seizures. Material and methods We recorded ECG using a dedicated wearable device (ePatch®) during long-term video-EEG monitoring. In this phase-2 clinical study, 100 patients were prospectively recruited; 43 of the patients had 126 seizures (108 focal, 18 GTC) of >20 s duration during recording (941 h training data, 2238 h test data). We analyzed 20 heart rate variability (HRV)-parameters and 6 combinations of these using either 50 or 100 R-R intervals sliding window with maximum overlapping. Each HRV-parameters cut-off value for seizure-alarm was set to 105% of the highest non-seizure period during training data of the same patient. Positive responders of seizure detection were defined, for each HRV-parameter, as patients with >66% of seizures detected. Results In total, 53.5% of the patients were responders for the best performing algorithm. In these patients, the method achieved a sensitivity of 93.1% and false detection rate of 1.1/day. An average of >50 beats/minute HR increase or decrease during seizure(s) is a positive predictor of being a responder of seizure detection (PPV: 87.0%, NPV: 90.0%), making it easy to define for which patients a reliable seizure alarm is feasible. Conclusions High sensitivity and low false positive alarm rates can be achieved with our algorithm analyzing ECG-signals using the wearable device in persons with average HR changes >50 beats/min during seizures.

Purpose The semiology of psychogenic non-epileptic seizures (PNES) can resemble epileptic seizures, and differentiation between epileptic seizures with no EEG-correlate and PNES can be challenging even for trained experts. Therefore, there has been a search for a quantitative measure, other than EEG and semiology that could distinguish PNES from epileptic seizures. We used ECG to measure heart rate variability (HRV) in order to compare maximum autonomic activity of epileptic seizures and PNES. These comparisons could potentially serve as biomarkers for distinguishing these types of clinical episodes. Method Forty-nine epileptic seizures from 17 patients and 24 PNES from 7 patients with analyzable ECG were recorded during long-term video-EEG monitoring. Moving windows of 100 R–R intervals throughout each seizure were used to find maximum values of Cardiac Sympathetic Index (CSI) (sympathetic tonus) and minimum values of Cardiac Vagal Index (CVI), Root-Mean-Square-of-Successive-Differences (RMSSD) and HF-power (parasympathetic tonus). In addition, non-seizure recordings of each patient were used to compare HRV-parameters between the groups. Results The maximum CSI for epilepsy seizures were higher than PNES ( P =0.015). The minimum CVI, minimum RMSSD and HF-power did not show significant difference between epileptic seizures and PNES ( P =0.762; P =0.152; P =0.818). There were no statistical difference of non-seizure HRV-parameters between the PNES and epilepsy patients. Conclusion We found the maximum sympathetic activity accompanying the epileptic seizures to be higher, than that during the PNES. However, the great variation of autonomic response within both groups makes it difficult to use these HRV-measures as a sole measurement in distinguishing epileptic seizures from PNES.

PURPOSE: Near infrared spectroscopy (NIRS) has proved useful in measuring significant hemodynamic changes in the brain during epileptic seizures. The advance of NIRS-technology into wireless and portable devices raises the possibility of using the NIRS-technology for portable seizure detection.METHODS: This study used NIRS to measure changes in oxygenated (HbO), deoxygenated (HbR), and total hemoglobin (HbT) at left and right side of the frontal lobe in 33 patients with epilepsy undergoing long-term video-EEG monitoring. Fifteen patients had 34 focal seizures (20 temporal-, 11 frontal-, 2 parietal-lobe, one unspecific) recorded and analyzed with NIRS. Twelve parameters consisting of maximum increase and decrease changes of HbO, HbR and HbT during seizures (1min before- to 3min after seizure-onset) for left and right side, were compared with the patients' own non-seizure periods (a 2-h period and a 30-min exercise-period). In both non-seizure periods a 4min moving windows with maximum overlapping were applied to find non-seizure maxima of the 12 parameters. Detection was defined as positive when seizure maximum change exceeded non-seizure maximum change.RESULTS: When analyzing the 12 parameters separately the positive seizure detection was in the range of 6-24%. The increase in hemodynamics was in general better at detecting seizures (15-24%) than the decrease in hemodynamics (6-18%) (P=0.02).CONCLUSION: NIRS did not seem to be a suitable technology for generic seizure detection given the device, settings, and methods used in this study. There are still several challenges to overcome before the NIRS-technology can be used as a home-monitoring seizure detection device.

Objectives Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG is a promising biomarker for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. Methods We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the video-EEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients (356 recording hours) were used as training set of data to optimize the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify and optimize an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Q- and S-peaks can create in the tachogram, which causes error in short-term HRV-analysis. Results The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value ( P + = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch® ECG device, when testing the same dataset. Conclusions/significance The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy. The R-peak detection algorithm is the first important step in creating a portable fully automatic real-time seizure detection for these patients.

Objectives REM sleep without atonia (RSWA) is a key feature of REM sleep behavior disorder (RBD). However, detailed visual scoring of EMG on 30 and 3 s intervals is highly time demanding. A computer program with automated scoring algorithm would thus be a valuable asset for sleep professionals scoring RBD. Methods Criteria’s for tonic (2xRMS baseline for >15 s per 30 s epoch), phasic (max amplitude >4 × RMS baseline (or 2 × Tonic), 3 s epochs) and any (tonic and/or phasic) chin EMG activity were implemented in a software algorithm. Sleep stages, respiratory-, arousal- and artifact events were visually scored and imported into the software. Visual scoring of EMG activity done by a blinded board-certified sleep professional was compared to the software algorithm by regression analysis and Kappa statistics. A total of 31989 3 s epochs in 19 patients with Parkinson’s disease (PD) were evaluated (mean age 65.2 ± 9.7, 10 RBD+, 9 RBD−). Results Total percentages of tonic, phasic and any muscle activity correlated strongly between the software algorithm and visual scoring ( R 2 > 0.97 for all activities). Cohen’s Kappa indicated very high agreement on individual events (0.73 for tonic, 0.80 for phasic, 0.83 for any activity). A cut-off value of 10% of any activity separated both groups correctly. Bouts of phasic activity were longer in patients with RBD compared non-RBD (375.4 ± 61.1 versus 478.3 ± 88.9, p = 0.014). Conclusion/significance Our software reliably detects altered chin muscle activity in PD patients when excluding artifact corrupted epochs, providing a fast tool for detecting RBD patients and metrics for correlation analysis with clinical variables.

Jesper Jeppesen *, Sandor Beniczky , Peter Johansen , Per Sidenius , Anders Fuglsang-Frederiksen a Department of Neurophysiology, Aarhus University Hospital, Norrebrogade 44, 8000 Aarhus C, Denmark Department of Clinical Neurophysiology, Danish Epilepsy Centre, Visby Alle 5, 4293 Dianalund, Denmark Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark Department of Neurology, Aarhus University Hospital, Norrebrogade 44, 8000 Aarhus C, Denmark

Objective Near Infrared Spectroscopy (NIRS) has proved useful in measuring significant hemodynamic changes in the brain during epileptic seizures. The advance of NIRS-technology into wireless and portable devices raises the possibility of using the NIRS-technology for portable seizure detection. Methods This study used NIRS to measure changes in oxygenated (HbO), deoxygenated (HbR), and total hemoglobin (HbT) at left and right side of the frontal lobe in 15 epilepsy patients (34 focal seizures) undergoing long-term video-EEG monitoring. Twelve parameters consisting of maximum increase and decrease changes of HbO, HbR and HbT during seizures (1-min before- to 3-min after seizure-onset) for left and right side, were compared with the patients’ own non-seizure periods (a 2-h period and a 30-min exercise-period). In both non-seizure periods a four minutes moving windows with maximum overlapping were applied to find non-seizure maxima of the twelve parameters. Results When analyzing the twelve parameters separately the positive seizure detection was in range of 6–24%. Conclusion NIRS did not seem to be a suitable technology for generic seizure detection given the device, settings, and methods used in this study. Key message There are still several challenges to overcome before the NIRS-technology can be used as home-monitoring seizure detection device.

Objective: To investigate whether epileptic seizures could be predicted or detected by means of spectral analysis of heart rate variability (HRV). Methods: Six patients with temporal lobe epilepsy (4 females, 2 males) participated in the prospective pilot study while enrolled for video/EEG monitoring (24 h/day, 2-4 days). ECG was continuously recorded and 30 min seizure-sessions (25-30 min pre-seizure to 30 sec-5 min post-seizure onset) and 30 min non-seizure-sessions (day- and night sessions for each patient, as control) were chosen for further HRV-analysis. Low frequency (LF) (0.04-0.15 Hz), High frequencies (HF) (0.15-0.40 Hz), LF/HF, LF/(LF+HF) and reciprocal HF-power was determined using continuous FFT- spectral analysis of 64 R-R interval windowing with maximum overlapping. Results: Six seizures were recorded and analyzed from three patients (2 females, 1 male). All of the analyzed EEG-correlated seizures showed reciprocal HF-power peaks between 10 sec pre seizure-onset and 24 sec post seizure-onset with peak amplitudes 2.96-93.63 times higher than control maximum peak. For the other parameters we could not find significant difference between seizure and non-seizure sessions. Conclusion: Specifically high reciprocal HF-power peaks suggest suppressed parasympathetic activity just around seizure-onset time. Seizure detection using HRV-analysis seems to be a promising method for non-invasive seizure detection in the early phase of the clinical event (even preceding the onset).