Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at ...home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.
The future of seizure detection Paesschen, Wim Van
Lancet neurology,
March 2018, 2018-03-00, 20180301, Letnik:
17, Številka:
3
Journal Article
Recenzirano
EEG allows detection of a large spectrum of seizure types, even those without motor and autonomic features. ...seizure detection systems with ambulatory, unobtrusive EEG electrodes will make seizure ...detection more reliable. ...devices should be small, unobtrusive, and comfortable to wear day and night for prolonged periods, allowing continuous recording of EEG and, thus, documentation of any seizures, even in a home-based setting. Because there are many diverse seizure types with different electroclinical features and physiological signals, a multimodal measurement of biosignals is important to increase sensitivity and specificity in detection of epileptic seizures. 2 A Van de Vel, K Cuppens, B Bonroy, Non-EEG seizure detection systems and potential SUDEP prevention: state of the art: review and update, Seizure, Vol. 41, 2016, 141-153 3 IC Zibrandtsen, P Kidmose, CB Christensen, TW Kjaer, Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy-a comparison with scalp EEG monitoring, Clin Neurophysiol, Vol. 128, 2017, 2454-2461 4 Y Gu, E Cleeren, J Dan, Comparison between scalp EEG and behind-the-ear EEG for development of a wearable seizure detection system for patients with focal epilepsy, Sensors (Basel), Vol. 18, 2017 5 BP Lucey, JS McLeland, CD Toedebusch, Comparison of a single-channel EEG sleep study to polysomnography, J Sleep Res, Vol. 25, 2016, 625-635 6 ED McKenzie, AS Lim, EC Leung, Validation of a smartphone-based EEG among people with epilepsy: a prospective study, Sci Rep, Vol. 7, 2017, 45567 7 AD Lam, G Deck, A Goldman, EN Eskandar, J Noebels, AJ Cole, Nat Med, Vol. 23, 2017, 678-680 8 K Vandecasteele, T De Cooman, Y Gu, Automated epileptic seizure detection based on wearable ECG and PPG in a hospital environment, Sensors (Basel), Vol. 17, 2017
Objective
To investigate the performance of a multimodal wearable device for the offline detection of tonic seizures (TS) in a pediatric childhood epilepsy cohort, with a focus on patients with ...Lennox–Gastaut syndrome.
Methods
Parallel with prolonged video‐electroencephalography (EEG), the Plug 'n Patch system, a multimodal wearable device using the Sensor Dot and replaceable electrode adhesives, was used to detect TS. Multiple biosignals were recorded: behind‐the‐ear EEG, surface electromyography, electrocardiography, and accelerometer/gyroscope. Biosignals were annotated blindly by a neurologist. Seizure characteristics were described, and performance was assessed by sensitivity, positive predictive value (PPV), F1 score, and false alarm rate (FAR) per hour. Performance was compared to seizure diaries kept by the caretaker.
Results
Ninety‐nine TS were detected in 13 patients. Seven patients (54%) had Lennox–Gastaut syndrome and six patients (46%) had other forms of (developmental) epileptic encephalopathies or drug‐resistant epilepsy. All but one patient had intellectual disability. Overall sensitivity was 41%, with a PPV of 9%, an F1 score of 14%, and a median FAR per hour of 0.75. Performance increased to an F1 score of 66% for nightly seizures lasting at least 10 s (sensitivity 66%, PPV 66%) and 71% for nightly seizures lasting at least 20 s (sensitivity 62%, PPV 82%). For these seizures there were no false alarms in 10 of 13 patients. Sensitivity of seizure diaries reached a maximum of 52% for prolonged (≥20 s) nightly seizures, even though caretakers slept in the same room.
Significance
We showed that it is feasible to use a multimodal wearable device with multiple adhesive sites in children with epilepsy and intellectual disability. For prolonged nightly seizures, offline manual detection of TS outperformed seizure diaries. The recognition of seizure‐specific signatures using multiple modalities can help in the development of automated TS detection algorithms.
Objective
The aim is to report the performance of an electroencephalogram (EEG) seizure‐detector algorithm on data obtained with a wearable device (WD) in patients with focal refractory epilepsy and ...their experience.
Methods
Patients used a WD, the Sensor Dot (SD), to measure two channels of EEG using dry electrode patches during presurgical evaluation and at home for up to 8 months. An automated seizure detection algorithm flagged EEG regions with possible seizures, which we reviewed to evaluate the algorithm's diagnostic yield. In addition, we collected data on usability, side effects, and patient satisfaction with an electronic seizure diary application (Helpilepsy).
Results
Sixteen inpatients used the SD for up to 5 days and had 21 seizures. Sixteen outpatients used the device for up to 8 months and reported 101 focal impaired awareness seizures during the periods selected for analysis. Focal seizure detection sensitivity based on behind‐the‐ear EEG was 52% in inpatients and 23% in outpatients. False detections/h, positive predictive value (PPV), and F1 scores were 7.13%, .11%, and .002% for inpatients and 7.77%, .04%, and .001% for outpatients. Artifacts and low signal quality contributed to poor performance metrics. The seizure detector identified 19 nonreported seizures during sleep, when the signal quality was better. Regarding patients' experience, the likelihood of using the device at 6 months was 62%, and side effects were the main reason for dropping out. Finally, daily and monthly questionnaire completion rates were 33% and 65%, respectively.
Significance
Focal seizure detection sensitivity based on behind‐the‐ear EEG was 52% in inpatients and 23% in outpatients, with high false alarm rates and low PPV and F1 scores. This unobtrusive wearable seizure detection device was well received but had side effects. The current workflow and low performance limit its implementation in clinical practice. We suggest different steps to improve these performance metrics and patient experience.
Summary
Purpose
To evaluate the safety and tolerability of adjunctive brivaracetam (BRV), a high‐affinity synaptic vesicle protein 2A (SV2A) ligand, in adults with uncontrolled epilepsy. Efficacy was ...also assessed in patients with focal seizures as a secondary objective, and explored by descriptive analysis in patients with generalized seizures.
Methods
This was a phase III, randomized, double‐blind, placebo (PBO)‐controlled flexible dose trial (N01254/NCT00504881) in adults (16–70 years) with uncontrolled epilepsy (up to 20% could be patients with generalized epilepsy). After a prospective 4‐week baseline, patients were randomized (3:1) to b.i.d. BRV or PBO, initiated at 20 mg/day and increased, as needed, to 150 mg/day during an 8‐week dose‐finding period. This was followed by an 8‐week stable‐dose maintenance period. The treatment period comprised the dose‐finding period plus the maintenance period (16 weeks).
Key Findings
A total of 480 patients were randomized (BRV 359, PBO 121); of these, 431 had focal epilepsy and 49 had generalized epilepsy. Ninety percent BRV‐ and 91.7% PBO‐treated patients completed the study. Similar proportions of patients (BRV 66.0%, PBO 65.3%) reported adverse events (AEs) during the treatment period. AEs led to treatment discontinuation in 6.1% and 5.0% of BRV‐ and PBO‐treated patients, respectively. The incidence of AEs declined from the dose‐finding (BRV 56.0%, PBO 55.4%) to the maintenance (BRV 36.8%, PBO 40.9%) period. The most frequent AEs during the treatment period were headache (BRV 14.2% vs. PBO 19.8%), somnolence (BRV 11.1% vs. PBO 4.1%), and dizziness (BRV 8.6% vs. PBO 5.8%). The incidence of psychiatric AEs was similar for BRV and PBO (BRV 12.3%, PBO 11.6%). In patients with focal seizures, the baseline‐adjusted percent reduction in seizure frequency/week in the BRV group (n = 323) over PBO (n = 108) was 7.3% (p = 0.125) during the treatment period. The median percent reduction in baseline‐adjusted seizure frequency/week was 26.9% BRV versus 18.9% PBO (p = 0.070), and the ≥50% responder rate was 30.3% BRV versus 16.7% PBO (p = 0.006). In patients with generalized seizures only, the number of seizure days/week decreased from 1.42 at baseline to 0.63 during the treatment period in BRV‐treated patients (n = 36), and from 1.47 at baseline to 1.26 during the treatment period in PBO‐treated patients (n = 13). The median percent reduction from baseline in generalized seizure days/week was 42.6% versus 20.7%, and the ≥50% responder rate was 44.4% versus 15.4% in BRV‐treated and PBO‐treated patients, respectively.
Significance
Adjunctive BRV given at individualized tailored doses (20–150 mg/day) was well tolerated in adults with uncontrolled epilepsy, and our results provided support for further evaluation of efficacy in reducing focal and generalized seizures.
Objective
Home monitoring of 3‐Hz spike–wave discharges (SWDs) in patients with refractory absence epilepsy could improve clinical care by replacing the inaccurate seizure diary with objective ...counts. We investigated the use and performance of the Sensor Dot (Byteflies) wearable in persons with absence epilepsy in their home environment.
Methods
Thirteen participants (median age = 22 years, 11 female) were enrolled at the university hospitals of Leuven and Freiburg. At home, participants had to attach the Sensor Dot and behind‐the‐ear electrodes to record two‐channel electroencephalogram (EEG), accelerometry, and gyroscope data. Ground truth annotations were created during a visual review of the full Sensor Dot recording. Generalized SWDs were annotated if they were 3 Hz and at least 3 s on EEG. Potential 3‐Hz SWDs were flagged by an automated seizure detection algorithm, (1) using only EEG and (2) with an additional postprocessing step using accelerometer and gyroscope to discard motion artifacts. Afterward, two readers (W.V.P. and L.S.) reviewed algorithm‐labeled segments and annotated true positive detections. Sensitivity, precision, and F1 score were calculated. Patients had to keep a seizure diary and complete questionnaires about their experiences.
Results
Total recording time was 394 h 42 min. Overall, 234 SWDs were captured in 11 of 13 participants. Review of the unimodal algorithm‐labeled recordings resulted in a mean sensitivity of .84, precision of .93, and F1 score of .89. Visual review of the multimodal algorithm‐labeled segments resulted in a similar F1 score and shorter review time due to fewer false positive labels. Participants reported that the device was comfortable and that they would be willing to wear it on demand of their neurologist, for a maximum of 1 week or with intermediate breaks.
Significance
The Sensor Dot improved seizure documentation at home, relative to patient self‐reporting. Additional benefits were the short review time and the patients' device acceptance due to user‐friendliness and comfortability.
Objective
Seizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)‐based seizure detection systems are a useful support tool to objectively detect and register seizures ...during long‐term video‐EEG recording. However, this standard full scalp‐EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting. We are developing a wearable device that records EEG with behind‐the‐ear electrodes. In this study, we determined whether the recognition of ictal patterns using only behind‐the‐ear EEG channels is possible. Second, an automated seizure detection algorithm was developed using only those behind‐the‐ear EEG channels.
Methods
Fifty‐four patients with a total of 182 seizures, mostly temporal lobe epilepsy (TLE), and 5284 hours of data, were recorded with a standard video‐EEG at University Hospital Leuven. In addition, extra behind‐the‐ear EEG channels were recorded. First, a neurologist was asked to annotate behind‐the‐ear EEG segments containing selected seizure and nonseizure fragments. Second, a data‐driven algorithm was developed using only behind‐the‐ear EEG. This algorithm was trained using data from other patients (patient‐independent model) or from the same patient (patient‐specific model).
Results
The visual recognition study resulted in 65.7% sensitivity and 94.4% specificity. By using those seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false‐positive detections (FPs)/24 hours with the patient‐independent model. The patient‐specific model achieved 69.1% sensitivity and 0.49 FPs/24 hours.
Significance
Visual recognition of ictal EEG patterns using only behind‐the‐ear EEG is possible in a significant number of patients with TLE. A patient‐specific seizure detection algorithm using only behind‐the‐ear EEG was able to detect more seizures automatically than what patients typically report, with 0.49 FPs/24 hours. We conclude that a large number of refractory TLE patients can benefit from using this device.
Recordings of neural activity, such as EEG, are an inherent mixture of different ongoing brain processes as well as artefacts and are typically characterised by low signal-to-noise ratio. Moreover, ...EEG datasets are often inherently multidimensional, comprising information in time, along different channels, subjects, trials, etc. Additional information may be conveyed by expanding the signal into even more dimensions, e.g. incorporating spectral features applying wavelet transform. The underlying sources might show differences in each of these modes. Therefore, tensor-based blind source separation techniques which can extract the sources of interest from such multiway arrays, simultaneously exploiting the signal characteristics in all dimensions, have gained increasing interest. Canonical polyadic decomposition (CPD) has been successfully used to extract epileptic seizure activity from wavelet-transformed EEG data (Bioinformatics 23(13):i10–i18, 2007; NeuroImage 37:844–854, 2007), where each source is described by a rank-1 tensor, i.e. by the combination of one particular temporal, spectral and spatial signature. However, in certain scenarios, where the seizure pattern is nonstationary, such a trilinear signal model is insufficient. Here, we present the application of a recently introduced technique, called block term decomposition (BTD) to separate EEG tensors into rank- (
L
r
,
L
r
,1) terms, allowing to model more variability in the data than what would be possible with CPD. In a simulation study, we investigate the robustness of BTD against noise and different choices of model parameters. Furthermore, we show various real EEG recordings where BTD outperforms CPD in capturing complex seizure characteristics.
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