ObjectiveTo determine the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) using a wearable surface EMG device.MethodsWe prospectively tested the technical performance and diagnostic accuracy of real-time seizure detection using a wearable surface EMG device. The seizure detection algorithm and the cutoff values were prespecified. A total of 71 patients, referred to long-term video-EEG monitoring, on suspicion of GTCS, were recruited in 3 centers. Seizure detection was real-time and fully automated. The reference standard was the evaluation of video-EEG recordings by trained experts, who were blinded to data from the device. Reading the seizure logs from the device was done blinded to all other data.ResultsThe mean recording time per patient was 53.18 hours. Total recording time was 3735.5 hours, and device deficiency time was 193 hours (4.9% of the total time the device was turned on). No adverse events occurred. The sensitivity of the wearable device was 93.8% (30 out of 32 GTCS were detected). Median seizure detection latency was 9 seconds (range −4 to 48 seconds). False alarm rate was 0.67/d.ConclusionsThe performance of the wearable EMG device fulfilled the requirements of patients: it detected GTCS with a sensitivity exceeding 90% and detection latency within 30 seconds.Classification of evidenceThis study provides Class II evidence that for people with a history of GTCS, a wearable EMG device accurately detects GTCS (sensitivity 93.8%, false alarm rate 0.67/d).

Summary Purpose:  Tonic seizures and the tonic phase of tonic–clonic epileptic seizures are defined as “sustained tonic” muscle contraction lasting a few seconds to minutes. Visual inspection of the surface electromyogram (EMG) during seizures contributed considerably to a better understanding and accurate diagnosis of several seizure types. However, quantitative analysis of the surface EMG during the epileptic seizures has received surprisingly little attention until now. The aim of our study was to elucidate the pathomechanism of the tonic muscle activation during epileptic seizures. Methods:  Surface EMG was recorded from the deltoid muscles, on both sides, during 63 seizures from 20 patients with epilepsy (10 with generalized tonic and 10 with tonic–clonic seizures). Twenty age‐ and gender‐matched normal controls simulated 100 generalized tonic seizures. To characterize the signal properties we calculated the root mean square (RMS) of the amplitudes, the median frequency (MF), and the coherence. Based on the spectrograms of both epileptic and simulated seizures, we chose to determine the relative spectral power (RP) in the higher (100–500 Hz) frequency domain. Key Findings:  During the tonic seizures there was a significant shift toward higher frequencies, expressed by an increase in the MF and the RP (100–500 Hz). The amplitude characteristic of the signal (RMS) was significantly higher during the tonic phase of the tonic–clonic seizures as compared to the simulated ones, whereas the RMS of the tonic seizures was significantly lower than the simulated ones. The EMG–EMG coherence was significantly higher during the epileptic seizures (both types) as compared to the simulated ones. Significance:  Our results indicate that the mechanism of muscle activation during epileptic seizures is different from the physiologic one. Furthermore the sustained muscle activation during the tonic phase of tonic–clonic seizures is different from that during tonic seizures: The tonic phase of tonic–clonic seizures is characterized by increased amplitude of the signal, whereas tonic seizures are produced by a significant increase in the frequency of the signal.

Patients are not able to call for help during a generalized tonic-clonic epileptic seizure. Our objective was to develop a robust generic algorithm for automatic detection of tonic-clonic seizures, based on surface electromyography (sEMG) signals suitable for a portable device. Twenty-two seizures were analyzed from 11 consecutive patients. Our method is based on a high-pass filtering with a cutoff at 150 Hz, and monitoring a count of zero crossings with a hysteresis of ±50 μV . Based on data from one sEMG electrode (on the deltoid muscle), we achieved a sensitivity of 100% with a mean detection latency of 13.7 s, while the rate of false detection was limited to 1 false alarm per 24 h. The overall performance of the presented generic algorithm is adequate for clinical implementation.

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We implemented a modified version of a previously published algorithm for detection of generalized tonic-clonic seizures into a prototype wireless surface electromyography (sEMG) recording device. The method was modified to require minimum computational load, and two parameters were trained on prior sEMG data recorded with the device. Along with the normal sEMG recording, the device is able to set an alarm whenever the implemented algorithm detects a seizure. These alarms are annotated in the data file along with the signal. The device was tested at the Epilepsy Monitoring Unit (EMU) at the Danish Epilepsy Center. Five patients were included in the study and two of them had generalized tonic-clonic seizures. All patients were monitored for 2-5 days. A double-blind study was made on the five patients. The overall result showed that the device detected four of seven seizures and had a false detection rate of 0.003/h or one in twelve days.

SUMMARYPurpose: Previous studies have suggested that cognitive tasks modulate (provoke or inhibit) the epileptiform electroencephalography (EEG) discharges (EDs) in patients with juvenile myoclonic epilepsy (JME). Their inhibitory effect was found to be especially frequent (64-90%). These studies arbitrarily defined modulation as a >100% increase or >50% decrease of the EDs compared with baseline, which may not sufficiently distinguish from spontaneous fluctuations. The aim of our study was to assess the modulation of EDs and the precipitation of myoclonic seizures by cognitive tasks and by conventional provocation methods, taking into account also the spontaneous fluctuation of EDs. Method: Sixty patients with JME underwent video-EEG recordings including 50-min baseline, sleep, hyperventilation, intermittent photic stimulation (IPS), and cognitive tasks. To account for spontaneous fluctuations of the EDs we divided the baseline period into 5-min epochs and calculated the 95% confidence interval for the baseline EDs in each patient. Modulation was assumed when the number of EDs during any 5-min test period was outside the 95% confidence interval. Key Findings: Using the arbitrary method, our results were similar to previous publications: Cognitive tasks seemed to inhibit the EDs in 94% of the patients, and to provoke them in 22%. However, when the spontaneous fluctuations were accounted for, inhibition was found in only 29% of the patients and provocation in 18%. A nonspecific effect of any cognitive task seemed to account for the observed significant inhibition in two-thirds of the cases, but was observed in only one of the patients with significant provocation. Photoparoxysmal response was observed in 23% of the patients. When accounting for the spontaneous occurrence of EDs, IPS had provocative effect in 10% of the patients. Hyperventilation and sleep had provocative effect on EDs to an extent similar to the cognitive tasks (hyperventilation: 22%; sleep: 18%). The conventional provocation methods tended to be more efficient in patients who were not seizure free. Myoclonia were recorded most often during the cognitive tasks (10 patients). Significance: Spontaneous fluctuations of EDs account for most of the previously described inhibitory effect of the cognitive tasks. The provocative effect of the cognitive tasks is task-specific, whereas the inhibitory effect seems to be related to cognitive activation in general.

The purpose of this project was to design an algorithm for detection of tonic seizures based on surface electromyography signals from the deltoids. A successful algorithm has a future prospect of being implemented in a wearable device as part of an alarm system. This has already been done for generalized tonic-clonic seizures, and the hypothesis was that some of the same characteristics could be found for tonic seizures. The signals were pre-processed by a high-pass filter to remove low frequency noise such as movement artifacts. Several different features were investigated, including kurtosis, median frequency, zero crossing rate and approximate entropy. These features were used as input in the random forest classifier to decide if a data segment was from a seizure or not. The goal was to develop a generic algorithm for all tonic seizures, but better results were achieved when certain parameters were adapted specifically for each patient. With patient specific parameters the algorithm obtained a sensitivity of 100% for four of six patients with false detection rates between 0.08 and 7.90 per hour.

SUMMARYObjective: To investigate the characteristics of sustained muscle activation during convulsive epileptic and psychogenic nonepileptic seizures (PNES), as compared to voluntary muscle activation. The main goal was to find surface electromyography (EMG) features that can distinguish between convulsive epileptic seizures and convulsive PNES. Methods: In this case-control study, surface EMG was recorded from the deltoid muscles during long-term video-electroencephalography (EEG) monitoring in 25 patients and in 21 healthy controls. A total of 46 clinical episodes were recorded: 28 generalized tonic-clonic seizures (GTCS) from 14 patients with epilepsy, and 18 convulsive PNES from 12 patients (one patient had both GTCS and PNES). The healthy controls were simulating GTCS. To quantitatively characterize the signals we calculated the following parameters: root mean square (RMS) of the amplitude, median frequency (MF), coherence, and duration of the seizures, of the clonic EMG discharges, and of the silent periods between the cloni. Based on wavelet analysis, we distinguished between a low-frequency component (LF 2-8 Hz) and a high-frequency component (HF 64-256 Hz). Results: Duration of the seizure, and separation between the tonic and the clonic phases distinguished at group-level but not at individual level between convulsive PNES and GTCS. RMS, temporal dynamics of the HF/LF ratio, and the evolution of the silent periods differentiated between epileptic and nonepileptic convulsive seizures at the individual level. A combination between HF/LF ratio and RMS separated all PNES from the GTCS. A blinded review of the EMG features distinguished correctly between GTCS and convulsive PNES in all cases. The HF/LF ratio and the RMS of the PNES were smaller compared to the simulated seizures. Significance: In addition to providing insight into the mechanism of muscle activation during convulsive PNES, these results have diagnostic significance, at the individual level. Surface EMG features can accurately distinguish convulsive epileptic from nonepileptic psychogenic seizures, even in PNES cases without rhythmic clonic movements.