Using sEMG to identify seizure semiology of motor seizures.

PURPOSE: Accurate characterization and quantification of seizure types are critical for optimal pharmacotherapy in epilepsy patients. Technological advances have made it possible to continuously monitor physiological signals within or outside the hospital setting. This study tested the utility of single-channel surface-electromyography (sEMG) for characterization of motor epileptic seizure semiology. METHODS: Seventy-one subjects were prospectively enrolled where vEEG and sEMG were simultaneously recorded. Three epileptologists independently identified and classified seizure events with upper-extremity (UE) motor activity by reviewing vEEG, serving as a clinical standard. Surface EMG recorded during the events identified by the clinical standard were evaluated using automated classification methods and expert review by a second group of three independent epileptologists (blinded to the vEEG data). Surface EMG classification categories included: tonic-clonic (TC), tonic only, clonic only, or other motor seizures. Both automated and expert review of sEMG was compared to the clinical standard. RESULTS: Twenty subjects experienced 47 motor seizures. Automated sEMG event classification methods accurately classified 72 % (95 % CI [0.57, 0.84]) of events (15/18 TC seizures, 5/9 tonic seizures, 1/3 clonic seizures, and 13/17 other seizures). Three independent reviewers' majority-rule analysis of sEMG correctly classified 81 % (95 % CI [0.67, 0.91]) of events (16/18 TC seizures, 8/9 tonic seizures, 1/3 clonic seizures, and 13/17 other manifestations). CONCLUSIONS: Continuous monitoring of sEMG data provides an objective measure to evaluate motor seizure activity. Using sEMG from a wearable monitor recorded from the biceps, automated and expert review may be used to characterize the semiology of events with UE motor activity, particularly TC and tonic seizures.

Differentiation of Epileptic and Psychogenic Nonepileptic Seizures Using Single-Channel Surface Electromyography.

PURPOSE: Epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) are difficult to differentiate when based on a patient's self-reported symptoms. This study proposes review of objective data captured by a surface electromyography (sEMG) wearable device for classification of events as ES or PNES. This may help clinicians accurately identify ES and PNES. METHODS: Seventy-one subjects were prospectively enrolled across epilepsy monitoring units at VA Epilepsy Centers of Excellence. Subjects were concomitantly monitored using video EEG and a wearable sEMG epilepsy monitor, the Sensing Portable sEmg Analysis Characterization (SPEAC) System. Three epileptologists independently classified ES and PNES that contained upper extremity motor activity based on video EEG. The sEMG data from those events were automatically processed to provide a seizure score for event classification. After brief training (60 minutes), the sEMG data were reviewed by a separate group of four epileptologists to independently classify events as ES or PNES. RESULTS: According to video EEG review, 17 subjects experienced 34 events (15 ES and 19 PNES with upper extremity motor activity). The automated process correctly classified 87% of ES (positive predictive value = 88%, negative predictive value = 76%) and 79% of PNES, and the expert reviewers correctly classified 77% of ES (positive predictive value = 94%, negative predictive value = 84%) and 96% of PNES. The automated process and the expert reviewers correctly classified 100% of tonic-clonic seizures as ES, and 71 and 50%, respectively, of non-tonic-clonic ES. CONCLUSIONS: Automated and expert review, particularly in combination, of sEMG captured by a wearable seizure monitor (SPEAC System) may be able to differentiate ES (especially tonic-clonic) and PNES with upper extremity motor activity.

Ictal quantitative surface electromyography correlates with postictal EEG suppression.

OBJECTIVE: To test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment. METHODS: Quantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES. RESULTS: We found significant correlations between quantitative surface EMG parameters and the duration of PGES (p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%. CONCLUSIONS: Ictal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression.

Automated Processing of Single-Channel Surface Electromyography From Generalized Tonic-Clonic Seizures to Inform Semiology.

PURPOSE: Advances in surface electromyography (sEMG) monitoring allow for long-term data collection in a natural environment, giving objective information that may identify risk of sudden unexpected death in epilepsy and guide clinical decision-making. Generalized tonic-clonic seizure semiology, namely motor tonic and clonic phase duration, may be an important factor in determining the level of seizure control and risk of sudden unexpected death in epilepsy. This study demonstrates a quantitative analysis of sEMG collected with a dedicated wearable device. METHODS: During routine monitoring, 23 generalized tonic-clonic seizures from 19 subjects were simultaneously recorded with video-EEG and sEMG. A continuous wavelet-transform was used to determine the frequency components of sEMG recorded during generalized tonic-clonic seizures. An automated process, incorporating a variant of cross-validation, was created to identify ideal frequencies and magnitude ranges for tonic and clonic phases and determine phase durations. Phase durations determined using sEMG analysis were compared with phase durations determined by independent epileptologists' review of video-EEG. RESULTS: Cross-validation revealed that the optimal frequency bands for tonic and clonic phases are 150 to 270 Hz and 12 to 70 Hz, respectively. The average difference in phase duration calculated using the two methods for tonic and clonic phases and total seizure duration were -0.42 ± 4.94, -5.12 ± 9.68, and -5.11 ± 11.33 seconds, respectively (results presented are TsEMG - TvEEG, µ ± σ). CONCLUSIONS: The automated processing of sEMG presented here accurately identified durations of tonic, clonic, and total motor durations of generalized tonic-clonic seizures similar to durations identified by epileptologists' review of video-EEG.

Downregulation of KCNMB4 expression and changes in BK channel subtype in hippocampal granule neurons following seizure activity.

A major challenge is to understand maladaptive changes in ion channels that sets neurons on a course towards epilepsy development. Voltage- and calcium-activated K+ (BK) channels contribute to early spike timing in neurons, and studies indicate that the BK channel plays a pathological role in increasing excitability early after a seizure. Here, we have investigated changes in BK channels and their accessory ß4 subunit (KCNMB4) in dentate gyrus (DG) granule neurons of the hippocampus, key neurons that regulate excitability of the hippocampus circuit. Two days after pilocarpine-induced seizures, we found that the predominant effect is a downregulation of the ß4 accessory subunit mRNA. Consistent with reduced expression, single channel recording and pharmacology indicate a switch in the subtype of channels expressed; from iberiotoxin-resistant, type II BK channels (BK α/ß4) that have higher channel open probability and slow gating, to iberiotoxin-sensitive type I channels (BK α alone) with low open probability and faster gating. The switch to a majority of type I channel expression following seizure activity is correlated with a loss of BK channel function on spike threshold while maintaining the channel's contribution to increased early spike frequency. Using heterozygous ß4 knockout mice, we find reduced expression is sufficient to increase seizure sensitivity. We conclude that seizure-induced downregulation of KCNMB4 is an activity dependent mechanism that increases the excitability of DG neurons. These novel findings indicate that BK channel subtypes are not only defined by cell-specific expression, but can also be plastic depending on the recent history of neuronal excitability.

Detection of generalized tonic-clonic seizures using surface electromyographic monitoring.

OBJECTIVE: A prospective multicenter phase III trial was undertaken to evaluate the performance and tolerability in the epilepsy monitoring unit (EMU) of an investigational wearable surface electromyographic (sEMG) monitoring system for the detection of generalized tonic-clonic seizures (GTCSs). METHODS: One hundred ninety-nine patients with a history of GTCSs who were admitted to the EMU in 11 level IV epilepsy centers for clinically indicated video-electroencephalographic monitoring also received sEMG monitoring with a wearable device that was worn on the arm over the biceps muscle. All recorded sEMG data were processed at a central site using a previously developed detection algorithm. Detected GTCSs were compared to events verified by a majority of three expert reviewers. RESULTS: For all subjects, the detection algorithm detected 35 of 46 (76%, 95% confidence interval [CI] = 0.61-0.87) of the GTCSs, with a positive predictive value (PPV) of 0.03 and a mean false alarm rate (FAR) of 2.52 per 24 h. For data recorded while the device was placed over the midline of the biceps muscle, the system detected 29 of 29 GTCSs (100%, 95% CI = 0.88-1.00), with a detection delay averaging 7.70 s, a PPV of 6.2%, and a mean FAR of 1.44 per 24 h. Mild to moderate adverse events were reported in 28% (55 of 199) of subjects and led to study withdrawal in 9% (17 of 199). These adverse events consisted mostly of skin irritation caused by the electrode patch that resolved without treatment. No serious adverse events were reported. SIGNIFICANCE: Detection of GTCSs using an sEMG monitoring device on the biceps is feasible. Proper positioning of this device is important for accuracy, and for some patients, minimizing the number of false positives may be challenging.