Privacy-preserving Early Detection of Epileptic Seizures in Videos

In this work, we contribute towards the development of video-based epileptic seizure classification by introducing a novel framework (SETR-PKD), which could achieve privacy-preserved early detection of seizures in videos. Specifically, our framework has two significant components - (1) It is built upon optical flow features extracted from the video of a seizure, which encodes the seizure motion semiotics while preserving the privacy of the patient; (2) It utilizes a transformer based progressive knowledge distillation, where the knowledge is gradually distilled from networks trained on a longer portion of video samples to the ones which will operate on shorter portions. Thus, our proposed framework addresses the limitations of the current approaches which compromise the privacy of the patients by directly operating on the RGB video of a seizure as well as impede real-time detection of a seizure by utilizing the full video sample to make a prediction. Our SETR-PKD framework could detect tonic-clonic seizures (TCSs) in a privacy-preserving manner with an accuracy of 83.9% while they are only half-way into their progression. Our data and code is available at https://github.com/DevD1092/seizure-detectionComment: Accepted to MICCAI 202

Altered cardiac structure and function is related to seizure frequency in a rat model of chronic acquired temporal lobe epilepsy

Objective: This study aimed to prospectively examine cardiac structure and function in the kainic acid-induced post-status epilepticus (post-KA SE) model of chronic acquired temporal lobe epilepsy (TLE), specifically to examine for changes between the pre-epileptic, early epileptogenesis and the chronic epilepsy stages. We also aimed to examine whether any changes related to the seizure frequency in individual animals. Methods: Four hours of SE was induced in 9 male Wistar rats at 10 weeks of age, with 8 saline treated matched control rats. Echocardiography was performed prior to the induction of SE, two- and 10-weeks post-SE. Two weeks of continuous video-EEG and simultaneous ECG recordings were acquired for two weeks from 11 weeks post-KA SE. The video-EEG recordings were analyzed blindly to quantify the number and severity of spontaneous seizures, and the ECG recordings analyzed for measures of heart rate variability (HRV). PicroSirius red histology was performed to assess cardiac fibrosis, and intracellular Ca2+ levels and cell contractility were measured by microfluorimetry. Results: All 9 post-KA SE rats were demonstrated to have spontaneous recurrent seizures on the two-week video-EEG recording acquired from 11 weeks SE (seizure frequency ranging from 0.3 to 10.6 seizures/day with the seizure durations from 11 to 62 s), and none of the 8 control rats. Left ventricular wall thickness was thinner, left ventricular internal dimension was shorter, and ejection fraction was significantly decreased in chronically epileptic rats, and was negatively correlated to seizure frequency in individual rats. Diastolic dysfunction was evident in chronically epileptic rats by a decrease in mitral valve deceleration time and an increase in E/E` ratio. Measures of HRV were reduced in the chronically epileptic rats, indicating abnormalities of cardiac autonomic function. Cardiac fibrosis was significantly increased in epileptic rats, positively correlated to seizure frequency, and negatively correlated to ejection fraction. The cardiac fibrosis was not a consequence of direct effect of KA toxicity, as it was not seen in the 6/10 rats from separate cohort that received similar doses of KA but did not go into SE. Cardiomyocyte length, width, volume, and rate of cell lengthening and shortening were significantly reduced in epileptic rats. Significance: The results from this study demonstrate that chronic epilepsy in the post-KA SE rat model of TLE is associated with a progressive deterioration in cardiac structure and function, with a restrictive cardiomyopathy associated with myocardial fibrosis. Positive correlations between seizure frequency and the severity of the cardiac changes were identified. These results provide new insights into the pathophysiology of cardiac disease in chronic epilepsy, and may have relevance for the heterogeneous mechanisms that place these people at risk of sudden unexplained death

Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions

EEG datasets for seizure detection and prediction— A review

Abstract Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other, and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalizability, and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and preprocessing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalizable, and effective seizure detection and prediction algorithm

Cardiorespiratory and autonomic function in epileptic seizures : a video-EEG monitoring study

Purpose: Seizure-induced cardiorespiratory and autonomic dysfunction has long been recognized, and growing evidence points to its implication in sudden unexpected death in epilepsy (SUDEP). However, a comprehensive understanding of cardiorespiratory function in the preictal, ictal, and postictal periods are lacking. Methods: We examined continuous cardiorespiratory and autonomic function in 157 seizures (18 convulsive and 139 nonconvulsive) from 70 consecutive patients who had a seizure captured on concurrent video-encephalogram (EEG) monitoring and polysomnography between February 1, 2012 and May 31, 2017. Heart and respiratory rates, heart rate variability (HRV), and oxygen saturation were assessed across four distinct periods: baseline (120 s), preictal (60 s), ictal, and postictal (300 s). Heart and respiratory rates were further followed for up to 60 min after seizure termination to assess return to baseline. Results: Ictal tachycardia occurred during both convulsive and nonconvulsive seizures, but the maximum rate was higher for convulsive seizures (mean: 138.8 beats/min, 95% confidence interval (CI): 125.3–152.4) compared with nonconvulsive seizures (mean: 105.4 beats/min, 95% CI: 101.2–109.6; p b 0.001). Convulsive seizures were associated with a lower ictal minimum respiratory rate (mean: 0 breaths/min, 95% CI: 0–0) compared with nonconvulsive seizures (mean: 11.0 breaths/min, 95% CI: 9.5–12.6; p b 0.001). Ictal obstructive apnea was associated with convulsive compared with nonconvulsive seizures. The low-frequency (LF) power band of ictal HRV was higher among convulsive seizures than nonconvulsive seizures (ratio of means (ROM): 2.97, 95% CI: 1.34– 6.60; p = 0.008). Postictal tachycardia was substantially prolonged, characterized by a longer return to baseline for convulsive seizures (median: 60.0 min, interquartile range (IQR): 46.5–60.0) than nonconvulsive seizures (median: 0.26 min, IQR: 0.008–0.9; p b 0.001). For postictal hyperventilation, the return to baseline was longer in convulsive seizures (median: 25.3 min, IQR: 8.1–60) than nonconvulsive seizures (median: 1.0 min, IQR: 0.07–3.2; p b 0.001). The LF power band of postictal HRV was lower in convulsive seizures than nonconvulsive seizures (ROM: 0.33, 95% CI: 0.11–0.96; p = 0.043). Convulsive seizures with postictal generalized EEG suppression (PGES; n = 12) were associated with lower postictal heart and respiratory rate, and increased HRV, compared with those without (n = 6). Conclusions: Profound cardiorespiratory and autonomic dysfunction associated with convulsive seizures may explain why these seizures carry the greatest risk of SUDEP

Risk of sudden unexpected death in epilepsy (SUDEP) with lamotrigine and other sodium channel‐modulating antiseizure medications

Abstract Objective In vitro data prompted U.S Food and Drug Administration warnings that lamotrigine, a common sodium channel modulating anti‐seizure medication (NaM‐ASM), could increase the risk of sudden death in patients with structural or ischaemic cardiac disease, however, its implications for Sudden Unexpected Death in Epilepsy (SUDEP) are unclear. Methods This retrospective, nested case–control study identified 101 sudden unexpected death in epilepsy (SUDEP) cases and 199 living epilepsy controls from Epilepsy Monitoring Units (EMUs) in Australia and the USA. Differences in proportions of lamotrigine and NaM‐ASM use were compared between cases and controls at the time of admission, and survival analyses from the time of admission up to 16 years were conducted. Multivariable logistic regression and survival analyses compared each ASM subgroup adjusting for SUDEP risk factors. Results Proportions of cases and controls prescribed lamotrigine (P = 0.166), one NaM‐ASM (P = 0.80), or ≥2NaM‐ASMs (P = 0.447) at EMU admission were not significantly different. Patients taking lamotrigine (adjusted hazard ratio [aHR] = 0.56; P = 0.054), one NaM‐ASM (aHR = 0.8; P = 0.588) or ≥2 NaM‐ASMs (aHR = 0.49; P = 0.139) at EMU admission were not at increased SUDEP risk up to 16 years following admission. Active tonic–clonic seizures at EMU admission associated with >2‐fold SUDEP risk, irrespective of lamotrigine (aHR = 2.24; P = 0.031) or NaM‐ASM use (aHR = 2.25; P = 0.029). Sensitivity analyses accounting for incomplete ASM data at follow‐up suggest undetected changes to ASM use are unlikely to alter our results. Significance This study provides additional evidence that lamotrigine and other NaM‐ASMs are unlikely to be associated with an increased long‐term risk of SUDEP, up to 16 years post‐EMU admission

Association of short-term heart rate variability and sudden unexpected death in epilepsy

Background and Objectives We compared heart rate variability (HRV) in sudden unexpected death in epilepsy (SUDEP) cases and living epilepsy controls. Methods This international, multicenter, retrospective, nested case–control study examined patients admitted for video-EEG monitoring (VEM) between January 1, 2003, and December 31, 2014, and subsequently died of SUDEP. Time domain and frequency domain components were extracted from 5-minute interictal ECG recordings during sleep and wakefulness from SUDEP cases and controls. Results We identified 31 SUDEP cases and 56 controls. Normalized low-frequency power (LFP) during wakefulness was lower in SUDEP cases (median 42.5, interquartile range [IQR] 32.6–52.6) than epilepsy controls (55.5, IQR 40.7–68.9; p = 0.015, critical value = 0.025). In the multivariable model, normalized LFP was lower in SUDEP cases compared to controls (contrast −11.01, 95% confidence interval [CI] −20.29 to 1.73; p = 0.020, critical value = 0.025). There was a negative correlation between LFP and the latency to SUDEP, where each 1% incremental reduction in normalized LFP conferred a 2.7% decrease in the latency to SUDEP (95% CI 0.95–0.995; p = 0.017, critical value = 0.025). Increased survival duration from VEM to SUDEP was associated with higher normalized high-frequency power (HFP; p = 0.002, critical value = 0.025). The survival model with normalized LFP was associated with SUDEP (c statistic 0.66, 95% CI 0.55–0.77), which nonsignificantly increased with the addition of normalized HFP (c statistic 0.70, 95% CI 0.59–0.81; p = 0.209). Conclusions Reduced short-term LFP, which is a validated biomarker for sudden death, was associated with SUDEP. Increased HFP was associated with longer survival and may be cardioprotective in SUDEP. HRV quantification may help stratify individual SUDEP risk

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