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1. Forecasting epileptic seizures with wearable devices: A hybrid short‐ and long‐horizon pseudo‐prospective approach

   https://doi.org/10.1111/epi.18466  

   Nasseri, Mona; Stirling, Rachel E; Viana, Pedro F; Cui, Jie; Nurse, Ewan; Karoly, Philippa J; Kremen, Vaclav; Dümpelmann, Matthias; Worrell, Gregory A; Freestone, Dean R; et al (May 2025, Epilepsia)

   Abstract ObjectiveSeizure unpredictability can be debilitating and dangerous for people with epilepsy. Accurate seizure forecasters could improve quality of life for those with epilepsy but must be practical for long‐term use. This study presents the first validation of a seizure‐forecasting system using ultra‐long‐term, non‐invasive wearable data. MethodsEleven participants with epilepsy were recruited for continuous monitoring, capturing heart rate and step count via wrist‐worn devices and seizures via electroencephalography (average recording duration of 337 days). Two hybrid models—combining machine learning and cycle‐based methods—were proposed to forecast seizures at both short (minutes) and long (up to 44 days) horizons. ResultsThe Seizure Warning System (SWS), designed for forecasting near‐term seizures, and the Seizure Risk System (SRS), designed for forecasting long‐term risk, both outperformed traditional models. In addition, the SRS reduced high‐risk time by 29% while increasing sensitivity by 11%. SignificanceThese improvements mark a significant advancement in making seizure forecasting more practical and effective. 

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2. Accuracy of Automated Machine Learning Software in Identifying EEGs with Prolonged Seizures

   Lin, Rebecca; Marquez, Destiny; Jacobson, Mercedes; Castaldi, Hannah; Buckman, Samuel; Shah, Vinit; Picone, Joseph (April 2020, Annual Meeting of the American Academy of Neurology (AAN))

   null (Ed.)

   Objective: To demonstrate that combining automatic processing of EEG data using high performance machine learning algorithms with manual review by expert annotators can quickly identify subjects with prolonged seizures. Background: Prolonged seizures are markers of seizure severity, risk of transformation into status epilepticus, and medical morbidity. Early recognition of prolonged seizures permits intervention and reduces morbidity. Design/Methods: We triaged the TUH EEG Corpus, an open source database of EEGs, by running a state-of-the-art hybrid LSTM-based deep learning system. Then, we postprocessed the output to identify high confidence hypotheses for seizures that were greater than three minutes in duration. Results: The triaging method selected 25 subjects for further review. 17 subjects had seizures; only 5 met criteria for seizures greater than 3 minutes. 11 subjects did not have a prior diagnosis of epilepsy. Among these, 63% had acute respiratory failure and 36% had cardiac arrest leading to seizures secondary to anoxic brain injury. 18 (72%) EEGs were obtained in long-term monitoring (LTM), 1 (4%) in the epilepsy monitoring unit (EMU), and 6 (24%) as a routine EEG (rEEG). 72.2% of seizures in LTM were identified correctly versus 66.7% in rEEGs. Of the 9 subjects who were deceased, 7 (78%) had been on LTM. The seizure detection algorithm misidentified seizures in 7 subjects (28%). A total of 22 (88%) subjects had some ictal pattern. Patterns mistaken for seizure activity included muscle artifact, generalized periodic discharges, generalized spike-and-wave, triphasic waves, and interestingly, an EEG recording captured during CPR. Conclusions: This hybrid approach, which combines state-of-the-art machine learning seizure detection software with human annotation, successfully identified prolonged seizures in 72% of subjects; 88% had ictal patterns. Prolonged seizures were more common in LTM subjects than the EMU and were associated with acute cardiac or pulmonary insult. 

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3. Applicability of Hyperdimensional Computing for Seizure Prediction Using LBP and PSD Features from iEEG

   https://doi.org/10.1109/MWSCAS57524.2023.10405889  

   Ge, Lulu; Parhi, Keshab K. (August 2023, Proc. of 2023 Midwest Symposium on Circuits and Systems)

   Hyperdimensional computing (HDC) has been assumed to be attractive for time-series classification. These classifiers are ideal for one or few-shot learning and require fewer resources. These classifiers have been demonstrated to be useful in seizure detection. This paper investigates subject-specific seizure prediction using HDC from intracranial elec-troencephalogram (iEEG) from the publicly available Kaggle dataset. In comparison to seizure detection (interictal vs. ictal), seizure prediction (interictal vs. preictal) is a more challenging problem. Two HDC-based encoding strategies are explored: local binary pattern (LBP) and power spectral density (PSD). The average performance of HDC classifiers using the two encoding approaches is computed using the leave-one-seizure-out cross-validation method. Experimental results show that the PSD method using a small number of features selected by the minimum redundancy maximum relevance (mRMR) achieves better seizure prediction performance than the LBP method on the trairring and validation data. 

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4. Seizure occurrence is linked to multiday cycles in diverse physiological signals

   https://doi.org/10.1111/epi.17607  

   Gregg, Nicholas M.; Pal Attia, Tal; Nasseri, Mona; Joseph, Boney; Karoly, Philippa; Cui, Jie; Stirling, Rachel E.; Viana, Pedro F.; Richner, Thomas J.; Nurse, Ewan S.; et al (April 2023, Epilepsia)

   Abstract ObjectiveThe factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. MethodsIn this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist‐worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter–Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. ResultsTen subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. SignificanceSeizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time‐varying approaches to epilepsy care. 

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5. SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

   https://doi.org/10.3390/bioengineering10080918  

   Al-Hussaini, Irfan; Mitchell, Cassie S. (August 2023, Bioengineering)

   This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy. 

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