Chi-Yuan Chang

Objective: The objective of this study was to determine whether missing individual doses of anti‐seizure medications (ASMs) elevate short‐term seizure risk in people with drug‐resistant epilepsy. Methods: In a prospective, community‐based cohort, adults with drug‐resistant epilepsy (≥ 3 seizures/month) or their caregivers recorded seizures and ASM intake with smartphone applications for 10 months each. Individual level analysis modeled the relationships between ASM adherence with seizure occurrence, as well as with a simplified seizure forecast via a 90‐day moving average (“Napkin method”). Group‐level analysis with a mixed‐effects model was performed to examine the relationship between ASM adherence and simplified forecasts, while controlling for differences in individual seizure frequency via random effects. Results: Twenty‐seven participants (median age = 29 years) contributed 7,853 person‐days. Individual analysis showed that only a small (n = 2) number of participants had a weak relationship between ASM adherence with seizure occurrence. Group‐level analysis showed that seizure occurrence was highly linked to the Napkin method, but not ASM adherence. Interpretation: Among individuals with frequent, drug‐resistant epilepsy, occasional missed ASM doses did not measurably raise immediate seizure risk. Whereas sustained nonadherence remains a clinical concern, clinicians may reassure patients that infrequent brief lapses are unlikely to trigger seizures acutely, yet they should continue emphasizing overall adherence for long‐term seizure control. ANN NEUROL 2026;99:1076–1082

Objective: Seizure forecasting using e‐diaries may help patients with seizures to organize their daily life. Until now, most methods were not rigorously tested against a strict standard. This study aims to assess whether the performance of various models for seizure forecasting using e‐diaries is better than the performance of a moving window average (a.k.a. the Napkin method, due to simplicity of calculation). Methods: We analyzed three cohorts from Seizure Tracker: a retrospective study and two prospective studies. E‐diaries and the type of seizures were extracted from the datasets. We implemented five machine learning models (Perceptron, 1D‐convolution, Multilayer Perceptron, Cycle, point‐process generalized linear model) and compared their performance at seizure forecasting against the Napkin forecast. The models predicted the probability of having at least one seizure in the next 24‐h period based on a 90‐day historical window. Model performances were evaluated by commonly used metrics (area under the precision–recall curve, area under the receiver operating characteristic curve, and Brier score). We considered a model to be clinically ineffective if it did not outperform the Napkin method across metrics and seizure frequencies. Results: A total of 5501 retrospective patients (3300 training, 1100 validation, and 1101 testing) and 36 prospective patients (21 from one cohort, 15 from the other) were included in the analysis. No model achieved significantly better performance than the Napkin method across metrics and frequencies. Significance: Clinically effective seizure forecasting (i.e., beyond the Napkin method) for 24‐h risk using e‐diaries alone may be infeasible with currently available techniques.

Artifact Subspace Reconstruction (ASR) is a machine learning technique widely used to remove non-brain signals (referred to as “artifacts”) from electroencephalograms (EEGs). The ASR algorithm can, however, be constrained by the limited memory available on portable devices. To address this challenge, we propose a Hardware-Oriented Memory-Limited Online ASR (HMO-ASR) algorithm. The proposed HMO-ASR algorithm consists of (1) two-level window-based preprocessing including PCA-based and z-score-based preprocessing to clean the data in each window, (2) iterative mean, standard deviation, and covariance update using a parallel algorithm to achieve window-based processing, and (3) early eigenvector matrix determination to save the computation. With the three schemes, the HMO-ASR method can be implemented on mobile devices, application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) with limited memory. The study results showed that the proposed HMO-ASR algorithm can achieve comparable performance to those obtained by the offline ASR algorithm with a 98.64% reduction in memory size. An FPGA implementation is used for silicon proof of the proposed HMO-ASR algorithm.

Objective: Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data. However, the effectiveness of ASR and the optimal choice of its parameter have not been systematically evaluated and reported, especially on actual EEG data. Methods: This study systematically evaluates ASR on twenty EEG recordings taken during simulated driving experiments. Independent component analysis (ICA) and an independent component classifier are applied to separate artifacts from brain signals to quantitatively assess the effectiveness of the ASR. Results: ASR removes more eye and muscle components than brain components. Moreover, even though some eye and muscle components retain after ASR cleaning, the power of their activities are reduced. Study results also showed that ASR cleaning improved the quality of a subsequent ICA decomposition. Conclusions: Empirical results show that the optimal ASR parameter is between 20 and 30, balancing between removing non-brain signals and retaining brain activities. Significance: With an appropriate choice of parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.