The cost-effectiveness of a real-time seizure detection application for people with epilepsy.

OBJECTIVES: Automated seizure detection modalities can increase safety among people with epilepsy (PWE) and reduce seizure-related anxiety. We evaluated the potential cost-effectiveness of a seizure detection mobile application for PWE in Singapore. METHODS: We used a Markov cohort model to estimate the expected changes to total costs and health outcomes from a decision to adopt the seizure detection application versus the current standard of care from the health provider perspective. The time horizon is ten years and cycle duration is one month. Parameter values were updated from national databases and published literature. As we do not know the application efficacy in reducing seizure-related injuries, a conservative estimate of 1% reduction was used. Probabilistic sensitivity analysis, scenario analyses, and value of information analysis were performed. RESULTS: At a willingness-to-pay of $45,000/ quality-adjusted life-years (QALY), the incremental cost-effectiveness ratio was $1,096/QALY, and the incremental net monetary benefit was $13,656. Probabilistic sensitivity analyses reported that the application had a 99.5% chance of being cost-effective. In a scenario analysis in which the reduction in risk of seizure-related injury was 20%, there was a 99.8% chance that the application was cost-effective. Value of information analysis revealed that health utilities was the most important parameter group contributing to model uncertainty. CONCLUSIONS: This early-stage modeling study reveals that the seizure detection application is likely to be cost-effective compared to current standard of care. Future prospective trials will be needed to demonstrate the real-world impact of the application. Changes in health-related quality of life should also be measured in future trials.

Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG.

Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse characteristics across patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG). First, we deploy a convolutional neural network with transformers and belief matching loss to detect seizures in single-channel EEG segments. Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments. At last, we apply post-processing filters to the segment-level outputs to determine seizures' start and end points in multi-channel EEGs. Finally, we introduce the minimum overlap evaluation scoring as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) dataset and evaluated it on five independent EEG datasets. We evaluate the systems with the following metrics: sensitivity (SEN), precision (PRE), and average and median false positive rate per hour (aFPR/h and mFPR/h). Across four adult scalp EEG and iEEG datasets, we obtained SEN of 0.617-1.00, PRE of 0.534-1.00, aFPR/h of 0.425-2.002, and mFPR/h of 0-1.003. The proposed seizure detector can detect seizures in adult EEGs and takes less than 15[Formula: see text]s for a 30[Formula: see text]min EEG. Hence, this system could aid clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment.