Table 3.

Utilization of algorithm-based medical devices/technology in management and prediction of epilepsy.

Main Method of Algorithm	System Function	Significant Findings	Refs.
Customizable multi-domain features in the seizure detection algorithm	Monitoring System	→ High detection accuracy with low dimension, which reduces computational complexities. → Customisable and optimisable for wearable device. Limitation: Only 5 datasets were used to test the algorithm, which may lower the reliability and validity of results.	[42]
Adaptive seizure prediction based on EEG synchronization	Prediction System	→ 84% sensitivity and 63% specificity to seizures are achieved. → Adaptive learning capabilities allowing improvements in performance over time. → Fast processing time allowing embedment into mobile devices. Limitation: Some false positives could be due to eye movement artifacts.	[43]
EEG rhythm decomposition using Jacobi polynomial transforms (JPTs) and linear discrimination analysis (LDA)	Prediction System	→ Processing chain for seizure detection yields a 96.25–100% accuracy → Able to discriminate between seizure-free, healthy and seizure conditions. Limitation: Computational time for processing chain is long.	[44]
1D convolution neural network	Prediction System	→ Able to learn a lot of features. → Better precision and accuracy compared to existing standard models. → 98.33% accuracy, may be useful for the development of automated systems. Limitation: Training accuracy and validation were found to be good after 20 epochs.	[45]

Note: EEG: Electro-encephalography; 1D: 1 dimension.