A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

(1) Sunset High School, Portland, Oregon

https://doi.org/10.59720/20-064
Cover photo for A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Epilepsy is a chronic brain disorder impacting more than 65 million people worldwide (1% of the population). Its primary symptoms, seizures, can occur without warning and can be deadly. Each year, over 100,000 patients die from Sudden Unexpected Death in Epilepsy (SUDEP). A reliable seizure warning system can help patients stay safe. This work presents a comprehensive, comparative analysis of three different signal processing algorithms for automated seizure/ictal detection. The methods perform feature extraction and seizure detection on scalp electroencephalogram (EEG) signals. The first optimized mathematical model, Approximate Entropy, performed statistical time domain analysis using a new sliding window protocol. The second algorithm performed seizure-specific spectral energy binning using the Fast Fourier Transform in the frequency domain. The third method applied signal decomposition to extract ictal features by implementing a time-frequency Discrete Wavelet Transform method. Each epileptic seizure detection algorithm was successfully validated using >75 hours of recordings from the Boston Children’s Hospital’s CHB-MIT scalp EEG clinical database. Results indicated that the Discrete Wavelet Transform algorithm performed the best, achieving a seizure detection sensitivity of 92% and a specificity of 98%. The experimental results show that the proposed methods can be effective for accurate automated seizure detection and monitoring in clinical care.

Download Full Article as PDF

This article has been tagged with: