System for automatic heart rate calculation in epileptic seizures

Abstract

This article presents a comprehensive system for automatic heart rate (HR) detection. The system is robust and resistant to disturbances (noise, interferences, artifacts) occurring mainly during epileptic seizures. ECG signal filtration (IIR) and normalization due to skewness and standard deviation were used as preprocessing steps. A key element of the system is a reference QRS complex pattern calculated individually for each ECG recording. Next, a cross-correlation of the reference QRS pattern with short, normalized ECG windows is calculated and the maxima of the correlation are found (R-wave locations). Determination of the RR intervals makes possible calculation of heart rate changes and also heart rate variability (HRV). The algorithm was tested using a simulation in which a noise of an amplitude several times higher than ECG standard deviation levels was added. The proposed algorithm is characterized by high QRS detection accuracy, and high sensitivity and specificity. The algorithm proved to be useful in clinical practice, where it was used to automatically determine HR for ECG signals recorded before and during 58 focal seizures in 56 adult patients with intractable temporal lobe epilepsy.

Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review

“…A total of N = 9 papers were reviewed analyzing seizure detection systems built with wearable devices, facing challenges related to the online estimation of HRV. Three studies analyzed systems to extract heartbeats in people with epilepsy automatically, without providing results in terms of detection accuracy [106][107][108]. In three consecutive studies, Jeppesen designed and refined a wearable system for detecting seizures during the activities of daily living [91, 105,109].…”

Comparison of Different Machine Learning Algorithms to Classify Epilepsy Seizure from EEG Signals

“…Hence it would not be wise to use accuracy as a performance indicator. Therefore, the F1 score is used to evaluate the classification models [17]. The F1 score is a metric that represents the harmonic mean of precision and recall, providing a more balanced summary of the model's performance.…”