Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder.

Area of Science:

• Biomedical Engineering
• Clinical Neuroscience
• Cardiology

Background:

• Short-term heart rate variability (HRV) analysis shows promise for detecting epileptic seizures.
• Accurate R-peak detection is crucial for real-time HRV analysis in portable ECG devices.

Purpose of the Study:

• To modify an existing QRS-detection algorithm into a precise R-peak detection algorithm.
• To minimize jitter from Q and S peaks that can cause errors in short-term HRV analysis.
• To develop a robust tool for real-time HRV analysis in epilepsy patients for potential seizure detection.

Main Methods:

• Utilized a portable CE marked ePatch® heart monitor to record ECG data from 14 epilepsy patients.
• Employed a training set from 7 patients and tested the algorithm on 467.6 recording hours from the remaining 7 patients.
• Modified a QRS-detection algorithm to specifically target R-peaks, avoiding Q and S wave interference.

Main Results:

• The novel R-peak detection algorithm achieved high sensitivity (Se = 99.979%) and positive predictive value (P+ = 99.976%).
• Performance was comparable to a previously published QRS-detection algorithm on the same dataset.
• The algorithm demonstrated high sensitivity and specificity, suitable for real-time HRV analysis.

Conclusions:

• The developed R-peak detection algorithm is a suitable tool for robust, fast, real-time HRV analysis in epilepsy patients.
• This advancement facilitates the possibility of real-time seizure detection.
• The algorithm's precision addresses jitter issues, enhancing HRV analysis accuracy for epilepsy monitoring.