Heart rate calculation from ensemble brain wave using wavelet and Teager-Kaiser energy operator

Area of Science:

• Biomedical Signal Processing
• Neuroscience
• Cardiology

Background:

• Electroencephalogram (EEG) signals are often contaminated by artifacts from various physiological sources, including electrocardiogram (ECG).
• Traditional methods for ECG artifact removal in EEG require synchronous ECG recordings, limiting practical applications.
• Accurate heart rate (HR) estimation from EEG is crucial for clinical diagnoses related to stress, fatigue, and sleep.

Purpose of the Study:

• To introduce an automated method for extracting ECG signals directly from single-channel EEG recordings.
• To enhance R-peak detection in ECG signals within EEG using wavelet and Teager-Kaiser energy operator.
• To enable accurate heart rate and R-R interval calculation for clinical diagnosis without a separate ECG.

Main Methods:

• Developed an automated algorithm utilizing wavelet transform for signal processing.
• Employed the Teager-Kaiser energy operator for precise R-peak enhancement and detection within the EEG signal.
• Calculated heart rate (HR) and mean R-R interval from the extracted ECG components.

Main Results:

• The proposed method achieved a mean error of 1.4% for heart rate calculation.
• A mean error of 1.7% was observed for the mean R-R interval estimation.
• Demonstrated successful ECG extraction from single-channel EEG, eliminating the need for synchronous ECG recordings.

Conclusions:

• The automated ECG extraction method from EEG is efficient and accurate for clinical diagnosis.
• This technique supports applications in stress analysis, fatigue assessment, and sleep stage classification.
• The method reduces reliance on additional equipment, making ECG analysis from EEG more accessible.