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Heart rate variability (HRV) from recorded electrocardiograms (ECG) is a well-known diagnostic method for the assessment of autonomic nervous function of the heart, which is widely used to predict clinically relevant outcomes in the critical care setting, to risk stratify patients, and predict outcomes such as mortality. The morphological variations in the ECG waveform and the high degree of heterogeneity in the QRS complex often make it difficult to identify R waves, which may preclude the accurate analysis for HRV. Photoplethysmographic (PPG) signal can provide information about both the cardiovascular and respiratory systems and have extremely high degree of correlation with ECG during cardiac cycle. In this paper, we developed robust algorithm for high-resolution inter-beat waveform extraction using combined ECG and PPG analysis, which is highly needed for accurate HRV estimation. The simulation results showed high performance for inter-beat waveform detection in different cases that identifies missing/extra peaks in the QRS detection algorithm.

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