Introduction Photoplethysmography (PPG) is used as a surrogate of electrocardiograms (ECG) for heart rate variability (HRV) analysis or respiratory rate monitoring. PPG is a more convenient way to measure HRV than ECG at rest, since respiration could be a confounding factor in HRV evaluation. However, it remains unclear whether or not controlled breathing affects breath-volume and breathing rate when HRV and pulse rate variability (PRV) are measured in different situations. Consciously controlled breathing was performed to alter the autonomic nervous states of subjects caused by respiratory sinus arrhythmia (RSA). The aim of this study was to investigate the coherence between parameters derived from ECG and PPG on healthy subjects with/without controlled breathing. Method With 30 healthy volunteers, we measured their respiratory frequency and recorded their ECG and PPG signals during spontaneous breathing and controlled breathing, including natural paced breathing, rapid and deep breathing, slow and deep breathing, rapid and shallow breathing, and slow and shallow breathing. Results Obvious coherence was observed between pulse rate and heart rate in both spontaneous breathing and controlled breathing tasks. However, a comparison of PRV and HRV indices demonstrated significant differences during controlled breathing. The results based on time domain and nonlinear method analysis showed that the frequency-dependent changes have more of an impact. The results also indicated that breathing corresponded well in ECG-derived parameters comparing with PPG-derived ones. Conclusion We concluded that PPG-based devices cannot be applied as a precision screening tool to detect HRV, particularly during the cardiopulmonary analysis for the controlled breathing maneuver.

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Inspiratory flow limitation (IFL) is a critical symptom of sleep breathing disorders. A characteristic flattened flow-time curve indicates the presence of highest resistance flow limitation. This study involved investigating a real-time algorithm for detecting IFL during sleep. Three categories of inspiratory flow shape were collected from previous studies for use as a development set. Of these, 16 cases were labeled as non-IFL and 78 as IFL which were further categorized into minor level (20 cases) and severe level (58 cases) of obstruction. In this study, algorithms using polynomial functions were proposed for extracting the features of IFL. Methods using first- to third-order polynomial approximations were applied to calculate the fitting curve to obtain the mean absolute error. The proposed algorithm is described by the weighted third-order (w.3rd-order) polynomial function. For validation, a total of 1,093 inspiratory breaths were acquired as a test set. The accuracy levels of the classifications produced by the presented feature detection methods were analyzed, and the performance levels were compared using a misclassification cobweb. According to the results, the algorithm using the w.3rd-order polynomial approximation achieved an accuracy of 94.14% for IFL classification. We concluded that this algorithm achieved effective automatic IFL detection during sleep.