Key result
A classification model using 110 photoplethysmography-based features distinguished four autonomic nervous system activation patterns with an accuracy of 0.80, outperforming traditional heart rate variability.
Why the study?
Noninvasive autonomic nervous system assessment is important, but the accuracy of standard methods based on electrocardiogram-derived heart rate variability has long been questioned.
Does photoplethysmography (PPG) feature extraction improve the classification accuracy of autonomic nervous system activation patterns compared to traditional HRV in healthy young men?
Observational (n=32)
No
Does photoplethysmography (PPG) feature extraction improve the classification accuracy of autonomic nervous system activation patterns compared to traditional HRV in healthy young men?
Absolute Event Rate: 0.8% vs 0.56%
Machine learning models utilizing comprehensive PPG-based features significantly improve the classification accuracy of autonomic nervous system activation states compared to traditional ECG-derived HRV.
May advance autonomic phenotyping in research; leaves open clinical utility pending validation.
Noninvasive assessment of autonomic nervous system (ANS) activity is of great importance, but the accuracy of the method used, which is primarily based on electrocardiogram-derived heart rate variability (HRV), has long been suspected. We investigated the feasibility of photoplethysmography (PPG) in ANS evaluation. Data of 32 healthy young men under four different ANS activation patterns were recorded: baseline, slow deep breathing (parasympathetic activation), cold pressor test (peripheral sympathetic activation), and mental arithmetic test (cardiac sympathetic activation). We extracted 110 PPG-based features to construct classification models for the four ANS activation patterns. Using interpretable models based on random forest, the main PPG features related to ANS activation were obtained. Results showed that pulse rate variability (PRV) exhibited similar changes to HRV across the different experiments. The four ANS patterns could be better classified using more PPG-based features compared with using HRV or PRV features, for which the classification accuracies were 0.80, 0.56, and 0.57, respectively. Sensitive features of parasympathetic activation included features of nonlinear (sample entropy), frequency, and time domains of PRV. Sensitive features of sympathetic activation were features of the amplitude and frequency domain of PRV of the PPG derivatives. Subsequently, these sensitive PPG-based features were used to fit the improved HRV parameters. The fitting results were acceptable ( p < 0.01), which might provide a better method of evaluating ANS activity using PPG.
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Liu et al. (2021) conducted an observational in Healthy (n=32). Photoplethysmography (PPG) features vs. Heart rate variability (HRV) and pulse rate variability (PRV) was evaluated on Classification accuracy of four autonomic nervous system activation patterns. A classification model using 110 photoplethysmography-based features distinguished four autonomic nervous system activation patterns with an accuracy of 0.80, outperforming traditional heart rate variability.
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