Key result
Adding spectral-phase parameters to PPG signals improves diagnostic model accuracy over traditional amplitude-time analysis.
Why the study?
The study was conducted to address the problem of increasing the efficiency of non-invasive express diagnostics of functional state based on photoplethysmographic signal analysis using machine learning.
Combining temporal and spectral characteristics of PPG signals improves the accuracy of machine learning models for medical diagnostics.
May refine PPG-based ML diagnostics; leaves open prospective validation before clinical use.
The problem of increasing the efficiency of non-invasive express diagnostics of the functional state of the organism based on the analysis of photoplethysmographic (PPG) signals using machine learning methods is considered.Based on the analysis of the morphological features of the pulse wave, which contain important information about hemodynamic processes, the feasibility of switching from the traditional analysis of the amplitude-time characteristics of the signal to a complex approach is justified, which includes additional extraction of spectral-phase parameters within individual cardiocycles. A method of signal preprocessing is proposed, which includes band-pass filtering, normalization and segmentation, as well as an approach to ``soft'' filtering, which allows preserving diagnostically significant features of the signal shape. The system of amplitude-time parameters (duration of anacrota, intervals between characteristic points, area under the curve, etc.) and spectral-phase characteristics obtained using Fourier series expansion is considered in detail.Particular attention is paid to the procedure for selecting informative features for machine learning models. A multi-step approach is proposed, which includes eliminating the dependence on heart rate, multicollinearity analysis, assessing the importance of features using the permutation importance method, and interpreting the results using the PCA and SHAP methods. It was found that the greatest contribution to the accuracy of the models is provided by spectral-phase parameters, in particular group delay and phase shifts of harmonics, which are directly related to the physiological properties of the vascular system.As a result, a set of features was formed that combines the temporal and spectral characteristics of the signal and provides improved classification and prediction quality. The results obtained confirm the prospects of using complex analysis of PPG signals in combination with machine learning methods for medical diagnostics and human condition monitoring.
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Мосійчук et al. (2026) studied Medical diagnostics and human condition monitoring. Complex analysis of PPG signals (amplitude-time and spectral-phase parameters) with machine learning vs. Traditional analysis of amplitude-time characteristics was evaluated on Classification and prediction quality (model accuracy). A complex approach combining amplitude-time and spectral-phase parameters of PPG signals, particularly group delay and phase shifts, improved classification and prediction quality for medical diagnostics.
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