Physiological Integrity of the Dicrotic Notch as a Biomarker for Multi-Point Wrist Pulse Based Health Assessment

Abstract

Accurate and non-invasive assessment of vascular health remains a significant challenge, particularly in wrist pulse analysis where traditional methods rely on qualitative interpretation or global waveform descriptors. Although the dicrotic notch (DN) is known to reflect arterial wave reflection and vascular compliance, its systematic utilization as a quantitative biomarker for health classification remains limited, especially in multi-point pulse analysis frameworks. The aim of this study is to establish the dicrotic notch as a physiologically interpretable and reliable biomarker for distinguishing healthy and unhealthy wrist pulse states using synchronized multi-point measurements across Vatta, Pitta, and Kapha radial artery locations. The primary contribution of this work lies in the development of a multi-point DN-centric framework that integrates DN timing, inter-site timing gradients, and beat-to-beat variability to capture spatial and temporal dynamics of arterial wave reflection. Unlike conventional approaches, the proposed method emphasizes physiologically grounded feature extraction and interpretable classification. The methodology involves synchronized photoplethysmography (PPG) signal acquisition, followed by preprocessing, beat segmentation, and fiducial point detection. DN-based features, including timing, gradient and variability are extracted and analysed using non-parametric statistical methods. A deterministic rule-based classification framework is then employed to ensure transparent decision-making based on physiological thresholds. The results demonstrate significant differences between healthy and unhealthy subjects, with DN timing reduced by approximately 9-14%, inter-site timing gradients collapsing by up to 75%, and DN variability increasing by approximately 52% in pathological conditions (p < 0.05, η² up to 0.416). The proposed classification approach achieved an overall accuracy of 91.9%, with sensitivity of 86.7% and specificity of 92.9%. In conclusion, the dicrotic notch serves as a robust and mechanistically meaningful biomarker for vascular health assessment. The proposed multi-point DN-based framework enables accurate, interpretable, and non-invasive classification of pulse states, providing a strong foundation for future clinical and wearable diagnostic applications.