A Frequency-domain Features Based Clustering Algorithm for Blood Pressure Estimation with Photoplethysmogram Signal

Numerous studies have been conducted on estimating blood pressure (BP) through photoplethysmogram (PPG) waveform. However, the PPG pulses are generated in the arterial vessel approximately every second, and the morphological features of the continuous pulses are similar. The presence of repeated pulses can lead to over-training in BP prediction. This study proposes a clustering algorithm that to classify PPG pulses into specific clusters using PPG frequency domain features. In this study, the PPG pulses are processed using fast Fourier transform. Subsequently, the PPG frequency, amplitude, phase, real part, and imaginary part of the first to fourth frequency components are extracted. In the proposed K-Means algorithm, a novel distance function is developed to assess the dissimilarity between two pulses. In addition, a cluster center merging framework is proposed to overcome the challenge of selecting the K-value. The experiment utilized in the Medical Information Mart for IntensiveCare II (MIMIC-II) dataset, all the pulses are clustered and merged into 17 clusters. The results demonstrate that the proposed clustering method successfully identifies distinct PPG pulse clusters corresponding to different BP ranges. This finding supports the notion that PPG pulse shape is correlated with BP and enhances the interpretability of BP estimation based on pulse wave analysis.