Abstract
Pulse wave analysis (PWA) has been widely used in the medical field. A novel multi-channel sensor is employed in arterial pulse acquisition and brings richer physiological information to PWA. However, the noise of this sensor is distributed in the main frequency band of the pulse signal, which seriously interferes with subsequent analyses and is difficult to eliminate by existing methods. This study proposes a cross-channel dynamic weighting robust principal component analysis algorithm. A channel-scaled factor technique is used to manipulate the weighting factors in the nuclear norm. This factor can adaptively adjust the weights among the channels according to the signal pattern of each channel, optimizing the feature extraction in multi-channel signals. A series of performance evaluations were conducted, and four well-known de-noising algorithms were used for comparison. The results reveal that the proposed algorithm achieved one of the best de-noising performances in the time and frequency domains. The mean of h1 in the amplitude relative error (ARE) was 23.4% smaller than for the WRPCA algorithm. Moreover, our algorithm could accelerate convergence and reduce the computational time complexity by approximately 34.6%. These results demonstrate the performance and efficiency of the algorithm. Meanwhile, the idea can be extended to other multi-channel physiological signal de-noising and feature extraction fields.
Highlights
Pulse waves (PW) are propagating waves generated by heart pulsation in blood circulation
Since 3DPI is a pulse signal with a spatial and temporal correlation of each channel [6], this study introduces a new de-noising algorithm called cross-channel DWRPCA, which produces a channel-scaled factor (CSF) technique to manipulate the weights of weighted robust principal component analysis (WRPCA)
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Summary
Pulse waves (PW) are propagating waves generated by heart pulsation in blood circulation. Pulse wave analysis (PWA) is one of the earliest vital analyses in modern medicine [1–3]. Some cardiac outputs (COs), such as arterial stiffness, can be measured and estimated by key physiological points in the arterial blood pressure (ABP) waveform, which has considerable clinical significance [4]. Multi-channel signal acquisition has been applied in complex physiological signal acquisition studies. The acquired signals are called three-dimensional pulse images (3DPIs) and are arranged in a matrix form. The 3DPI can provide multiple accurate temporal pulse waves [5] and provide three-dimensional spatial features of pulse waves. The shapes and trends of the pulse waves reflect more physiological information, such as arterial stiffness [6,7]
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