###### 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 \(h_{1}\) 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. de-noising algorithm; radial arterial pulse wave; multi-channel signals +
Footnote †: journal:  
Today, researchers employ tactile sensors to obtain multi-channel pulse wave signals. [PERSON] developed a pulse diagnosis instrument (PDI) with 12-channel (3 \(\times\) 4 channels) tactile sensors [8]. [PERSON] and his colleague designed an arterial palpation instrument with 24-channel (4 \(\times\) 6 channels) tactile sensors [9]. [PERSON] developed a wrist pulse acquisition device with 24-channel (5 \(\times\) 5 \(-\) 1 channels) tactile sensors [6]. These studies above used the tactile sensor systems developed by PPS (Pressure Profile Systems Inc., Los Angeles, CA, USA), which are recommended tools for biomedical signal acquisition with hundreds of peer-reviewed papers. A typical PPS sensor system has 12 or 24 sensing elements arranged on a near 1 cm\({}^{2}\) sensor tip, with over 4 cm conductive leads on a flexible substrate to the circuits (amplifier, filter, analog-to-digital converter, and so on), which can be used independently [10] or assembled on the robot finger [6]. Due to crosstalk and material properties of the sensing elements in the multi-channel sensor, more consequent background noises in 3 DPI may be generated or enlarged [11; 12; 13]. In addition, the sensor also has contact noise while measuring pulse waves. Furthermore, the relatively long conductive leads further disturb the signals before entering the amplifier and filter circuits, which is unavoidable due to the requirements for flexible use. Hence, these noises cannot be completely eliminated by the system. Aimed at reducing these noises of tactile sensors in pulse wave acquisition, researchers have proposed several de-noising methods. [PERSON] et al. used a wavelet transform algorithm to remove motion artefacts and background noise from pulse waves acquired by a 12-channel tactile sensor [14]. Other methods such as variational mode decomposition (VMD) were applied to 3 DPI de-noising and achieve a noticeable effect [15]. However, some of the noises are mixed with signals in the frequency domain and are, therefore, difficult to eliminate using these traditional methods. Principal component analysis (PCA) is a statistical method employed for data dimensionality reduction and de-noising [16]. The PCA method can extract the pulse characteristics for multiple pulse cycles in a specific channel. Previous studies have found that pulse signals between cycles in a short period have small nonlinearity [5; 16; 17; 18]. Hence, the PCA method can be used in pulse signal feature extraction and de-noising fields.