Image-based non-contact measurements for pulse wave remote acquisition and monitoring have an important practical value in clinical use. Accurate pulse waves are a major prerequisite for measuring parameters of human physiology such as the heart rate, heart rate variability, blood oxygen concentration, and blood pressure. Based on the fact that the carotid artery is the closest observable artery to the human heart and contains a wealth of physiological information, vibrations of the epidermis caused by blood flow can be observed on the surface of the human body. In addition, the amplitude of random motion on the neck is much smaller than that on the human face. Accordingly, the signal source is set on the neck for better observations, less disturbance, and more up-to-date results. Under normal circumstances, the pulsation of the human carotid artery causes a small vibration that is visible to the naked eye and can be obtained by analyzing the vibration using conventional image and signal processing methods. However, in clinical practice, some patients have a relatively weak carotid pulse, and the existing statistical signal processing and time-frequency domain signal processing methods are inadequate for obtaining the desired signal. Thus, a new signal processing method is required for these types of situations.
Under the illumination of an 850 nm near-infrared light source, a near-infrared camera was used to continuously shoot the image sequence of the vibration of the neck skin. The final signal was obtained through a series of images and signal processing. The specific process is described as follows. First, the region of interest (ROI) was obtained using the inter-frame difference method. The original gray signal was then obtained by calculating the mean value of the ROI. Next, the original signal was normalized from the gray signal at an interval of 0 to 1. Finally, the desired pulse wave signal was acquired using bandpass filtering and the proposed multi-region dominant frequency enhancement (MRDFE) method. The MRDFE method is a joint algorithm that combines frequency domain processing and principal component analysis in two steps. In the first step, the signal obtained in each ROI was assigned the weight of the dominant frequency signal-to-noise ratio. In the second step, the signals in these ROI channels were evaluated by principal component analysis, and the feature vector corresponding to the first eigenvalue obtained was the final output signal. To further demonstrate the robustness of the algorithm, we established our own database, which contained 24 sets of weak pulse vibration image sequences. In dealing with these data, we compared our method with other existing algorithms based on four indicators: periodic integrity, periodic variation, tidal wave integrity, and repulse wave integrity.
The proposed MRDFE method can be used to obtain pulse waves with preserved feature points in a weak pulse situation (Fig. 4). To compare the MRDFE method with other conventional methods, a feature point recognition algorithm called the stepwise threshold descent method was used to detect feature points from the final signal obtained by each method. Our experimental results show that the proposed method performs much better than the other three conventional algorithms. Our method exhibits a more stable periodic state and retains approximately 70% of the tidal wave characteristics and more than 50% of the repulse wave characteristics (Table 1). Based on observations of the signals derived from the different methods (Fig. 7), the periodicity of the pulse wave obtained by our method is more obvious, and more feature points are preserved. The MRDFE method enhances the signal with a high signal-to-noise ratio and weakens the signal with a low signal-to-noise ratio through weight assignment, yielding satisfactory results.
This study presents a method for obtaining pulse wave signals under the condition of weak pulse vibrations of the carotid artery. With a near-infrared light source used for illumination, the image sequence of neck skin vibration was captured by a camera. Several ROIs were selected from the image sequence, and the initial signal was acquired using outlier processing and bandpass filtering. The pulse wave signal of the weak pulse vibration was then processed successfully using the MRDFE method. Compared with other signal processing methods, the analytical results show that the signal obtained by the MRDFE method is of higher quality, preserves a greater number of feature points, and provides better cycle integrity. Our analysis and experimental results show that the proposed method is superior in performance to the existing signal processing methods. Robust and reliable pulse wave signals can be obtained using this method and applied in further measurements of the heart rate, heart rate variability, blood oxygen, and even blood pressure. The MRDFE method adds considerable value to new signal processing for image-based non-contact pulse wave extraction.
