Non-contact physiological signal acquisition and processing system for people with heart and cerebral vessels
Through the combination of micro-bending fiber sensors and signal processing modules, skin inflammation and sleep interference caused by contact devices are solved, and non-invasive and safe cardiac cycle detection is achieved, improving signal denoising accuracy and detection accuracy.
Patent Information
- Application Number
- CN202510810064.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-17
- Publication Date
- 2025-08-12
AI Technical Summary
Existing portable physiological signal acquisition devices are usually contact or semi-contact. Long-term wearing will cause skin inflammation in the wear area, and wearing at night will affect sleep, making it difficult to achieve non-invasive and safe daily health monitoring for people at high risk of cardiovascular and cerebrovascular diseases.
The micro-bending fiber sensor is used to obtain the cardiac impact signal without contact, and denoising and feature extraction are performed through the signal processing module, including the denoising unit and feature extraction unit. The improved harmony search algorithm is used to optimize the variational modal decomposition and adaptive algorithm to process the signals, suppress environmental noise and baseline drift, and extract cardiac cycle parameters.
It realizes contactless physiological signal acquisition, avoids skin inflammation, supports continuous monitoring at night, significantly improving signal denoising accuracy and accuracy of cardiac cycle detection.
Smart Images
Figure CN120458561A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of biomedical engineering and medical health technology, and more specifically, to a non-contact physiological signal acquisition and processing system for cardiovascular and cerebrovascular patients. Background Art
[0002] Existing portable physiological signal acquisition devices are usually contact or semi-contact devices. If worn for a long time, they often cause inflammation of the skin at the wearing site, and wearing them at night can easily affect sleep. For people at high risk of cardiovascular and cerebrovascular diseases, full physiological signal monitoring is crucial.
[0003] Considering the above reasons, how to obtain the cardiac impact signal without contact for cardiac cycle detection and achieve safe and non-invasive daily health monitoring is exactly the problem considered in this application. Summary of the Invention
[0004] In response to the shortcomings of existing technologies, a non-contact physiological signal acquisition and processing system for cardiovascular and cerebrovascular patients is provided, which can obtain cardiac shock signals without contact for cardiac cycle detection, thereby realizing safe and non-invasive daily health monitoring.
[0005] To achieve the above-mentioned purpose, the following technical solution is provided: a non-contact physiological signal acquisition and processing system for cardiovascular and cerebrovascular patients, comprising a physiological signal acquisition module for contactlessly acquiring a user's ballistocardiographic signal and a signal processing module for denoising and extracting features from the ballistocardiographic signal;
[0006] The physiological signal acquisition module includes a slightly bent optical fiber sensor, which includes a slightly bent optical fiber sensing head, and the slightly bent optical fiber sensing head is used to convert the cardiopulmonary vibration signal into a change in optical transmission loss;
[0007] The signal processing module includes a denoising unit for suppressing environmental noise and baseline drift in the signal and a feature extraction unit for obtaining cardiac cycle parameters.
[0008] Preferably, the micro-bend optical fiber sensor head includes a multi-mode optical fiber and a grid structure, and the grid structure is used to periodically change the bending state of the multi-mode optical fiber to respond to the cardiopulmonary vibration signal.
[0009] Preferably, the denoising units are executed sequentially, and the harmony search algorithm dynamically adjusts the modal decomposition number and penalty factor of the variational modal decomposition and selects effective modal components based on signal dispersion entropy and autocorrelation coefficient to reconstruct the signal.
[0010] Preferably, the step of dynamically adjusting the variational modal decomposition introduces a cosine similarity operator for balancing the local search and global search of the harmony memory library and a momentum adaptive operator for adjusting the mutation probability of the harmony vector and optimizing the parameter convergence speed.
[0011] Preferably, the feature extraction unit performs in sequence normalization processing on the denoised signal, positioning the preselected characteristic peak based on the normalized signal, screening the overlap with the preselected characteristic peak through envelope detection, and dynamically judging the screened characteristic peak and extracting cardiac cycle parameters.
[0012] This technical solution has the following beneficial effects:
[0013] 1. The use of slightly bent optical fiber sensors enables contactless physiological signal acquisition, completely avoiding the skin inflammation caused by long-term wear of traditional contact devices, significantly improving user comfort, enabling continuous nighttime monitoring, and reducing sleep disturbances.
[0014] 2. The harmony search algorithm dynamically adjusts the modal decomposition number and penalty factor of the variational mode decomposition and selects effective modal components for signal reconstruction based on the signal dispersion entropy and autocorrelation coefficient, overcoming the modal aliasing problem of traditional empirical mode decomposition (EMD), effectively suppressing environmental noise and baseline drift, significantly improving the signal denoising accuracy, and ensuring the reliability of cardiac cycle parameter extraction.
[0015] 3. Improve the accuracy of cardiac cycle detection by screening the overlap between envelope detection and pre-selected characteristic peaks, dynamically judging the filtered characteristic peaks and extracting cardiac cycle parameters. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 Schematic diagram of the structure of the slightly bent optical fiber physiological information sensor.
[0017] Figure 2 Schematic diagram of the principle of cardiac cycle detection method based on slightly bent optical fiber sensor.
[0018] Figure 3 Flowchart of the improved VMD algorithm under harmony search optimization.
[0019] Reference numerals: 1. multimode optical fiber; 2. grid structure. DETAILED DESCRIPTION
[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.
[0021] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in the present invention include direct and indirect connections (couplings) unless otherwise specified. In the description of the present invention, it should be understood that the orientations or positional relationships indicated by the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc. are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.
[0022] Reference Figure 1-3 As shown, the present invention develops a contactless physiological signal acquisition device that obtains cardiac impulse signals through a slightly bent optical fiber sensor to perform cardiac cycle detection, thereby realizing a safe and non-invasive daily health monitoring solution.
[0023] The structure of the slightly bent optical fiber sensor is as follows Figure 1 As shown in the figure, it consists of a multimode optical fiber 1, a grid structure 2, a silicone pad, a light source, a photodetector, and a signal processing circuit. The multimode optical fiber 1, grid structure 2, and silicone pad constitute a slightly bent optical fiber sensor head. When a weak cardiopulmonary vibration signal acts on the slightly bent optical fiber sensor head, the multimode optical fiber 1 undergoes periodic bending, converting some of its guided modes into radiation modes, which in turn results in light transmission loss. The relationship between the weak cardiopulmonary vibration signal ΔP and the change in light intensity transmittance ΔT is as follows:
[0024]
[0025] Among them, K is the proportional coefficient, K f is the mechanical constant of the bent optical fiber, A s is the cross-sectional area of the optical fiber, Y s is the Young's modulus of the optical fiber, l s is the length of the optical fiber acted on by the grid structure 2.
[0026] The cardiac cycle detection method based on micro-bend optical fiber sensing is as follows Figure 2 As shown in the figure, after acquiring weak heart and lung vibration signals using a slightly bent fiber optic sensor, the extraction of cardiac cycle parameters primarily consists of two key steps: BCG waveform extraction and BCG feature recognition. In the BCG waveform extraction step, adaptive harmonic search variational mode decomposition (ADMD) is used to suppress low-frequency trend terms in the acquired signal, achieving BCG signal denoising. In the BCG feature recognition step, BCG characteristic peaks are adaptively located using prior information such as amplitude characteristics and peak time intervals, effectively extracting parameters such as heart rate and cardiac cycle.
[0027] First, normalize the denoised signal data x(k) to obtain the signal sequence f(k), and then locate all local extreme values f in f(k). peak (k), and based on the characteristic that the peak value of J wave is higher than other peak values, the peaks with peak amplitude higher than the adjacent peaks are selected as the preselected sequence of J peaks. Then the envelope of the signal sequence f(k) is extracted by the Hilbert transform method, and the overlap degree of the signal envelope and the preselected J peaks is detected, and the preselected J peaks are further selected. The number, peak value and position of the obtained preselected J peak sequence are marked as N respectively. pre , Peaks pre (k) and PLoc pre (k). The influence of burr noise is eliminated by detecting the overlap between the signal envelope and the preselected J peak. Then, the preselected J peak is further screened according to the BCG amplitude characteristics. The second peak Peaks2(k) after each preselected J wave is selected, and its mean Th is calculated. The mean Th is used as the amplitude threshold to further screen the obtained preselected J waves, and the preselected J peaks that are less than the threshold Th are deleted to eliminate the influence of the noise peak. The new J peak preselected sequence is obtained, and its number, peak value and position are recorded as N respectively. Th , Peaks Th (k) and PLoc Th (k) Finally, based on the characteristics of the J wave peak time interval, we further screened and pre-selected J peaks. Based on the time characteristics of the human cardiac cycle (the human cardiac cycle is 0.43-1.49 seconds), we set a time threshold and performed adaptive false detection and missed detection judgment on the J peak pre-selected sequence. This resulted in a peak sequence of J peaks for subsequent cardiac cycle parameter extraction.
[0028] The collection of ECG signals and cardiac shock signals is easily interfered by environmental factors, and noise is also generated during the digital-to-analog conversion process, resulting in inaccurate monitoring data. In response to the above problems, the present invention removes environmental noise from the signal spectrum by studying signal denoising algorithms in complex scenarios. The variational mode decomposition (VMD) algorithm based on the heuristic search algorithm is studied to overcome the modal aliasing problem between the intrinsic mode function (IMF) components in the traditional empirical mode decomposition (EMD), and a heuristic search algorithm is used to optimize the modal decomposition number and penalty factor in VMD to obtain the best signal decomposition accuracy, thereby accurately selecting the noise component in the IMF component and removing it, and finally reconstructing the remaining effective components into the denoised signal. The specific execution process of the improved VMD algorithm is as follows. Figure 3 shown.
[0029] First, the harmonic memory is initialized, its size is determined, harmonic vectors are randomly generated, and a mapping relationship is established between the harmonic vector position dimension and the K level and penalty factor α in VMD to construct the variational modal model. The specific formula for the harmonic memory is as follows:
[0030]
[0031] Secondly, in order to eliminate baseline drift, the K-th modal component of VMD decomposition is subjected to EMD decomposition, and the low-frequency component modality of the signal after EMD decomposition is discarded to reconstruct the ECG signal. The reconstructed ECG signal is sent to step three for VMD decomposition again. In step three, it is necessary to first calculate the fitness of each harmony vector, and use the signal dispersion entropy of VMD as the fitness function of APSO. The autocorrelation coefficient of the threshold method is selected as the selection of useful IMF components. Finally, the useful IMF components are accumulated to calculate the dispersion entropy of each combination as the fitness value. The fitness function is a measure of the quality of the harmony vector. The smaller the fitness function, the closer the harmony vector is to the optimal solution. The specific formula is as follows:
[0032]
[0033] Where m is the signal dimension, c is the number of species, and p(i) is the number of each c m The relative frequency, x i is the dispersion pattern corresponding to the embedded signal. The random number is then used to determine whether to search the current optimal harmony vector from the memory bank or randomly generate a new variable. If the search is successful, the random number is used again to determine whether to mutate. After each cycle, the cosine similarity operator and momentum adaptive operator are used to modify the HMCR and PAR until a complete harmony vector is generated. The adjustment formulas for HMCR and PAR are as follows:
[0034]
[0035] Here, cosine-similarity represents the cosine similarity between the first i components of the newly generated harmony vector and the first i components of the optimal harmony vector in the current harmony memory, and θ represents the influence factor of the cosine similarity operator. Finally, when the termination condition is met and the iteration stops, the optimal harmony vector in the harmony memory is regarded as the optimal solution of the optimized variational model and is introduced into the denoising model with the decomposition layer K and the iteratively obtained penalty factor α.
[0036] In order to achieve nonlinear over-optimization and accelerate convergence, the present invention introduces the cosine similarity operator, the formula is as follows:
[0037]
[0038] If the cosine similarity between the currently generated harmony vector block and the same-position block of the optimal harmony vector shows a high value, the probability of selecting the decision variable at the same position as the optimal harmony vector is reduced to avoid the generated harmony vector being too similar to the optimal harmony vector, and to ensure the diversity of high-quality solutions in the memory bank as much as possible to balance local search and global search.
[0039] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0040] The above-described embodiments merely illustrate several embodiments of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.
Claims
1. A non-contact physiological signal acquisition and processing system for cardiovascular and cerebrovascular patients, characterized in that: It includes a physiological signal acquisition module for contactlessly acquiring a user's cardiac shock signal and a signal processing module for performing denoising and feature extraction on the cardiac shock signal; The physiological signal acquisition module includes a slightly bent optical fiber sensor, which includes a slightly bent optical fiber sensor head, and the slightly bent optical fiber sensor head is used to convert the cardiopulmonary vibration signal into a change in optical transmission loss; the signal processing module includes a denoising unit for suppressing environmental noise and baseline drift in the signal and a feature extraction unit for obtaining cardiac cycle parameters.
2. The non-contact physiological signal acquisition and processing system for cardiovascular and cerebrovascular patients according to claim 1 is characterized in that: The micro-bend optical fiber sensor head includes a multi-mode optical fiber and a grid structure. The grid structure is used to periodically change the bending state of the multi-mode optical fiber to respond to cardiopulmonary vibration signals.
3. The non-contact physiological signal acquisition and processing system for cardiovascular and cerebrovascular patients according to claim 2 is characterized in that: The denoising units are executed sequentially, and the harmony search algorithm dynamically adjusts the modal decomposition number and penalty factor of the variational modal decomposition and selects effective modal components based on signal dispersion entropy and autocorrelation coefficient to reconstruct the signal.
4. The non-contact physiological signal acquisition and processing system for cardiovascular and cerebrovascular patients according to claim 3 is characterized in that: The step of dynamically adjusting the variational modal decomposition introduces a cosine similarity operator for balancing the local search and global search of the harmony memory library and a momentum adaptive operator for adjusting the mutation probability of the harmony vector and optimizing the parameter convergence speed.
5. The non-contact physiological signal acquisition and processing system for cardiovascular and cerebrovascular patients according to any one of claims 1 to 4, characterized in that: The feature extraction unit performs normalization processing on the denoised signal, locates the preselected characteristic peak based on the normalized signal, screens the overlap with the preselected characteristic peak through envelope detection, and dynamically determines the screened characteristic peak and extracts cardiac cycle parameters.