FNIRS-based peripheral artery blood circulation abnormality detection system

Abstract

The invention discloses a peripheral artery blood circulation abnormity detection system based on FNIRS. The device comprises seven independent channels, and is equipped with a dual-wavelength LED emission module and a photoelectric signal detection module, so that the efficient light source efficiency and the signal quality are ensured. And a Teensy3.1 microcontroller is used for realizing signal synchronization and transmission, so that the accuracy and the real-time performance of data are ensured. By optimizing wavelength selection and light source driving circuit design, crosstalk is effectively reduced, and monitoring accuracy is improved. And meanwhile, the second-order gain amplification circuit and the signal synchronization circuit are adopted, so that the signal quality and stability are further improved. The system has the advantages of being small in size, low in cost and capable of monitoring physiological signals of multiple positions in real time, and a high-performance solution is provided for arteriosclerosis diagnosis. Through the signal processing and band-pass filtering technology based on the Beer-Lambert law and in combination with the machine learning technology, precise diagnosis and evaluation of peripheral artery diseases are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of clinical scientific research, and in particular to a peripheral arterial blood circulation abnormality detection system based on FNIRS. Background Art

[0002] Peripheral arterial blood circulation abnormalities usually have no obvious symptoms in the early stages, so it is difficult to detect and treat them in time. Traditional detection methods such as ultrasound and blood tests can often only detect abnormalities when the disease progresses to a certain extent, which cannot meet the needs of early diagnosis. FNIRS imaging, as a non-invasive detection method, has received widespread attention in the biomedical field due to its unique spectral characteristics. The absorption rate of near-infrared light by various elements in human tissue is different. Among them, HbO and Hb are the main substances that absorb light signals in human tissue. The FNIRS technology obtains the change parameters of HbO and Hb based on the different absorption rates of various elements in the tissue, and then reflects the characteristics of brain and human hemodynamics. In addition, the low-frequency oscillation (0.01-0.15Hz) in the peripheral blood oxygen signal is an effective vascular perfusion marker that can detect slow changes in hemodynamic parameters and vascular integrity. Its combination with FNIRS technology in the clinical field has gradually become a hot topic.

[0003] Because most of the FNIRS systems developed by domestic institutions are traditional frequency domain modulation systems, they are large in size, high in cost, and cannot simultaneously detect physiological change signals at multiple locations around the human body. In addition, FNIRS signals are interfered by many factors, such as motion artifacts, ambient light, etc. How to effectively remove these interferences and noise and improve the stability and reliability of the signal is a major challenge facing FNIRS technology. The amount of data generated by FNIRS technology is huge and complex. How to effectively process and analyze this data to extract useful information is an important challenge. The FNIRS system monitors two chromophores, oxygenated hemoglobin and deoxygenated hemoglobin, and their changes may produce crosstalk. Crosstalk is affected by wavelength and extinction coefficient errors.

[0004] Therefore, in response to this situation, our team has developed a low-power, portable, non-modulated continuous wave multi-channel concurrent FNIRS system. The system contains seven independent channels, equipped with a dual-wavelength LED emission module and a photoelectric signal detection module to ensure high light source efficiency and signal quality. At the same time, the second-order gain amplifier circuit and signal synchronization circuit are used to further improve the signal quality and stability. The Teensy3.1 microcontroller is used to achieve signal synchronization and transmission to ensure data accuracy and real-time performance. By optimizing the wavelength selection and light source drive circuit design, crosstalk is effectively reduced and monitoring accuracy is improved. Summary of the invention

[0005] The present invention proposes a peripheral arterial blood circulation abnormality detection system based on FNIRS. The technical problem to be solved is to provide a complete set of low-power, portable non-modulated continuous wave multi-channel concurrent FNIRS system, which can simultaneously detect the changes in blood oxygen saturation and LFO signals at multiple peripheral positions, so as to amplify and reduce noise of FNIRS signals; synchronize the acquisition of multi-channel signals, and transmit multi-channel signals in time-sharing;

[0006] In order to solve the above problems, the present invention adopts the following technical solutions:

[0007] A peripheral arterial blood circulation abnormality detection system based on FNIRS, which adopts a low-power main control chip, integrates a blood oxygen collection module and a light source driving module, adds a multi-channel signal transmission module and is equipped with a professional medical probe;

[0008] The programmable gain amplification technology is adopted, through the second-order gain amplification circuit, in the first-order amplification, the weak physiological signal is converted into a voltage signal and amplified through transimpedance amplification; in the second-order amplification, the DC noise signal is filtered out through 15 times gain differential amplification.

[0009] This system contains 7 near-infrared channels, and the communication between Teensy3.1 and each MOD-pulse board is realized through a serial bus. Each board can transmit 32 bytes of data. The transmission protocol uses queue data, which is affected by the filter and lasts for about 10ms. The MOD-pulse board address is set to 1 to 7, and the command consists of a single-byte address and an operation code. Common commands include self-test and start and stop sampling. The board only responds to address commands from 1 to 7. After receiving the command, it immediately feeds back data to Teensy3.1, which takes about 4ms. Teensy3.1 instructs each board in turn to form a time-sharing communication process.

[0010] Light source switch control and brightness adjustment control are indispensable conditions for determining the accuracy of data collected during the operation of this system. We need to adjust the light source intensity once before signal acquisition begins to maximize the dynamic range of signal detection for each channel and keep the LED light source output intensity constant during signal acquisition. This system uses an H-bridge circuit to drive the light source to ensure stable light intensity.

[0011] The MBLL algorithm is used to calculate the hemoglobin concentration, and programming is started with Python. When studying the hemodynamic parameters of the human body, the low-frequency oscillation signal is usually divided into two parts: 0.01-0.04Hz and 0.04-0.15Hz. This system extracts the low-frequency oscillation signal of 0.01-0.15Hz through software filtering, uses a zero-phase filter to avoid phase shift, and allows the filter parameters to be adjusted through the software panel.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] The system uses a low-power main control chip, integrates a blood oxygen collection module and a light source driver module, adds a multi-channel signal transmission module and is equipped with a professional medical probe, achieving low energy consumption, miniaturization and meeting medical research conditions.

[0014] A second-order gain amplifier circuit is designed to filter out DC noise while amplifying physiological signals. Each amplifier output adds ADC conversion, which can automatically adjust the light source drive current according to the size of the physiological signal, achieving rapid start-up of the light source and automatic control of output power;

[0015] Through the signal time-sharing transmission protocol design, multiple channels are controlled to be opened and closed at the same time, and the data is controlled to be transmitted in an orderly manner, and the time-sharing characteristics are maintained to prevent data transmission confusion;

[0016] An H-bridge circuit is used to drive the 660nm and 920nm LED light sources, and the domestic HSPD940-COB SiPD detector is selected to transmit and receive the light source, which effectively reduces crosstalk and improves monitoring accuracy.

[0017] Peripheral arterial sclerosis clinical data is used to extract low-frequency oscillation correlation and time delay features, so that the research is closely integrated with clinical practical applications. Then, random forest training is used to compare the training results with the patient's clinical imaging diagnosis, proving that the diagnosis and evaluation results of this system are true and reliable, providing a low-cost and high-performance solution for the clinical diagnosis of peripheral arterial sclerosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is the overall technical roadmap of the present invention;

[0019] Figure 2 It is the second-order gain amplification principle diagram of the present invention;

[0020] Figure 3 It is a functional structure diagram of the software system of the present invention;

[0021] Figure 4 It is a schematic diagram of the random forest algorithm model of the present invention. DETAILED DESCRIPTION

[0022] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0023] like Figure 1As shown in the figure, a technical roadmap of a peripheral arterial blood circulation abnormality detection system based on FNIRS is shown. The system includes three parts: hardware development, feature recognition, and software design. The hardware development part includes an optical signal transmission module and an optical signal detection module. The feature recognition part includes spectrum analysis and feature statistical analysis. The software design part includes a data processing module and a data display module.

[0024] The optical signal transmitting module is used to simultaneously transmit multi-channel light sources, and its hardware part contacts multiple parts of the patient's body;

[0025] The optical signal detection module is used to receive the optical signal and convert it into an electrical signal. It completes the signal amplification and filtering preprocessing through the second-order amplifier circuit and extracts the original optical signal carrying physiological information.

[0026] The spectrum analysis and characteristic statistical analysis parts are used to extract key features of the extracted original optical signal;

[0027] The data processing module is used to extract the low-frequency oscillation signal from the extracted key features;

[0028] The data display module is used to present data through the host computer and display interface;

[0029] like Figure 2 As shown, the second-order gain amplifier circuit includes modules such as a mutual impedance amplifier (OA0), a second-stage amplifier (OA1), a multiplexer, and an H-bridge driving circuit;

[0030] The mutual impedance amplifier (OA0) is used to amplify the voltage signal. The light signal is reflected and absorbed by the tissue and then collected by the photodetector, converted into a voltage signal and amplified by the mutual impedance amplifier (OA0). However, the signal output by the first-order amplifier includes motion noise signals and ambient light signals, resulting in the output signal containing two parts: DC and AC. Among them, the AC component contains information about changes in the physiological state of the human body, while the DC component is mainly motion noise and ambient light noise. Because the OA0 output signal is very sensitive to motion noise and ambient light noise, although the OA0 output signal contains physiological signals that can reflect the physiological state of the human body, this part of the signal needs to be filtered and cannot be used directly. The high threshold and low threshold of the OA0 output light intensity signal are 3800 and 2700 respectively. Before the experiment starts, the LED light source intensity needs to be adjusted to keep the OA0 output signal value within the target range to ensure that the output signal intensity is within the processable range of the next stage.

[0031] The second stage amplifier (OA1) is a differential amplifier, which is used to filter out the DC noise from OA0 and amplify the AC component. The input of OA1 is mainly divided into two parts, the first part is the output of OA0, and the second part is the DC offset of the DC tracker.

[0032] The multiplexer is used to make the light sources of the same wavelength in multiple channels emit simultaneously, that is, the light sources of different wavelengths in the same channel adopt time division multiplexing and work separately. Due to the long distance between the peripheral detection positions, when multiple channels trigger the light source at the same time, there will be no crosstalk between the channels. The working principle of CON-CW FNIRS uses a constant light intensity light source, and does not require light source modulation and demodulation, avoiding the increase in equipment volume and cost due to the modulation and demodulation circuit.

[0033] The H-bridge driving circuit is used to transmit and receive the light source. The present invention uses the H-bridge circuit to drive the 660nm and 920nm LED light sources, and uses the domestic HSPD940-COB SiPD detector to effectively reduce crosstalk and improve monitoring accuracy.

[0034] like Figure 3 As shown, the software system includes MOD-pulse signal acquisition module, second-order gain amplification module, filtering processing, down 16 times sampling program, MBLL conversion module and other parts;

[0035] MOD-pulse signal acquisition module, through which signal acquisition is realized;

[0036] Filtering processing, through the bandpass filter to complete the extraction of low-frequency oscillation signals in blood oxygen parameters, so as to more intuitively observe the changes in neural activity. The detected raw data is the outgoing light intensity signal;

[0037] The 16-fold sampling procedure can reduce the sampling frequency, which is much higher than the rate of change of hemodynamics, to a frequency close to the change of hemodynamic parameters, which is in line with biological characteristics. Then, the downsampled light intensity signal is converted into a hemoglobin signal by modifying the Beer-Lambert law, and the curve of HbO concentration change is displayed in real time on the python display interface;

[0038] MBLL conversion, this module uses the modified Lambert-Beer law (MBLL) to convert the light intensity signal into the Oxy-Hb and Deoxy-Hb concentration change signal;

[0039] like Figure 4 As shown, the random forest algorithm includes multiple decision trees, which are used to achieve the purpose of clinical automatic diagnosis and status evaluation through model training. The present invention uses the random forest algorithm to establish a peripheral arteriosclerosis diagnosis model, and each tree ultimately gives a prediction result. The integrated output of the random forest model determines the final diagnosis result through majority voting or average prediction of regression problems, thereby improving the diagnostic accuracy and clinical application value of the model.

[0040] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A peripheral arterial blood circulation abnormality detection system based on FNIRS, characterized in that: include: Seven independent channels, each channel contains a dual-wavelength LED emission module, which emits light sources with wavelengths of 660nm and 920nm respectively, and a photoelectric signal detection module for real-time monitoring of blood oxygen saturation changes and low-frequency oscillation (LFO) signals; A two-stage gain amplifier circuit design is adopted. The first stage converts weak physiological signals into voltage signals through transimpedance amplification and performs preliminary amplification. The second stage filters out DC noise signals through 15 times gain differential amplification. Teensy3.1 microcontroller, used for synchronous signal control and data transmission, communicates with each MOD-pulse board through a serial bus to achieve time-sharing transmission of multi-channel signals; The H-bridge circuit drives the LED light source; Domestic HSPD940-COBSiPD photoelectric detector to achieve signal capture; Signal synchronization circuit design ensures the independence and synchronization of each channel signal to avoid crosstalk; The MBLL algorithm is combined with Python programming to achieve accurate calculation of hemoglobin concentration, while software filtering technology extracts LFO signals of 0.01 to 0.15 Hz; The GSO method is combined with optimized assessment limit standards to construct LFO baseline standards for evaluating peripheral blood circulation and blood integrity.

2. The peripheral arterial blood circulation abnormality detection system based on FNIRS according to claim 1, characterized in that: The second-order gain amplifier circuit design increases ADC conversion at each amplifier output, automatically adjusts the light source driving current according to the size of the physiological signal, and realizes rapid startup of the light source and automatic control of the output power.

Images