Sleep monitoring system based on noninvasive brain oxygen saturation monitor

Abstract

The invention relates to the technical field of medical instruments, in particular to a sleep monitoring system based on a non-invasive brain oxygen saturation monitor, which comprises a monitoring system and is used for synchronously and dynamically monitoring local brain oxygen in real time, and the monitoring system comprises a non-invasive brain oxygen dynamic monitoring unit used for monitoring the brain blood oxygen saturation of a patient in real time by adopting a near infrared spectrum technology; the multi-parameter synchronous analysis unit is used for integrating the brain oxygen data, the electroencephalogram signals, the cerebral blood flow data and the respiratory event indexes; the intelligent risk assessment engine analyzes a brain oxygen change rule based on a deep learning algorithm, and predicts a brain function impairment risk caused by sleep breathing disorder; in the intelligent risk assessment engine, on the basis of a space-time convolutional neural network, brain oxygen time sequence data and spatial distribution characteristics are combined, and a brain hypoxia mode of the OSA patient is recognized; the method has the characteristic of realizing early warning of the brain injury related to sleep breathing disorder through noninvasive brain oxygen monitoring, multi-parameter fusion analysis and intelligent risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and particularly to a sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor. Background Art

[0002] Obstructive sleep apnea is a common sleep breathing disorder, manifested as repeated partial or complete obstruction of the upper airway during sleep, which can cause repeated nocturnal hypoxemia and sleep structure disorders, and then trigger various cardiovascular and cerebrovascular events, metabolic disorders, and cognitive dysfunction, including hypertension, coronary heart disease, Alzheimer's disease, diabetes, stroke, etc.;

[0003] Upon inquiry, the publication number: CN215959831U discloses a polysomnography monitoring device. In this technology, it is disclosed that "a polysomnography monitoring device includes at least one acquisition terminal and a multi-functional terminal. The multi-functional terminal includes at least one storage cavity for storing the acquisition terminal, and the acquisition terminal and the multi-functional terminal are communicatively connected; the acquisition terminal is used for acquiring and storing physiological data, and is also used for sending the physiological data to the multi-functional terminal; the multi-functional terminal is used for reading and displaying the physiological data" and other technical solutions, and has technical effects such as "the acquisition terminal and the multi-functional terminal are communicatively connected, making it simple to wear, and each acquisition terminal stores the acquired physiological data respectively, thus ensuring that the data will not be lost".

[0004] Currently, the sleep monitoring devices used clinically have significant functional limitations: they can only obtain peripheral blood oxygen saturation data through fingertip blood oxygen monitoring. This single indicator has a weak statistical correlation with the cognitive dysfunction caused by OSA and cannot provide effective guidance for clinical decision-making; at the same time, the apnea-hypopnea index (AHI) and tissue oxygen saturation monitoring data provided by existing devices can neither reflect the real-time oxygenation state changes of local brain tissues, nor have the ability to synchronously and dynamically monitor the oxygen distribution in multiple brain regions, and even more fail to achieve a systematic assessment of the brain tissue hypoxia load (including the duration, severity, and recovery ability of hypoxia), resulting in the inability to accurately evaluate the actual impact of sleep breathing disorders on brain function. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor, which has the characteristics of early warning of sleep apnea-related brain damage through non-invasive brain oxygen monitoring, multi-parameter fusion analysis, and intelligent risk assessment.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor, including a monitoring system for real-time synchronous dynamic monitoring of local cerebral oxygen, and the monitoring system includes:

[0007] A non-invasive brain oxygen dynamic monitoring unit for real-time monitoring of the blood oxygen saturation in a patient's brain using near-infrared spectroscopy technology;

[0008] A multi-parameter synchronous analysis unit for integrating brain oxygen data, electroencephalogram signals, cerebral blood flow data, and respiratory event indicators;

[0009] An intelligent risk assessment engine, based on a deep learning algorithm, analyzes the changing patterns of brain oxygen and predicts the risk of brain function damage caused by sleep apnea-hypopnea syndrome.

[0010] Preferably, in the intelligent risk assessment engine, based on a spatio-temporal convolutional neural network, combining brain oxygen time series data with spatial distribution characteristics, identifies the brain hypoxia patterns in patients with obstructive sleep apnea (OSA) and classifies them as:

[0011] Sudden hypoxia, with a rapid and short-term drop in brain oxygen, directly related to apnea;

[0012] Persistent hypoxia, with a long-term low brain oxygen level, related to cognitive function decline.

[0013] Preferably, the monitoring system further includes a dynamic hypoxic load assessment unit for analyzing the recovery rate and recovery time of blood oxygen saturation after a hypoxic event, establishing an assessment index for the hypoxia tolerance of brain tissue. The dynamic hypoxic load assessment unit includes:

[0014] A brain oxygen analysis module for calculating the hypoxic load index and the oxygen recovery capacity index of the brain;

[0015] An integrated generation module for generating a brain damage risk score by combining the oxygen recovery capacity index of the brain with the apnea-hypopnea index.

[0016] Preferably, the oxygen recovery capacity index in the brain oxygen analysis module refers to recording the time required to recover from the lowest blood oxygen point to the baseline level for each hypoxic event. The formula is: calculate the recovery rate RR of each event = (baseline blood oxygen value - lowest blood oxygen value) / recovery time, and statistically analyze the average recovery time and average recovery rate of all hypoxic events during the whole night's sleep as the ORCI value.

[0017] Preferably, in the integrated generation module, weight coefficients are set according to the severity grading of the apnea-hypopnea index AHI:

[0018] 5 ≤ AHI < 15 is mild, with a weight coefficient of 1.0;

[0019] 15 ≤ AHI < 30 is moderate, with a weight coefficient of 1.2;

[0020] AHI ≥ 30 is severe, with a weight coefficient of 1.5.

[0021] Preferably, the brain injury risk scoring formula in the comprehensive generation module is:

[0022]

[0023] where PBDRS is the brain injury risk score, is the average value of ORCI throughout the night, T ref is the recovery time, W AHI is the weight coefficient.

[0024] Preferably, the classification of the brain injury risk score in the comprehensive generation module is:

[0025] PBDRS < 50 indicates low risk;

[0026] 50 ≤ PBDRS < 80 indicates medium risk;

[0027] PBDRS ≥ 80 indicates high risk.

[0028] Preferably, the monitoring system further includes a flexible sensing unit, which adopts a wearable flexible cerebral oxygen sensor array and fits on multiple brain regions of the forehead and temporal lobe to achieve dynamic imaging of the whole-brain oxygen distribution.

[0029] Preferably, the flexible sensing unit includes:

[0030] A flexible substrate material, a flexible polymer substrate material with a Young's modulus less than 1 GPa and capable of fitting the curved surface of the human head;

[0031] Matrix-arranged sensors, each sensing unit including an LED light source, a proximal photodetector, and a distal photodetector;

[0032] An integrated signal processing circuit, a micro-signal processing circuit integrated on the substrate, for real-time processing of the original optical signal and conversion into a digital signal for output.

[0033] The present invention provides a sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor. Compared with the prior art, it has the following beneficial effects:

[0034] 1. Through the non-invasive near-infrared spectroscopy technology and the multi-parameter synchronous analysis unit, multi-modal data fusion of cerebral oxygen, electroencephalogram, cerebral blood flow, and respiratory events is achieved, and a comprehensive brain function evaluation system is constructed; based on the intelligent risk assessment engine of the spatio-temporal convolutional neural network, sudden and persistent hypoxia patterns can be accurately identified, and the association between different hypoxia types and brain function damage can be revealed; this intelligent multi-dimensional analysis breaks through the limitations of traditional single-parameter monitoring and provides an integrated solution for clinical practice from real-time monitoring to risk prediction, significantly improving the early diagnosis ability of brain injuries related to sleep disordered breathing.

[0035] 2. The dynamic hypoxic load assessment unit establishes an objective index of brain tissue hypoxia tolerance by quantifying the cerebral oxygen recovery rate and time, and generates a differential brain injury risk score in combination with the AHI grading weight coefficient. The clear risk grading (mild, moderate, high) standardizes the clinical decision-making process, which can not only avoid over-intervention in mild cases but also timely warn high-risk patients. Such a dynamic and hierarchical assessment system provides a scientific basis for the formulation of individualized treatment plans and optimizes the allocation of medical resources and patient prognosis management.

[0036] 3. The flexible sensing unit adopts a low-modulus polymer substrate and a matrix optical sensor design. While ensuring stable signal acquisition, it perfectly fits the head curve, taking into account wearing comfort and long-term monitoring reliability. Synchronous monitoring of multiple brain regions realizes dynamic imaging of the whole-brain oxygen distribution, and the improvement of spatial resolution provides richer data support for brain function research and disease management. This design combines non-invasiveness, comfort and high-precision detection, promoting the practical application process of wearable medical devices in the field of brain health monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a block diagram of the monitoring system of the present invention;

[0038] Figure 2 It is a block diagram of the dynamic hypoxic load assessment unit in the present invention;

[0039] Figure 3 It is a block diagram of the weight coefficient of the AHI severity grading of the ventilation index in the present invention;

[0040] Figure 4 It is a block diagram of the PBDRS grading of the brain injury risk score in the present invention;

[0041] Figure 5 It is a block diagram of the flexible sensing unit in the present invention.

[0042] In the figure: 1. Monitoring system; 11. Non-invasive cerebral oxygen dynamic monitoring unit; 12. Multi-parameter synchronous analysis unit; 13. Intelligent risk assessment engine; 14. Dynamic hypoxic load assessment unit; 141. Cerebral oxygen analysis module; 142. Comprehensive generation module; 15. Flexible sensing unit; 151. Flexible substrate material; 152. Matrix-arranged sensors; 153. Integrated signal processing circuit. DETAILED DESCRIPTION OF THE INVENTION

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0044] Please refer to Figure 1 - Figure 5 , the present invention provides a technical solution: a sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor, including a monitoring system 1 and used for real-time synchronous dynamic monitoring of local cerebral oxygen, and the monitoring system 1 includes:

[0045] A non-invasive cerebral oxygen dynamic monitoring unit 11, which is used to monitor the blood oxygen saturation of a patient's brain in real time by using near-infrared spectroscopy technology;

[0046] A multi-parameter synchronous analysis unit 12, which is used to integrate cerebral oxygen data, electroencephalogram signals, cerebral blood flow data and respiratory event indicators;

[0047] An intelligent risk assessment engine 13, based on a deep learning algorithm, analyzes the variation law of cerebral oxygen and predicts the risk of brain function damage caused by sleep apnea.

[0048] In this implementation scheme, the real-time synchronous monitoring of local cerebral oxygen saturation is realized through the non-invasive cerebral oxygen dynamic monitoring unit. Combining the multi-parameter synchronous analysis unit to integrate multi-modal data of cerebral oxygen, electroencephalogram, cerebral blood flow and respiratory events, an evaluation system that comprehensively reflects the cerebral function state is constructed. The intelligent risk assessment engine deeply mines the spatio-temporal variation law of cerebral oxygen based on a deep learning algorithm, and realizes the early risk prediction of brain function damage related to sleep apnea. This solution breaks through the limitations of traditional sleep monitoring through a non-invasive, multi-parameter and intelligent technical path. It can not only capture the subtle changes in the oxygenation state of brain tissue in real time, but also reveal the internal relationship between cerebral oxygen metabolism and respiratory events and electroencephalogram activities, providing a complete solution from physiological indicators to risk warning for clinical practice, and significantly improving the early recognition rate and intervention accuracy of brain damage related to sleep apnea.

[0049] Specifically, in the intelligent risk assessment engine 13, based on a spatio-temporal convolutional neural network, combining cerebral oxygen time series data with spatial distribution characteristics, the cerebral hypoxia patterns of OSA patients are identified and classified as:

[0050] Sudden hypoxia, a rapid and short-term decrease in cerebral oxygen, is directly related to apnea;

[0051] Persistent hypoxia, a long-term low level of cerebral oxygen, is related to cognitive function decline.

[0052] In this embodiment, through the spatio-temporal convolutional neural network in the intelligent risk assessment engine, accurate identification and classification of the brain hypoxia patterns of patients with obstructive sleep apnea (OSA) are achieved. By fusing the temporal dynamic changes and spatial distribution characteristics of cerebral oxygen signals, this engine can effectively distinguish between two typical patterns of sudden hypoxia and persistent hypoxia, not only revealing the direct association between apnea events and acute cerebral oxygen decline, but also clarifying the potential impact of long-term hypoxia on cognitive function. This multi-dimensional analysis based on deep learning significantly improves the accuracy of hypoxia pattern classification, providing an objective basis for clinical differentiation of acute hypoxia risk and chronic brain function damage, thus supporting doctors in formulating differentiated intervention strategies - immediate ventilation treatment for sudden hypoxia and long-term oxygen therapy and cognitive protection programs for persistent hypoxia, ultimately achieving a precise medical closed-loop from symptom management to etiological treatment.

[0053] Specifically, the monitoring system 1 further includes a dynamic hypoxic load assessment unit 14 for analyzing the recovery rate and recovery time of cerebral oxygen saturation after hypoxic events and establishing an assessment index for cerebral tissue hypoxia tolerance. The dynamic hypoxic load assessment unit 14 includes:

[0054] A cerebral oxygen analysis module 141 for calculating the hypoxic load index and the cerebral oxygen recovery ability index;

[0055] An integrated generation module 142 for generating a brain injury risk score by combining the cerebral oxygen recovery ability index and the apnea hypopnea index.

[0056] In this embodiment, a systematic analysis of the recovery rate and recovery time of cerebral oxygen saturation after hypoxic events is achieved, thus constructing an objective assessment index for cerebral tissue hypoxia tolerance. This unit accurately quantifies the hypoxic load and cerebral oxygen recovery ability through the cerebral oxygen analysis module, and integrates multi-dimensional parameters such as the apnea hypopnea index by means of the integrated generation module to generate a brain injury risk score, significantly improving the early warning ability for brain injuries related to sleep disordered breathing. This multi-dimensional and dynamic assessment system can not only accurately reflect the differences in the compensatory ability of cerebral tissue to hypoxic stress, but also provide complete decision-making support for clinical practice from pathological mechanisms to risk stratification, ultimately achieving the precise formulation of individualized treatment plans and the optimal selection of intervention timing.

[0057] Specifically, the cerebral oxygen recovery ability index in the cerebral oxygen analysis module 141 refers to the time required to recover from the lowest blood oxygen point to the baseline level for each hypoxic event. The formula is: calculate the recovery rate RR of each event = (baseline blood oxygen value - lowest blood oxygen value) / recovery time, and statistically calculate the average recovery time and average recovery rate of all hypoxic events during the whole night's sleep as the ORCI value.

[0058] In this embodiment, through the calculation of the Oxygen Recovery Capacity Index (ORCI), a dynamic quantitative assessment of the recovery process of each hypoxic event is achieved. By precisely capturing the time and rate of blood oxygen recovery from the lowest point to the baseline level, this index can objectively reflect the oxygen metabolism compensation ability of the patient's brain tissue, providing a more refined basis for clinical brain function assessment. This statistical method based on event analysis not only improves the early recognition ability of cerebral oxygen regulation disorders but also effectively differentiates hypoxic events of different severities, thereby assisting doctors in formulating more targeted intervention plans and optimizing the warning efficacy of brain injuries related to sleep disordered breathing.

[0059] Specifically, in the comprehensive generation module 142, weight coefficients are set according to the severity classification of the Apnea Hypopnea Index (AHI):

[0060] When 5 ≤ AHI < 15, it is mild, and the weight coefficient is 1.0;

[0061] When 15 ≤ AHI < 30, it is moderate, and the weight coefficient is 1.2;

[0062] When AHI ≥ 30, it is severe, and the weight coefficient is 1.5.

[0063] In this embodiment, by dynamically associating the severity classification of the Apnea Hypopnea Index (AHI) with weight coefficients, the brain injury risk score can more accurately reflect the potential risk levels of different patients with sleep disordered breathing. Through this differential weighting strategy, the risk recognition sensitivity of the scoring algorithm for moderate and severe patients is significantly improved, avoiding overwarning of mild patients while ensuring prominent warnings for high-risk cases, providing a more scientific decision-making basis for clinical graded intervention, thus optimizing the allocation of medical resources and improving the prognosis management effect of patients.

[0064] Specifically, the brain injury risk score formula in the comprehensive generation module 142 is:

[0065]

[0066] where PBDRS is the brain injury risk score, is the average value of ORCI throughout the night, T ref is the recovery time, and W AHI is the weight coefficient.

[0067] In this embodiment, by achieving a quantitative assessment of the brain injury risk and through the comprehensive calculation of multi-dimensional indicators, it can more comprehensively and objectively reflect the patient's cerebral oxygen metabolism status and recovery potential, thereby helping clinical doctors accurately identify potential risks and providing a scientific basis for the formulation of early intervention and personalized treatment plans, ultimately enhancing the accuracy and effectiveness of brain injury management.

[0068] Specifically, the grading of the brain injury risk score in the comprehensive generation module 142 is as follows:

[0069] When PBDRS < 50, it is a low risk;

[0070] When 50 ≤ PBDRS < 80, it is a medium risk;

[0071] When PBDRS ≥ 80, it is a high risk.

[0072] In this embodiment, by clearly grading the brain injury risk score (PBDRS) and dividing the risks into three levels: low, medium, and high, it helps clinical staff quickly and intuitively evaluate the potential danger degree of patients' brain injuries. This grading system not only improves the standardization and operability of risk warning, but also assists doctors in formulating differential intervention strategies, thereby optimizing the diagnosis and treatment efficiency, striving for more timely treatment opportunities for high-risk patients, and ultimately improving the prognosis management effect.

[0073] Specifically, the monitoring system 1 further includes a flexible sensing unit 15, which adopts a wearable flexible cerebral oxygen sensor array and is attached to multiple brain regions of the forehead and temporal lobe to realize dynamic imaging of the whole-brain oxygen distribution.

[0074] In this embodiment, through the wearable design of the flexible sensing unit and the use of a flexible cerebral oxygen sensor array closely attached to multiple brain regions such as the forehead and temporal lobe, dynamic non-invasive monitoring of the whole-brain oxygen distribution is achieved. This design not only improves the wearing comfort and adaptability for long-term use, but also optimizes the spatial resolution of cerebral oxygen imaging through multi-region synchronous detection, providing more comprehensive and accurate real-time data support for brain function evaluation, disease monitoring, and rehabilitation treatment.

[0075] Specifically, the flexible sensing unit 15 includes:

[0076] A flexible substrate material 151, a flexible polymer substrate material with a Young's modulus less than 1 GPa and capable of conforming to the curved surface of the human head;

[0077] Matrix-arranged sensors 152, each sensing unit including an LED light source, a proximal photodetector, and a distal photodetector;

[0078] An integrated signal processing circuit 153, a micro signal processing circuit integrated on the substrate, used to process the original optical signal in real time and convert it into a digital signal for output.

[0079] In this embodiment, by using a flexible polymer substrate material with a low Young's modulus, it can closely fit the curved surface of the human head, ensuring wearing comfort and signal acquisition stability; the matrix-arranged sensors integrate LED light sources and near and far-end photodetectors, combined with an integrated signal processing circuit, realizing the real-time processing and digital output of optical signals, thus significantly improving the reliability of signal detection and the system integration level, and being applicable to long-term and dynamic human physiological monitoring scenarios.

[0080] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0081] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A sleep monitoring system based on a non-invasive brain oxygen saturation monitor, characterized by: Comprising a monitoring system (1) and used for real-time synchronous dynamic monitoring of regional cerebral oxygen, the monitoring system (1) includes: A non-invasive cerebral oxygen dynamic monitoring unit (11), which is used to monitor the blood oxygen saturation of the patient's brain in real time by using near-infrared spectroscopy technology; A multi-parameter synchronous analysis unit (12), which is used to integrate cerebral oxygen data, electroencephalogram signals, cerebral blood flow data and respiratory event indicators; An intelligent risk assessment engine (13), which, based on deep learning algorithms, analyzes the laws of cerebral oxygen changes and predicts the risk of brain function damage caused by sleep apnea.

2. The sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor according to claim 1, characterized in that: In the intelligent risk assessment engine (13), based on a spatio-temporal convolutional neural network, combining cerebral oxygen time series data with spatial distribution characteristics, identifies the cerebral hypoxia patterns of OSA patients and classifies them into: Sudden hypoxia, a rapid and transient decrease in cerebral oxygen, which is directly related to apnea; Persistent hypoxia, a long-term low level of cerebral oxygen, which is related to cognitive function decline.

3. The sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor according to claim 1, characterized in that: The monitoring system (1) further includes a dynamic hypoxic load assessment unit (14) and is used to analyze the recovery rate and recovery time of cerebral oxygen saturation after hypoxic events, establish an assessment index for cerebral tissue hypoxia tolerance. The dynamic hypoxic load assessment unit (14) includes: A cerebral oxygen analysis module (141), which is used to calculate the hypoxic load index and the cerebral oxygen recovery ability index; A comprehensive generation module (142), which is used to generate a brain damage risk score by combining the cerebral oxygen recovery ability index with the apnea hypopnea index.

4. The sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor according to claim 3, wherein: The cerebral oxygen recovery ability index in the cerebral oxygen analysis module (141) refers to recording the time required to recover from the lowest blood oxygen point to the baseline level for each hypoxic event. The formula is: calculate the recovery rate RR of each event = (baseline blood oxygen value - lowest blood oxygen value) / recovery time, and statistically calculate the average recovery time and average recovery rate of all hypoxic events during the whole night's sleep as the ORCI value.

5. The sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor according to claim 3, characterized in that: In the comprehensive generation module (142), weight coefficients are set according to the severity grading of the apnea hypopnea index AHI: 5 ≤ AHI < 15 is mild, and the weight coefficient is 1.0; 15 ≤ AHI < 30 is moderate, and the weight coefficient is 1.2; AHI ≥ 30 is severe, and the weight coefficient is 1.

5.

6. The sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor according to claim 3, characterized in that: The formula for the brain damage risk score in the comprehensive generation module (142) is: Among them, PBDRS is the brain injury risk score, and ORCI avg is the average value of ORCI for the whole night, and T ref is the recovery time, and W AHI is the weight coefficient.

7. A sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor according to claim 6, characterized in that: The grading of the brain damage risk score in the comprehensive generation module (142) is: PBDRS < 50 is low risk; 50 ≤ PBDRS < 80 is medium risk; PBDRS ≥ 80 is high risk.

8. The sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor according to claim 1, characterized in that: The monitoring system (1) further includes a flexible sensing unit (15), which uses a wearable flexible cerebral oxygen sensor array and fits on multiple brain regions of the forehead and temporal lobe to realize dynamic imaging of the whole-brain oxygen distribution.

9. The sleep monitoring system based on a non-invasive cerebral oxygen saturation monitor according to claim 8, wherein: The flexible sensing unit (15) includes: A flexible substrate material (151), a flexible polymer substrate material, whose Young's modulus is less than 1 GPa and can fit the curved surface of the human head; A matrix-arranged sensor (152), each sensing unit contains an LED light source, a proximal photodetector and a distal photodetector; An integrated signal processing circuit (153), a micro-signal processing circuit integrated on the substrate, which is used to process the original optical signal in real time and convert it into a digital signal for output.

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