A system for preventing suffocation risk during MECT surgery

Abstract

The present invention relates to a MECT intraoperative suffocation risk prevention system, the system includes a blood oxygen monitoring module for measuring the blood oxygen data of the patient's blood oxygen saturation; an image acquisition module for capturing and recording the image data of the patient's head and chest posture; a processing module, which is data-connected to the image acquisition module and the blood oxygen monitoring module, and evaluates the patient's suffocation risk based on the acquired image data and blood oxygen data, and formulates a corresponding control plan accordingly, wherein the processing module can be data-connected to the control module placed on the patient's head, so that the control module can execute the control plan and reduce the suffocation risk in the form of adjusting the patient's head posture. By adding image monitoring technology, the system of the present invention can formulate a multi-dimensional monitoring strategy, so as to provide more comprehensive information about the patient's ventilation and oxygenation status, and more accurately and timely reflect the patient's suffocation risk.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a system for preventing suffocation risks during MECT surgery. Background Art

[0002] Surve, R., Bansal, S., Sriganesh, K., Subbakrishna, D., Thirthalli, J., & Rao, G. et al., 2015, "Incidence and risk factors for oxygendesaturation during recovery from modified electroconvulsive therapy: A prospective observational study", Journal of Anaesthesiology, ClinicalPharmacology, 31, 99 – 103. In this retrospective study, the complications and morbidity of electroconvulsive therapy (ECT) were evaluated. The study retrospectively reviewed the complications that occurred during 612 ECT treatments with propofol anesthesia in 75 patients. The results showed that 51 patients (68%) had at least one complication during treatment; 12 of these complications were potentially life-threatening, including angina, aspiration pneumonia, bronchospasm, hypoxemia (SpO2 < 92%, FiO2 = 1), and hypoxemia caused by severe laryngospasm; 25 patients (33%) had confusion more than 2 hours after ECT; in 10 patients (13%), confusion recurred after several ECT treatments; 6 patients had traumatic complications, one of whom required surgery. This shows that ECT is not a low-risk procedure, especially the high incidence of respiratory complications, which may have been overlooked before. Therefore, for most patients, conventional medical measures may not be suitable for ETC treatment. This article particularly emphasizes the risk of suffocation caused by hypoxemia and laryngospasm during ECT treatment, which may pose a threat to patient safety.

[0003] Técoult, E., & Nathan, N. published the article "Morbidity in electroconvulsive therapy" in 2001, European Journal of Anaesthesiology, 18, 511-518. As a prospective observational study, the article aims to evaluate the incidence and risk factors of oxygen desaturation (apnea) during the recovery period after modified electroconvulsive therapy (MECT) or electroconvulsive therapy (ECT). According to the study, the overall incidence of oxygen desaturation in MECT was 29% (93 / 316 patients); the study found that seizure duration and BMI were significantly associated with oxygen desaturation after ECT; the incidence of oxygen desaturation in obese patients (BMI>30) was 64%, and that in non-obese patients was 27%; the incidence of oxygen desaturation in patients with seizures lasting more than 45 seconds was 37%, and that in patients with seizures lasting less than 45 seconds was 26%. In conclusion, this article demonstrates a high incidence of oxygen desaturation during recovery from MECT anesthesia and identifies obesity and seizure duration as independent predictors of this complication.

[0004] What is known in the field is that MECT is a procedure used to treat severe depression and other mental illnesses, and it is usually performed with the assistance of general anesthesia and muscle relaxants. In this case, the patient cannot breathe on his own, so mechanical ventilation (using a mask, laryngeal mask or endotracheal tube) is required to maintain proper oxygenation and carbon dioxide removal. However, in the case of a mask, laryngeal mask or endotracheal tube, the epileptic discharge caused by intraoperative electrical stimulation may temporarily interfere with the function of the respiratory center and produce the physiological phenomenon of respiratory depression. There is still a risk of intraoperative asphyxia, so doctors need to adjust the patient's position based on experience to deal with or relieve asphyxia. However, many doctors need to perform such operations in different hospitals, but the mechanical ventilation equipment provided by different hospitals is different from each other. It is unrealistic for doctors to accurately grasp the characteristics of various equipment and the relationship between the patient's BMI index and it. Therefore, even under mechanical ventilation, the risk of MECT asphyxia is still not low.

[0005] It is known in the field that during MECT treatment, propofol, as a short-acting systemic intravenous anesthetic with rapid onset, is used to make the patient lose consciousness, thereby eliminating negative emotions such as fear and anxiety. In order to reduce complications such as fractures and tongue injuries caused by strong muscle contractions during electroconvulsive therapy, succinylcholine, a skeletal muscle relaxant, is often used clinically. However, succinylcholine can cause relaxation of respiratory muscles, causing patients to quickly enter a state of apnea, which in turn causes hypoxia and carbon dioxide retention. Therefore, in clinical practice, positive pressure ventilation with oxygen supply is usually required in conjunction with an anesthesia mask. It is worth noting that anesthetic drugs can reduce the pressure of the lower esophageal sphincter, and positive pressure ventilation with a mask may cause some oxygen to enter the stomach and accumulate, thereby causing gastric distension and increased intragastric pressure. This will not only cause postoperative discomfort to the patient, but may also increase the risk of reflux aspiration during the perianesthetic period. In addition, during modified electroconvulsive therapy, the current passing through the glossopharynx will stimulate the glossopharyngeal vagus nerve, causing the patient to experience symptoms of nausea and vomiting, which further increases the risk of suffocation. At the same time, the convulsions caused by electric shock may cause the patient to produce excessive saliva or vomit. If not cleaned up in time, these secretions may block the airway and cause suffocation.

[0006] Patent applications for suffocation are rare in the prior art, and there are only technical solutions such as CN115381468A, which only disclose methods and systems such as electroconvulsive data sampling. Although such technical solutions can synchronously record and analyze the stimulation and brain function status during the entire process of electroconvulsive therapy, which helps to evaluate the treatment effect and potential adverse reactions, they still have limitations. The physiological signals collected by this system mainly focus on EEG signals, EMG signals, and ECG signals. Because these signal types are not indicative enough in representing certain symptoms, such as suffocation, the system cannot monitor and warn patients of possible suffocation symptoms in a timely manner. This limitation makes the system unable to prevent the risk of suffocation of patients during MECT treatment, or to take effective measures in time to deal with the risk of suffocation when it occurs.

[0007] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the invention

[0008] The prior art has already presented a technical solution for identifying the actual sleep status of patients by combining the negative correlation analysis between blood oxygen saturation and sleep breathing state. For example, the patent document with publication number CN103462597A discloses a monitoring device for preventing obstructive sleep apnea based on smartphone control, which includes a smartphone, an image detection and signal processing device, and a blood oximeter. The image detection and signal processing device includes a DSP-based image recognition and signal processing module, an RGB camera module, a storage module, and a wireless module. The recognition and signal processing module includes a heart rate signal analysis module, a snoring signal analysis module, and a respiratory frequency analysis module; the heart rate signal analysis module is connected to the RGB camera module and the storage module respectively, and it measures the sleep heart rate, breathing, and snoring in a non-contact manner, measures the blood oxygen saturation through the blood oximeter, and fits the equation according to the negative correlation between the blood oxygen saturation and the number of sleep apnea times, thereby improving the accuracy and effectiveness of the blood oximeter, systematically analyzing the signal changes to identify the sleep status, and preventing sleep apnea. However, this technical solution mainly relies on the image processing and analysis of smartphones to realize the awakening process of patients in daily life due to myocardial hypoxia caused by obstructed sleep, which is different from the technical solution for adjusting the patient's posture in real time during the treatment process to prevent the patient from suffocating during the MECT treatment process. Furthermore, this technical solution does not involve the technical content of executing the corresponding control scheme through the control module according to the patient's real-time breathing state to adjust the patient's head posture, and thus cannot timely control the patient's suffocation state to reduce the risk of suffocation.

[0009] In view of the deficiencies of the prior art, the present invention provides a system for preventing the risk of asphyxia during MECT surgery to solve at least part of the above-mentioned technical problems.

[0010] The MECT intraoperative suffocation risk prevention system of the present invention includes: a blood oxygen monitoring module for measuring the blood oxygen data of the patient's blood oxygen saturation; an image acquisition module for capturing and recording the image data of the patient's head and chest posture; a processing module, which is data-connected to the image acquisition module and the blood oxygen monitoring module, and evaluates the patient's suffocation risk based on the acquired image data and blood oxygen data, and formulates a corresponding control plan accordingly. The processing module can be data-connected to the control module placed on the patient's head, so that the control module can execute the control plan and reduce the suffocation risk in the form of adjusting the patient's head posture.

[0011] Unlike the prior art, the present invention can jointly evaluate the patient's suffocation risk through the blood oxygen data collected by the blood oxygen monitoring module and the image data of the patient's head and chest posture collected by the image acquisition module, and formulate a corresponding control scheme of the control module for reducing the suffocation risk according to the suffocation risk. Based on the above-mentioned distinguishing technical features, the problems to be solved by the present invention may include: how to accurately detect the risk of apnea and hypoxia caused by the relaxation of respiratory muscles in patients during MECT treatment and provide corresponding auxiliary control schemes. Specifically, in order to comprehensively monitor the respiratory state and identify the risk of suffocation early, the MECT intraoperative suffocation risk prevention system of the present invention adds image monitoring technology on the basis of monitoring the patient's blood oxygen saturation, and realizes multi-dimensional monitoring of the patient's suffocation risk by obtaining the patient's chest rise and fall during MECT surgery, as well as the shape of the neck and mandible. Combining the data of image monitoring and blood oxygen monitoring, more comprehensive respiratory status information can be provided to the processing module. Image monitoring can capture the subtle movements of the chest and mandible, which are important physiological signals that the blood oxygen meter cannot provide. Before the blood oxygen saturation decreases, the image data may have shown signs of dyspnea. Blood oxygen monitoring provides key information about the patient's oxygenation status, while image acquisition provides intuitive data about the patient's breathing pattern and body posture. Image monitoring is part of a multidimensional monitoring strategy. When integrated with other monitoring methods such as blood oxygen monitoring, this comprehensive approach can more comprehensively assess the patient's risk of suffocation. Therefore, through this multidimensional information fusion, the risk of suffocation can be determined more accurately, which can facilitate medical staff or control equipment to take timely countermeasures and avoid the occurrence of related complications. This multidimensional monitoring strategy can provide more comprehensive information about the patient's ventilation and oxygenation status, and reflect the patient's risk of suffocation more accurately and promptly.

[0012] According to a preferred embodiment, the image acquisition module has different acquisition perspectives and acquisition contents in different time stages, wherein the image acquisition module continuously acquires the patient's chest rise and fall and mandibular shape in the monitoring stage, and acquires the patient's head position and posture in the auxiliary stage.

[0013] Unlike the prior art, the image acquisition module of the present invention has different acquisition perspectives and contents at different time stages. For example, the patient's chest rise and fall and mandibular shape are continuously acquired during the monitoring stage, and the patient's head position and posture are acquired during the auxiliary stage. The parameters acquired by the image acquisition module include chest rise and fall frequency, chest movement amplitude, mandibular shape changes, and head height, etc. Based on the above-mentioned distinguishing technical features, the problems to be solved by the present invention may include: how to improve the accuracy of the patient's suffocation risk detection while improving the accuracy of the head auxiliary control scheme, so as to achieve an accurate assessment of the patient's suffocation risk, and timely and accurately adjust the patient's head posture to reduce the risk of suffocation. Specifically, the image acquisition module has no less than two image acquisition angles. On the one hand, periodically changing the acquisition angle during the monitoring stage can ensure that the image acquisition module can capture and record the patient's chest rise and fall and changes in mandibular morphology from at least the front and side perspectives, making the acquired image data more specific and complete; on the other hand, in the auxiliary stage, the image acquisition module can change the acquisition angle to confirm the current height and position of the patient's head from a clearer camera position, and it can send such information to the control module, thereby assisting in guiding the execution of the control plan to perform fine-tuning within an appropriate adjustable range, so as to adjust the patient's head to the optimal posture to reduce the risk of suffocation.

[0014] According to a preferred embodiment, the image data acquired by the image acquisition module in the monitoring phase includes chest rise and fall frequency, chest movement amplitude, and mandibular morphological changes, and the image data acquired in the auxiliary phase includes head height.

[0015] The frequency of chest rise and fall refers to the number of breaths a patient takes per minute, and is a direct indicator of respiratory rate. The amplitude of chest movement can reflect the range of chest expansion and contraction during the patient's respiratory cycle, and is used to assess the depth of breathing. Mandibular morphological changes refer to changes in the position of the mandible in different respiratory stages (such as inhalation and exhalation), as well as mandibular movements related to breathing, such as the extent to which the mandible drops during open-mouth breathing. The head height can, to a certain extent, reflect the degree to which the patient's head is currently lifted, thereby providing a reference for the control module to adjust the patient's head to a predetermined lift angle. These parameters comprehensively describe the patient's breathing pattern and provide the necessary basic information for further data analysis and processing by the processing module.

[0016] According to a preferred embodiment, the image acquisition module is configured as a plurality of cameras or a rotatable and / or movable camera to switch the image acquisition angles in the same or different time periods. Such a setting is intended to enable the image acquisition module to utilize multiple acquisition angles to adapt to different monitoring requirements.

[0017] According to a preferred embodiment, the control module can dynamically adjust the control scheme according to the head height information fed back by the image acquisition module to ensure that the patient's actual head posture is consistent with the preset target posture. Given that the shape of the head and neck of each patient is different when lying flat on the smart pillow, a fixed adjustment scheme may not be able to accurately place the patient in an ideal posture, which may have an adverse effect on the treatment effect. In the auxiliary stage, the image acquisition module continuously captures the real-time height data of the patient's head, which provides an accurate adjustment reference for the control module. Using this data, the control module can accurately determine the specific position and height of the smart pillow that needs to be adjusted, thereby achieving precise control of the patient's head posture. The control module optimizes the treatment effect by accurately adjusting the degree of expansion of different parts in the smart pillow to ensure that the patient's actual head posture is consistent with the preset target posture.

[0018] According to a preferred embodiment, the control module includes a shoulder pillow, a neck pillow and a head pillow that are interconnected to form a whole. The shoulder pillow, the neck pillow and the head pillow are each equipped with a plurality of inflatable columns and inflation holes and deflation holes. A connecting pipe is connected between the inflatable columns, and the inflation holes and deflation holes are arranged on different inflatable columns. The inflation holes can be connected to an external inflatable device to inflate the inflatable columns, and the deflation holes are used to discharge the gas in the inflatable columns. The shoulder pillow, the neck pillow and the head pillow all have multiple layers of inflatable columns to increase the adjustable height range allowed for each. The inflatable columns of the shoulder pillow, the neck pillow and the head pillow are connected by a connecting pipe, and the height of the shoulder pillow, the neck pillow and the head pillow can be adjusted according to the amount of inflation, thereby changing the head posture of the patient. The inflation holes have a larger caliber than the deflation holes, so that rapid inflation can be achieved to quickly fill the smart pillow, and the height can be fine-tuned by slowing down the deflation speed.

[0019] According to a preferred embodiment, the inflation hole and the deflation hole are equipped with a solenoid valve that can be controlled to open and close, and the control signal of the solenoid valve is sent by a controller located on the side of the smart pillow and capable of receiving the control scheme from the processing module. The controller can receive the control scheme from the processing module and quickly convert it into a corresponding solenoid valve control signal. This control scheme accurately sets the required inflation volume of the shoulder pillow, neck pillow and head pillow.

[0020] According to a preferred embodiment, the processing module can obtain the patient's basic information and pre-evaluate the patient's asphyxia factor based on this information. The asphyxia factor can use the patient's previous MECT stimulation-related records, diseases that affect the patient's tolerance to electrical stimulation, and drugs that affect the nervous system's response as evaluation factors.

[0021] The processing module of the present invention can be connected to the HIS (hospital information system) in the hospital for data connection, so as to realize the pre-assessment of the asphyxia factor. Different from the assessment of the patient's asphyxia risk by the processing module, its assessment of the asphyxia factor is different at least in the time sequence and the method of assessment. The assessment of the asphyxia factor refers to the identification and analysis of factors that may cause individuals to experience asphyxia symptoms under specific conditions, which may include individual physiological characteristics, existing medical conditions, drug use, and environmental factors. These factors can be known before the MECT treatment, so the assessment time can be before the MECT treatment. The assessment of the risk of asphyxia refers to further analysis based on the identified asphyxia factors to determine the probability of an asphyxia event when an individual exhibits a specific condition (such as decreased chest fluctuation and decreased blood oxygen saturation) during the MECT treatment, so the assessment time is after the asphyxia factor. Together, the two constitute the basis for individual asphyxia risk management.

[0022] According to a preferred embodiment, the processing module can formulate different image acquisition schemes for the monitoring phase of the image acquisition module according to the evaluation results of the asphyxia factor, wherein the different image acquisition schemes differ in at least one of the start-up duration, monitoring frequency, rotation speed and angle.

[0023] Unlike the prior art, the processing module of the present invention can pre-evaluate the patient's asphyxia factor and adjust different image acquisition schemes based on the patient's basic information. Based on the above-mentioned distinguishing technical features, the problems to be solved by the present invention may include: how to adjust the image acquisition scheme according to the individual differences of the patient to improve the accuracy of the patient's asphyxia risk analysis results. Specifically, the processing module of the present invention can formulate a personalized image acquisition scheme for the monitoring stage of the image acquisition module according to the evaluation results of the asphyxia factor. In view of the causal relationship between nausea, vomiting and asphyxia, and these symptoms can be reflected in the chest rise and fall and the changes in the mandibular morphology, the processing module optimizes the image clarity acquired by the image acquisition module, aiming to effectively identify the early symptoms of asphyxia when they are not obvious. When the image acquisition module is configured with multiple cameras, the system will automatically adjust the startup time and monitoring frequency of the camera according to the patient's asphyxia risk level to ensure that the slight changes in the chest and mandible can be captured from multiple angles. In the case of a single rotatable or movable camera, the system will automatically adjust the speed of camera rotation and the frequency of angle change based on the patient's risk level, thereby maximizing the quality and relevance of the image data. This personalized program can effectively improve the level of monitoring of patients' potential suffocation risks.

[0024] According to a preferred embodiment, the processing module can be connected to the data of the mechanical ventilation device so as to coordinately perform adaptive adjustment of the ventilation volume and ventilation frequency when the control module adjusts the posture of the patient's head.

[0025] This dynamic adjustment mechanism of ventilation volume not only considers the amount of gas inhaled in a single time to adapt to the patient's oxygen needs, but also focuses on the adjustment of ventilation frequency to ensure that the patient's number of breaths per minute can match their physiological needs, avoiding the risk of suffocation caused by fixed ventilation settings. Therefore, the present invention achieves close coordination between the smart pillow and the mechanical assisted ventilation device through real-time monitoring and adaptive adjustment, providing patients with safer and more effective respiratory support. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a hardware topology diagram of the MECT intraoperative asphyxia risk prevention system provided by the present invention;

[0027] Figure 2 is an example block diagram of a system for preventing the risk of asphyxia during MECT surgery provided by the present invention;

[0028] Figure 3 It is a schematic diagram of the overall structure of the control module from one perspective provided by the present invention;

[0029] Figure 4 is a schematic diagram of the overall structure of the control module provided by the present invention from another perspective;

[0030] Figure 5 is a cross-sectional view of the front view of the control module provided by the present invention;

[0031] Figure 6 is a cross-sectional view of a control module provided by the present invention from a side perspective;

[0032] Figure 7 It is a schematic diagram of the connection of the inflatable column of the control module provided by the present invention;

[0033] Figure 8 is a curve chart of the change of the suffocation risk index of a certain patient over time provided by the present invention;

[0034] Fig. 9 It is a schematic diagram of the system provided by the present invention adjusting the corresponding preset angle when using a type A laryngeal mask during surgery in a patient with a BMI in the range of 25 to 30;

[0035] Fig.10 It is a schematic diagram of the system provided by the present invention adjusting the corresponding preset angle when using a type A mask during surgery for a patient with a BMI in the range of 25 to 30;

[0036] Fig.11A schematic diagram of the system provided by the present invention adjusting the corresponding preset angle when using a B-type laryngeal mask during surgery in a patient with a BMI in the range of 25 to 30;

[0037] Fig.12 A schematic diagram of the system provided by the present invention adjusting the corresponding preset angle when a B-type mask is used during surgery for a patient with a BMI in the range of 25 to 30;

[0038] Fig.13 A schematic diagram of the system provided by the present invention adjusting the corresponding preset angle when using a C-type laryngeal mask during surgery in a patient with a BMI in the range of 25 to 30;

[0039] Fig.14 The system provided by the present invention is a schematic diagram of adjusting the corresponding preset angle when a C-type mask is used during surgery for a patient with a BMI in the range of 25 to 30.

[0040] Reference numerals list

[0041] 100: blood oxygen monitoring module; 200: image acquisition module; 300: processing module; 400: control module; 410: shoulder pillow; 420: neck pillow; 430: head pillow; 440: inflatable column; 450: connecting tube; 460: inflation hole; 470: deflation hole; 480: controller; 500: mechanical ventilation equipment; 600: type A laryngeal mask; 610: type B laryngeal mask; 620: type C laryngeal mask; 700: type A mask; 710: type B mask; 720: type C mask. DETAILED DESCRIPTION

[0042] The following is a detailed description with reference to the accompanying drawings.

[0043] Example 1

[0044] In MECT, epileptiform discharges induced by electrical stimulation are achieved by delivering an electric current of a certain intensity to the brain, with the purpose of stimulating neurons, which changes the neurotransmitter levels in the brain by inducing a brief whole-brain discharge activity. However, this whole-brain discharge activity may also affect the respiratory control center of the brain, because breathing is controlled by a network of neurons located in the medulla oblongata and pons, an area called the respiratory center. Epileptiform discharges induced by electrical stimulation may temporarily interfere with the function of these respiratory centers, causing respiratory arrest. This is a physiological phenomenon called respiratory depression, which usually occurs immediately after electrical stimulation and usually lasts for a short time, ranging from a few seconds to tens of seconds. In most cases, the patient's breathing will resume naturally after the end of electrical stimulation. In addition, aspiration of oral secretions during MECT treatment can also cause respiratory arrest. Although respiratory arrest is usually short-lived, it still needs to be closely monitored and managed during MECT to prevent the occurrence of hypoxia or other related complications.

[0045] To this end, the present invention proposes a MECT intraoperative suffocation risk prevention system, such as Figure 1 , Figure 2 As shown, it includes a blood oxygen monitoring module 100 for measuring the blood oxygen data of the patient's blood oxygen saturation; an image acquisition module 200 for capturing and recording the image data of the patient's head and chest posture; and a processing module 300, which is data-connected to the image acquisition module 200 and the blood oxygen monitoring module 100. The processing module can evaluate the suffocation risk of the current patient based on the image data and blood oxygen data obtained from the image acquisition module 200 and the blood oxygen monitoring module 100, wherein the suffocation risk is associated with the first moment when the image data and the blood oxygen data are obtained. The processing module 300 determines the control scheme according to the suffocation risk of the current patient at the first moment, wherein the processing module 300 can be data-connected with the control module 400 placed on the head of the corresponding patient to send the control scheme to the control module 400 in the form of data instructions, so that the control module 400 executes the control scheme corresponding to the first moment condition of the corresponding patient, wherein the control scheme at least reduces the suffocation risk of the corresponding patient in the form of adjusting the head posture of the patient.

[0046] In order to comprehensively monitor the respiratory state and identify the risk of suffocation early, the MECT intraoperative suffocation risk prevention system of the present invention adds image monitoring technology on the basis of monitoring the patient's blood oxygen saturation, and realizes multi-dimensional monitoring of the patient's suffocation risk by obtaining the patient's chest rise and fall during the MECT operation, as well as the shape of the neck and mandible. In the MECT operation, the processing module 300 of the system according to the present invention determines that the current patient has a risk of apnea exceeding a predetermined threshold (for example, due to aspiration of oral secretions) after analyzing the chest posture image data captured by the image acquisition module 200 and / or the blood oxygen data provided by the blood oxygen monitoring module 100, and executes a control scheme for changing the head posture through the control module 400. This multi-dimensional monitoring strategy can provide more comprehensive information about the patient's ventilation and oxygenation status, more accurately and timely reflect the patient's suffocation risk, and improve the effectiveness of the control scheme executed by the control module 400.

[0047] Preferably, in the system of the present invention for MECT surgery, the processing module 300 formulates a corresponding control scheme based on the ventilation mode currently adopted by the patient obtained (for example, from HIS or other similar data sources) and in combination with the image data and / or blood oxygen data obtained, wherein when the patient's risk of suffocation exceeds the aforementioned threshold, in the case of laryngeal mask ventilation, the control scheme is executed to make the patient's head tilt back from the neutral position to a first preset angle to facilitate suction and ventilation; in the case of mask ventilation, the control scheme is executed to make the patient's head turn to one side from the neutral position at a second preset angle to facilitate suction and / or ventilation. The neutral position is a standardized positioning of the patient's head relative to the trunk, which means that the patient's head is neither bent forward or backward, nor deflected left or right, and is maintained in an upright position so that the external auditory canal and the lower edge are on the same horizontal line, that is, the auditory orbital line is parallel to the bed surface or the ground. This position is intended to maintain the natural alignment of the airway to optimize airway patency and reduce the risk of related complications. The offset generated when leaning back refers to the angle of the patient's head moving backward along the sagittal plane from the neutral position; while the offset when turning sideways refers to the angle of the patient's head moving to one side along the horizontal plane from the neutral position. And because the adjustment of the head has multiple degrees of freedom, the patient's head can have a certain amount of backward offset and lateral offset at the same time without exceeding the physiological limit, and the two head offsets do not interfere with each other within a reasonable range.

[0048] Preferably, when the processing module 300 formulates a corresponding control scheme according to the current patient ventilation mode obtained by it and the image data and blood oxygen data at the first moment obtained by the image acquisition module 200 and the blood oxygen monitoring module 100, since the processing module 300 formulates the control scheme for one of the two ventilation modes respectively, and the dynamic parameters involved in the control scheme are also determined in association with the two ventilation modes respectively, its data processing amount is extremely small, and there is no need to adopt complex computing mechanisms such as artificial intelligence algorithms, and it can complete the task of formulating multiple control schemes for multiple patients in real time. In particular, it can be implemented in the form of a simple lookup table.

[0049] Preferably, the first preset angle is determined based on the selected laryngeal mask type (such as Unique TM, endoscopic laryngeal mask or ProSeal TM, which will be referred to as Type A laryngeal mask 600, Type B laryngeal mask 610 and Type C laryngeal mask 620 respectively) and the current patient's BMI index, so as to determine a moderate reclining angle according to the body shape under anesthesia, thereby reducing airway resistance and improving ventilation effect. The reason why the BMI index is selected for joint evaluation is that for obese and pregnant patients, an additional lifting angle is required to reduce airway resistance during endotracheal intubation ventilation.

[0050] Preferably, the second preset angle is determined based on the BMI index of the patient of the selected mask type (such as a full-face mask, a nasal mask, and a mouth mask, hereinafter referred to as type A mask 700, type B mask 710, and type C mask 720, respectively), so as to determine an appropriate deflection angle according to the body shape under anesthesia, thereby reducing airway resistance and improving ventilation effect.

[0051] Preferably, the laryngeal mask type, the mask type and the patient's BMI index have a non-negligible influence on the airway seal and ventilation quality when the head is in a neutral position. Therefore, a lookup table is formed by summarizing an empirical table of a series of data determined by actual work, and the processing module 300 searches the server (for example, from the HIS) according to the laryngeal mask type, the mask type and the patient's BMI index to determine the first preset angle or the second preset angle that is closest to the previously manually determined situation from the lookup table to form a control plan for the current patient. Since the lookup table can be stored in a server (such as the HIS), it only takes a short time to complete the formulation or confirmation of multiple control plans with high timeliness requirements for multiple patients. The lookup table itself can be shared with multiple hospitals for further use and expansion, and a standardized training set can also be formed for the development of related artificial intelligence products in the future.

[0052]

[0053] Combined with the above table, Figure 9~Figure 14 The following is a schematic diagram showing the preset angles corresponding to three types of laryngeal masks or face masks used by patients with a BMI of 25 to 30 during MECT.

[0054] In clinical practice, in order to ensure airway patency and optimize ventilation effect, it is very important to calculate the preset angle of patient head movement. The present invention takes into account factors such as laryngeal mask type, mask type and patient's BMI index, and constructs a mathematical model to calculate the first preset angle A1 (laryngeal mask ventilation) and the second preset angle A2 (mask ventilation) by the following formula.

[0055] The calculation formulas of the first preset angle A1 and the second preset angle A2 can be expressed as:

[0056]

[0057]

[0058] These formulas can be used to calculate the preset angles of head movement to optimize ventilation based on the patient's BMI and the type of laryngeal mask and face mask used. In these formulas:

[0059] A1 represents the first preset angle, which is suitable for laryngeal mask ventilation; A2 represents the second preset angle, which is suitable for face mask ventilation.

[0060] α represents the basic deviation angle of laryngeal mask ventilation. According to clinical practice, when the patient lies normally, in order to ensure airway patency, the initial angle is usually selected to be smaller, which is set as the basic deviation of forward and backward tilt. Considering the needs of laryngeal mask ventilation, the initial forward and backward tilt angle α is set to 0~10°.

[0061] β represents the basic deviation angle of mask ventilation. Based on clinical experience, the left-right deviation angle of the patient's head should be small during mask ventilation to ensure the stability and tightness of the mask. According to clinical observations, the appropriate basic left-right deviation angle β is 0~5°.

[0062] C L (T L ) represents the influence coefficient of the laryngeal mask type. Different types of laryngeal mask designs and material properties can affect the airflow dynamics in the airway. For example, some laryngeal masks can improve ventilation by optimizing the shape of the airway to reduce the resistance to airflow. The specific degree of influence can be quantified by experimentally comparing the changes in airway pressure of different types of laryngeal masks under the same ventilation conditions. For example, an airway simulator or animal model can be used to measure the airway pressure and flow when different laryngeal masks are used under the same ventilation conditions, and the influence coefficient can be analyzed using fluid dynamics principles (such as the Bernoulli equation).

[0063] C M( T M ) represents the influence coefficient of the mask type. The design of the mask type (such as shape, fit, material, etc.) has a significant impact on the distribution of airflow and pressure loss. For example, the design of a full-face mask may be more in line with the shape of the face and can distribute pressure more evenly, thereby reducing air leakage, while a nasal mask may cause higher local pressure and airflow resistance due to a smaller coverage area. The fit of different masks in different positions will directly affect the ventilation effect. Similarly, by comparing the airway pressure and flow of different masks under standardized ventilation conditions, pressure sensors and flow meters are used to collect data, and fluid dynamics models are used for analysis (Bernoulli's equation can also be used) to determine the influence coefficient of each type of mask.

[0064] When airflow passes through a laryngeal mask or face mask, it will be affected by pressure and flow. The following is the influence coefficient C of the laryngeal mask type. L (T L ) as an example, the specific determination method is given:

[0065]

[0066] This equation describes the relationship between velocity, pressure and height during the flow of a fluid, where P1 and P2 are the air pressures at the inlet and outlet of the airway, respectively; v is the airflow velocity; and ρ is the density of the gas. These parameters can all be measured using corresponding gas sensors.

[0067] k1 represents the adjustment factor of BMI during laryngeal mask ventilation, reflecting the influence of BMI on the first preset angle; k2 represents the adjustment factor of BMI during face mask ventilation, reflecting the influence of BMI on the second preset angle.

[0068] The BMI adjustment factor k1 during laryngeal mask ventilation can be obtained through observational studies, recording the airway ventilation effect of patients with different BMI under the same ventilation conditions. The specific process is as follows:

[0069] Record each patient's BMI value and its corresponding tidal volume V T ; Through simple linear regression analysis, the relationship is established:

[0070]

[0071] Where k1 is the adjustment factor that needs to be determined, BMI represents the patient's BMI index, and this formula is calculated based on 25 as the benchmark.

[0072] Similarly, the BMI adjustment factor k2 for mask ventilation can also be obtained by evaluating the effects of mask ventilation on patients with different BMIs through clinical experimental studies. The specific steps are as follows:

[0073] The BMI of each patient and its corresponding airway pressure P were recorded; multivariate regression model analysis was used to establish the relationship:

[0074]

[0075] Where k2 is the adjustment factor that needs to be determined.

[0076] In regression analysis, the intercept β0 represents the expected value of the dependent variable (such as tidal volume or airway pressure) when the independent variable (such as BMI) is zero. It provides the basic level of the model. Although a BMI of zero has no practical significance physiologically, the intercept still provides baseline information for the model.

[0077] In linear regression, the intercept and adjustment factor (i.e., k1, k2, represented by k in the following formula) can be calculated simultaneously through the analytical solution of the least squares method. The specific formula is:

[0078]

[0079]

[0080] Where N is the sample size, x is the independent variable (such as BMI), and y is the dependent variable (such as tidal volume V). T or airway pressure P).

[0081] Preferably, the second preset angle is 30 to 60 degrees, preferably 40 to 50 degrees, and particularly preferably 45 degrees, and in particular, the control scheme can determine one of the positive and negative 45 degrees according to the blood oxygen data provided by the blood oxygen monitoring module 100. For example, in the case of airway obstruction caused by sputum, etc., when the head is deflected 45 degrees from the neutral position to one side and maintained stably, the laryngeal mask ventilation effect can be significantly improved, for example, see Itagaki, T., Oto, J., Burns, S., Jiang, Y., Kacmarek, R., & Mountjoy, J. (2017). The effect of head rotation onefficiency of face mask ventilation in anaesthetised apnoeic adults: Arandomised, crossover study. European Journal of Anaesthesiology, 34, 432–440.

[0082] Preferably, after formulating the control scheme, the step of executing the control scheme by the system of the present invention further includes:

[0083] S1. Changing the head posture through the control module 400, for example, changing the inclination angle of the patient's head or tilting it to one side, so as to reduce the risk of aspiration and facilitate sputum suction;

[0084] S2. Clear the respiratory tract (including the oral cavity), for example by using a negative pressure suction device to aspirate oral secretions;

[0085] S3. Improve ventilation, such as by pressurized oxygen;

[0086] S4, at the second moment after step S2 and / or S3, the image acquisition module 200 and the blood oxygen monitoring module 100 acquire image data and blood oxygen data, and the processing module 300 evaluates the suffocation risk of the current patient at the second moment after clearing the airway and / or improving the ventilation. Here, the processing module 300 adjusts the control scheme formulated at the first moment according to the suffocation risk of the current patient at the second moment, wherein the processing module 300 determines whether to adjust the control scheme formulated at the first moment, or activate the emergency procedure to start the emergency response mode in an emergency situation according to the change rate between the suffocation risks at the first moment and the second moment.

[0087] Preferably, in the emergency response mode, the processing module 300 can respond quickly, increase the monitoring frequency, and adjust the number and angle of the cameras to determine the chest changes in a shorter time period, wherein, when the processing module 300 determines that the chest change amplitude of the corresponding patient shows a periodicity from strong to weak and from weak to strong within the preset time period for the analysis of the chest change law based on the image data collected by the image acquisition module 200, the processing module 300 determines whether to issue an emergency risk warning related to Cheyne-Stokes respiration to the doctor based on the suffocation risk change rate and the periodic characteristics of the chest change amplitude within the aforementioned preset time period. This is because MECT involves general anesthesia that may affect the respiratory center, so in some cases Cheyne-Stokes respiration (CSR) may occur during MECT treatment, and the doctor needs to closely monitor the patient's vital signs, including breathing and heartbeat, to ensure safety; if Cheyne-Stokes respiration or other respiratory problems occur, the doctor should take immediate measures to deal with them.

[0088] Preferably, if Figure 3~Figure 6 As shown, the control module 400 is configured in the form of a smart pillow, which includes a shoulder pillow 410, a neck pillow 420 and a head pillow 430, which are fixedly connected to form a whole. The shoulder pillow 410, the neck pillow 420 and the head pillow 430 are each configured with an inflation hole 460 and an air release hole 470, wherein when the smart pillow is placed in a use posture, the height of the inflation hole 460 is lower than the air release hole 470. A plurality of inflation columns 440 are arranged inside the shoulder pillow 410, the neck pillow 420 and the head pillow 430, and a connecting tube 450 is fixedly connected between the inflation columns 440, and the inflation holes 460 and the air release holes 470 are arranged on different inflation columns 440.

[0089] Preferably, if Figure 3~Figure 7 As shown, the inflatable column 440 is used to adjust the height of the shoulder pillow 410, the neck pillow 420 and the head pillow 430. Specifically, the inflatable hole 460 can be connected to an inflatable device to inflate the inflatable column 440, and the deflation hole 470 is provided with an openable and closable valve to discharge the gas in the inflatable column 440. As a preferred embodiment, the shoulder pillow 410, the neck pillow 420 and the head pillow 430 all have multiple layers of inflatable columns 440 to increase the adjustable range of the height allowed by each. The inflatable columns 440 of the shoulder pillow 410, the neck pillow 420 and the head pillow 430 are connected by a connecting pipe 450, and the height of the shoulder pillow 410, the neck pillow 420 and the head pillow 430 can be adjusted respectively according to the amount of inflation, so as to change the head posture of the patient. The inflatable hole 460 has a larger caliber than the deflation hole 470, so that rapid inflation can be achieved to quickly fill the smart pillow, and the height can be fine-tuned by slowing down the deflation speed.

[0090] Preferably, each inflation hole 460 and deflation hole 470 is equipped with a solenoid valve that can accurately control opening and closing, and the control signals of these solenoid valves are sent by a dedicated controller 480 located on the side of the smart pillow. The controller 480 can receive the control scheme from the processing module 300 and quickly convert it into a corresponding solenoid valve control signal. This control scheme accurately sets the required inflation volume of the shoulder pillow 410, the neck pillow 420 and the head pillow 430.

[0091] Preferably, in step S1, the control module 400 controls the filling degree of the inflatable columns 440 in the shoulder pillow 410, the neck pillow 420 and the head pillow 430 according to the direction of the head deflection of the corresponding patient. The shoulder pillow 410, the neck pillow 420 and the head pillow 430 respectively include an inflatable group composed of a plurality of parallel inflatable columns 440, and these inflatable groups are divided into at least two, especially three independent areas in the horizontal direction, in which the pressure can be controlled separately, and the air pressure and volume are adjusted separately, so as to assist the patient's head to deflect sideways in the horizontal direction.

[0092] Preferably, the control module 400 can adjust the shoulder pillow 410, the neck pillow 420 and the head pillow 430 to preset angles respectively in the following manner.

[0093] Preferably, the inflatable columns 440 inside the shoulder pillow 410 are designed into three groups in the width direction (i.e., the direction across the left and right shoulder joints) to be responsible for the independent control of three areas. The three areas are the middle area corresponding to the levator scapulae muscles of the shoulder and the two side areas corresponding to the deltoid muscles of the shoulder. Each area is controlled by a different solenoid valve, allowing independent adjustment of the air pressure and volume.

[0094] At the first moment, if the patient needs to adjust to the first preset angle, that is, to increase the backward tilt angle of the patient's head, the controller 480 will instruct the solenoid valve in the middle area to open, allowing air to flow in, while maintaining or reducing the air pressure in the two side areas, so as to raise the patient's shoulder area as a whole. If the patient needs to adjust to the second preset angle, that is, to adjust the side deviation angle of the patient's head, for example, 45 degrees to one side, the controller 480 will instruct the inflatable column 440 in the corresponding side area to inflate, while reducing the air pressure in the other side area, so as to assist the patient's shoulder to deflect in the specified direction by forming a height difference.

[0095] At the second moment, according to the data of the blood oxygen monitoring module 100 and the image acquisition module 200, if the patient's risk of suffocation is reduced, the controller 480 will simultaneously reduce the inflation volume of the three regional inflatable columns 440 to reduce the height of the shoulder pillow 410 as a whole and reduce the pressure on the neck. If the risk of suffocation increases, the controller 480 will further increase the inflation volume of the inflatable column 440 in accordance with the adjustment method in the first moment, increase the height or lateral angle of the shoulder pillow 410, to provide better support and reduce the pressure of the abdomen on the lungs.

[0096] Preferably, the inflatable column 440 inside the neck pillow 420 can be designed into two groups in the length direction (i.e., the extension direction of the neck) to be responsible for the independent control of two areas, namely the upper area corresponding to the upper part of the back neck area and the lower area corresponding to the lower part of the back neck area.

[0097] At the first moment, if the patient needs to adjust to the first preset angle, that is, the angle of the patient's head tilting back needs to be increased, the controller 480 will instruct the electromagnetic valve in the lower area to open to allow gas to be filled, and the electromagnetic valve in the upper area to close to keep the air pressure unchanged, so that the neck pillow 420 forms a posture in which the lower area is lifted and the upper area remains unchanged, which prompts the patient's neck to tilt back. If the patient needs to adjust to the second preset angle, that is, the patient's head needs to be deflected to one side, since the contact area between the neck pillow 420 and the patient is small, its adjustment ability in the lateral direction is weak, so the neck pillow 420 mainly adjusts the tilt angle of the neck, and does not make detailed adjustments in the lateral width direction. At this time, the controller 480 will instruct the electromagnetic valves in the upper and lower areas to remain closed, so as to maintain the air pressure unchanged, that is, the deflection of the patient's head is mainly adjusted by the posture changes of the shoulder pillow 410 and the head pillow 430.

[0098] At the second moment, according to the data of the blood oxygen monitoring module 100 and the image acquisition module 200, if the patient's suffocation risk decreases, the controller 480 will simultaneously reduce the inflation volume of the two regional inflatable columns 440 to reduce the height of the neck pillow 420 as a whole and reduce the pressure on the neck. If the suffocation risk increases, the controller 480 will further increase the inflation volume of the inflatable column 440 to increase the height of the neck pillow 420 in accordance with the adjustment method in the first moment.

[0099] Preferably, the inflatable columns 440 inside the headrest 430 are designed into three groups in the width direction to be responsible for the independent control of three areas, which are the middle area directly below the head and the two side areas on both sides of the middle area. Each area is controlled by a different solenoid valve, allowing independent adjustment of air pressure and volume.

[0100] At the first moment, if the patient needs to adjust to the first preset angle, that is, to increase the backward tilt angle of the patient's head, the controller 480 will instruct the solenoid valve in the middle area to open, allowing air to flow in, while maintaining or reducing the air pressure in the two side areas, so as to raise the patient's head area as a whole. If the patient needs to adjust to the second preset angle, that is, to adjust the side deviation angle of the patient's head, for example, 45 degrees to one side, the controller 480 will instruct the inflatable column 440 in the corresponding side area to inflate, while reducing the air pressure in the other side area, so as to assist the patient's head in deflecting in the specified direction by forming a height difference.

[0101] At the second moment, according to the data of the blood oxygen monitoring module 100 and the image acquisition module 200, if the patient's risk of suffocation is reduced, the controller 480 will simultaneously reduce the inflation volume of the three regional inflatable columns 440 to reduce the height of the headrest 430 as a whole and reduce the pressure on the neck. If the risk of suffocation increases, the controller 480 will further increase the inflation volume of the inflatable columns 440 in accordance with the adjustment method in the first moment, increase the height or side deviation angle of the headrest 430, to provide better support and reduce the pressure of the abdomen on the lungs.

[0102] Preferably, the blood oxygen monitoring module 100 can use a pulse oximeter for non-invasive monitoring, and may include a sensor and a display screen connected to the sensor wire. The sensor is usually clamped on a patient's fingertip, earlobe or other body part with high transparency. This wearing method ensures that no discomfort or displacement occurs during the MECT process. The sensor can emit two different wavelengths of light, which are received by the photoelectric probe on the other side of the sensor. Due to the different abilities of oxygenated hemoglobin and deoxygenated hemoglobin to absorb these two types of light, by analyzing the absorption of light passing through the blood, the pulse oximeter can calculate the proportion of oxygenated hemoglobin in the blood, and then display the blood oxygen saturation value on a display screen located next to the patient's bed.

[0103] Preferably, the blood oxygen monitoring module 100 has the function of real-time collection and analysis of blood oxygen data, including accurate calculation of blood oxygen saturation and its changing slope. The module can present the patient's blood oxygen saturation curve on the display screen in real time, and provide blood oxygen saturation slope information at any time. In addition, the module also has the ability to display historical trends and alarm information of blood oxygen saturation, providing medical staff with comprehensive monitoring of the patient's blood oxygen status. The blood oxygen monitoring module 100 is connected to the processing module 300 via wireless connection methods such as Bluetooth, Wi-Fi, or wired connection methods such as USB, to ensure that the collected blood oxygen data is transmitted to the processing module 300 in real time and efficiently. At the same time, the module will also retain historical data for subsequent in-depth analysis.

[0104] Preferably, the image acquisition module 200 may use a high-resolution, low-light camera that can capture clear images of the chest and mandible. The camera is placed at a certain distance from the patient's chest and head, and is adjusted to an optimal shooting angle through a flexible bracket or fixture. The camera transmits image data to the processing module 300 in real time through a high-speed data connection (such as HDMI or USB3.0). The image acquisition module 200 can use a built-in processor, such as a graphics processing unit (GPU) or a central processing unit (CPU) to execute relevant processing algorithms for image recognition and feature extraction to form image data that can be recognized and read by the processing module 300.

[0105] Preferably, the image acquisition module 200 has different acquisition contents in the monitoring stage and the auxiliary stage. Specifically, the monitoring stage is the stage in which the processing module 300 has not yet found that the patient has a risk of suffocation, and the image acquisition module 200 continues to acquire the patient's chest rise and fall and mandibular shape, while the auxiliary stage refers to the stage in which the image acquisition module 200 acquires the patient's head position and posture to assist the control module 400 in accurately adjusting the patient's head posture after the processing module 300 finds that the patient has a risk of suffocation. In particular, the image acquisition module 200 has no less than two image acquisition angles. On the one hand, periodically changing the acquisition angle during the monitoring phase can ensure that the image acquisition module 200 can capture and record the patient's chest rise and fall and mandibular morphology changes from at least the front and side perspectives, making the acquired image data more specific and complete; on the other hand, in the auxiliary phase, the image acquisition module 200 can change the acquisition angle to confirm the current height and position of the patient's head from a clearer camera position, and it can send such information to the control module 400, thereby assisting in guiding the control module 400 to perform fine-tuning within an appropriate adjustable range when executing the control scheme, so as to adjust the patient's head to the best posture to reduce the risk of suffocation. To achieve such a function, the image acquisition module 200 can be configured with multiple cameras or a rotatable and / or movable camera, so as to use multiple acquisition angles to adapt to different monitoring needs.

[0106] Preferably, the image data acquired by the image acquisition module 200 may include: chest rise and fall frequency, chest movement amplitude, mandibular morphological changes, etc., wherein the chest rise and fall frequency refers to the number of breaths per minute of the patient, which is a direct indicator of the respiratory frequency; the chest movement amplitude can reflect the range of chest expansion and contraction of the patient during the respiratory cycle, which is used to evaluate the depth of breathing; mandibular morphological changes refer to the position changes of the mandible in different respiratory stages (such as inhalation and exhalation), and mandibular movements related to breathing, such as the lowering amplitude of the mandible during open-mouth breathing. These parameters comprehensively depict the patient's breathing pattern and provide necessary basic information for further data analysis and processing by the processing module 300.

[0107] The image acquisition module 200 in the present invention not only plays a role in monitoring the patient's respiratory parameters, but also participates in the humanized adjustment of the control scheme. The image acquisition module 200 can capture the image data of the patient's head in real time and transmit the data to the control module 400. In order to reduce the risk of suffocation of the patient, the control module 400 needs to adjust the patient's head position when necessary to ensure that the airway is unobstructed. However, continuously keeping the head elevated may cause excessive extension of the patient's neck, thereby affecting comfort. Therefore, the control module 400 will only moderately raise the head when the risk of suffocation is detected according to the patient's real-time status, aiming to balance safety needs and the patient's comfort experience. The control module 400 adaptively adjusts the patient's head posture based on the received head position and height information to ensure the accuracy and comfort of the treatment. Therefore, the image acquisition module 200 not only plays the role of a data acquisition device, but also serves as a decision support part in the adjustment process. It can complete the functions that traditional physiological parameters (such as blood oxygen level, etc.) cannot provide, and provide a comprehensive dynamic adjustment basis for the control module 400.

[0108] Preferably, the processing module 300 can be configured on a high-performance computer or server near a medical workstation or placed in a dedicated server room. This module is mainly responsible for performing complex data analysis and model calculation tasks. By exchanging data with the blood oxygen monitoring module 100 and the image acquisition module 200, the processing module 300 can receive blood oxygen and image data. Subsequently, the processing module 300 fuses the two types of data and uses a machine learning model to assess the patient's risk of suffocation. Based on the risk level assessment results, the processing module 300 will formulate an appropriate adjustment plan for the control module 400 to ensure patient safety.

[0109] When judging the patient's risk of suffocation, the processing module 300 uses image data and blood oxygen data in combination, rather than relying on a single parameter, thereby significantly improving the accuracy and sensitivity of the assessment. The module uses a data fusion algorithm to achieve this function. Specifically, the processing module 300 first pre-processes the data obtained from the image acquisition module 200 and the blood oxygen monitoring module 100. In terms of image data, the module uses a specific algorithm to identify and quantify the key information of chest rise and fall and mandibular movement; and in terms of blood oxygen data, the module ensures that it accurately matches the image data in time series. Subsequently, the processing module 300 extracts key features from the pre-processed data, including respiratory rate, respiratory amplitude, and changes in blood oxygen saturation. The selection of these features is intended to fully reflect the patient's respiratory status and blood oxygen level, providing an important basis for subsequent suffocation risk assessment. Next, the processing module 300 uses a variety of built-in algorithms, such as weighted average, Kalman filter, neural network or decision fusion algorithm, to fuse the features of image data and blood oxygen data. The purpose of this step is to ensure that the information from the two data sources can complement each other and jointly construct a comprehensive and accurate suffocation risk assessment model. Finally, the processing module 300 uses advanced machine learning classifiers, such as random forests, support vector machines, or deep learning networks, to train the fused data. Through continuous iteration and optimization, these classifiers can gradually improve the ability to assess the patient's suffocation risk and provide strong support for clinical decision-making.

[0110] Preferably, the patient's head posture and chest posture control scheme is formulated by the processing module 300. The module performs calculations based on a pre-stored calculation model and in combination with the blood oxygen saturation data obtained from the blood oxygen monitoring module 100 and the head and chest posture information before control provided by the image acquisition module 200. Specifically, the control scheme includes a specific adjustment amount Δθ (°) for the patient's head posture and a specific adjustment amount Δh (cm) for the chest posture.

[0111] The calculation formula of the head posture adjustment value is as follows:

[0112]

[0113] In the formula,

[0114] is the target blood oxygen level (usually set at 95-100%);

[0115] R is the suffocation risk assessment result (0-1);

[0116] θ: head posture angle before control (°);

[0117] a represents the sensitivity of blood oxygen level to head posture adjustment;

[0118] b represents the sensitivity of suffocation risk to head posture adjustment;

[0119] c is the feedback coefficient for adjusting the front head posture.

[0120] The calculation formula of chest posture adjustment value is as follows:

[0121]

[0122] In the formula,

[0123] h: chest posture height before adjustment (cm)

[0124] d represents the sensitivity of blood oxygen level to chest posture adjustment;

[0125] e represents the sensitivity of suffocation risk to chest posture adjustment;

[0126] f is the feedback coefficient for adjusting the front chest posture.

[0127] Parameters a to f have different effects on the control of head posture and chest posture. Blood oxygen level is an important factor affecting head posture. When the blood oxygen level deviates from the ideal value, timely adjustment of the head posture can significantly improve oxygenation. Therefore, coefficient a can reflect the necessity of this adjustment to highlight its positive impact on patient health. The risk of suffocation is an important consideration to ensure patient safety. When facing a high risk of suffocation, the adjustment of head posture needs to be more cautious to reduce the possibility of complications. Coefficient b reflects this risk management trade-off in the model and helps guide clinical decision-making during operation. The initial angle of the head posture also affects the required adjustment. Different posture angles will lead to slight differences in the need for adjustment, so coefficient c emphasizes the importance of the initial state in the model to ensure the effectiveness of the adjustment effect. The adjustment of chest posture is closely related to the deviation of blood oxygen level. According to the clinical experience of experts, chest posture adjustment is of considerable importance in improving the overall oxygenation effect, so coefficient d gives this factor a corresponding weight in the model to reflect its positive impact on patient oxygenation. In addition, the adjustment of chest posture due to suffocation risk cannot be ignored. Moderate consideration of the risk of suffocation can provide safer guidance for chest posture adjustment, and the setting of coefficient e helps to ensure the safety of patients during the adjustment process. Finally, although the effect of the initial height of the chest posture on the need for adjustment is relatively small, it is still of certain importance. Therefore, coefficient f should be considered in the model to ensure a comprehensive assessment of the potential impact of chest posture adjustment.

[0128] Based on the above considerations, experts in the field can set the initial empirical values ​​of each coefficient a~coefficient f in the model in combination with clinical experience. Under the premise that the initial values ​​of each parameter are known, large sample data is collected (such as the patient's blood oxygen level, head and chest posture, suffocation risk assessment results in different clinical scenarios, etc.), and the parameters a, b, c, d, e, and f can be optimized through linear regression analysis. Specifically, the suffocation risk assessment result R is used as the dependent variable, and other variables (such as blood oxygen level O, target blood oxygen level , head posture angle θ, chest posture height h, etc.) as independent variables, and by establishing a regression model, the impact of each variable on the risk of suffocation is analyzed to optimize parameters a, b, c, d, e and f. This process will help improve the accuracy of predicting the risk of suffocation and ensure that the adjustment plan for head and chest posture is more scientific and reasonable.

[0129] In summary, the final head posture after adjustment is: θ'=θ+Δθ; the chest posture after adjustment is: h'=h+Δh.

[0130] Combining the data of image monitoring and blood oxygen monitoring can provide more comprehensive respiratory status information to the processing module 300. Image monitoring can capture subtle movements of the chest and mandible, which are important physiological signals that the blood oxygen meter cannot provide. Before the blood oxygen saturation drops, the image data may have shown signs of dyspnea. Therefore, through this multi-dimensional information fusion, the risk of suffocation can be determined more accurately, which can facilitate medical staff or control equipment to take timely countermeasures and avoid the occurrence of related complications.

[0131] In a course of MECT treatment, patients usually undergo 8 to 12 treatments, with a treatment frequency of every other day. Among them, the power of the first treatment needs to be adjusted according to the individual differences of the patient, and the parameters provided by different models of machines are accurately set. For subsequent treatments, the adjustment of power is based on the specific circumstances of the last epileptic wave attack. The setting of stimulation parameters is affected by many factors, including but not limited to the patient's age, gender, number of previous stimulations, type of stimulation, location of electrode placement, and the dosage of sedatives and barbiturate anesthetics previously used. It is worth noting that when the current passes through the glossopharynx, it stimulates the glossopharyngeal vagus nerve, which may cause nausea and vomiting in patients. Under the same treatment power, due to differences in physical fitness and other factors among different patients, their tolerance to electrical stimulation will also be different. Especially for those patients with low tolerance, the risk and severity of suffocation caused by nausea and vomiting will be relatively high.

[0132] Preferably, in order to realize personalized monitoring and prevention of the patient's suffocation risk, the processing module 300 of the present invention can be connected to the HIS (hospital information system) in the hospital for data connection, so as to quickly obtain the patient's basic information and pre-evaluate the patient's suffocation factor based on this information. Different from the evaluation of the patient's suffocation risk by the processing module 300, its evaluation of the suffocation factor is different at least in the time sequence and the way of evaluation. The evaluation of the suffocation factor refers to the identification and analysis of factors that may cause individuals to experience suffocation symptoms under specific conditions, which may include individual physiological characteristics, existing medical conditions, drug use, and environmental factors. These factors can be known before the MECT treatment, so the evaluation time can be before the MECT treatment. The evaluation of the suffocation risk refers to further analysis based on the identified suffocation factors to determine the probability of suffocation events when the individual exhibits a specific condition (such as a decrease in chest fluctuation and a decrease in blood oxygen saturation) during the MECT treatment, so the evaluation time is after the suffocation factor. Together, the two constitute the basis for individual suffocation risk management.

[0133] Preferably, in the preliminary assessment of the asphyxia factor, the processing module 300 focuses on obtaining the patient's previous MECT stimulation-related records, other disease factors that may affect their tolerance to electrical stimulation, and drugs that affect the response of the nervous system from the HIS. In view of the causal relationship between nausea, vomiting and asphyxia, and that these symptoms can be reflected in the rise and fall of the chest and changes in the mandibular morphology, the processing module 300 optimizes the clarity of the image obtained by the image acquisition module 200, aiming to effectively identify the early symptoms of asphyxia when they are not yet obvious. In addition, the processing module 300 can also be connected to the MECT instrument to obtain the stimulation power information of the current treatment setting and the stimulation power information used in historical treatments.

[0134] Preferably, the determination of the asphyxia factor mainly relies on a machine learning algorithm that can comprehensively consider the patient's medical history, drug use, and power adjustment during MECT. Specifically, the algorithm first trains a prediction model based on the patient's historical MECT response data, including but not limited to physical reactions and nervous system feedback. This model also takes into account the patient's existing diseases and current medications, especially those known to affect the reactivity of the nervous system. Based on these data, the algorithm calculates an asphyxia factor score, dividing the asphyxia factor into at least two levels, low and high, to provide a basis for subsequent personalized monitoring. A low level of asphyxia factor indicates that the patient has a high tolerance to electrical stimulation and is not prone to symptoms of asphyxia during MECT treatment; a high level of asphyxia factor indicates that the patient has a low tolerance to electrical stimulation and is prone to symptoms of asphyxia during MECT treatment. The determination of the asphyxia factor is based on the above algorithm in combination with various influencing factors.

[0135] Preferably, in addition to relying on scientific methods, the assessment of the asphyxia factor can also be determined by medical staff with rich clinical experience. They can conduct a comprehensive analysis based on individual differences of patients, accurately interpret the patient's non-verbal information, and quickly identify potential asphyxia risks. In addition, the display device equipped with the processing module 300 can display key information related to the patient's asphyxia factor in real time, providing powerful assistance to medical staff, so that they can make a more accurate judgment on the asphyxia factor based on their own experience.

[0136] Preferably, according to the evaluation results of the asphyxia factor, the processing module 300 will formulate a personalized image acquisition scheme for the monitoring stage of the image acquisition module 200. When the module is configured with multiple cameras, the system will automatically adjust the startup time and monitoring frequency of the camera according to the patient's asphyxia risk level to ensure that the slight changes in the thorax and mandible can be captured from multiple angles. In the case of a single rotatable or movable camera, the system will automatically adjust the speed of the camera rotation and the frequency of angle change based on the patient's risk level, thereby maximizing the quality and relevance of the image data. This personalized scheme can effectively improve the monitoring level of the patient's potential asphyxia risk. Specifically, when formulating a personalized image acquisition scheme, the processing module 300 can comprehensively consider multiple parameters to optimize the performance of the image acquisition module 200. The processing module 300 can convert the asphyxia factor evaluation results into a specific risk level and formulate a monitoring strategy accordingly. For example, at a high risk level, the processing module 300 will start all cameras to work simultaneously, provide comprehensive monitoring data from multiple angles, and ensure that the slight changes in the thorax and mandible can be captured. The adjustment of the monitoring frequency can be based on a preset time interval or based on an event trigger, such as automatically increasing the monitoring frequency when an abnormal chest rise frequency is detected. In addition, the processing module 300 may also improve the image resolution to capture more subtle physiological changes, and dynamically adjust the angle and field of view of the camera in the image acquisition module 200 according to the suffocation risk level and the changes in the patient's body position. Further, the processing module 300 uses a machine learning algorithm to continuously learn and optimize parameter settings based on real-time collected image data to adapt to various situations that may occur during the treatment process. At the same time, the processing module 300 can establish a feedback mechanism to adjust subsequent image acquisition schemes based on real-time monitoring results, such as automatically adjusting camera parameters when the chest rise amplitude is detected to be lower than a preset threshold. The processing module 300 can also recognize and adapt to slight changes in the patient's body position, and automatically adjust the camera position in the image acquisition module 200 to maintain the best monitoring angle. In an emergency, the processing module 300 can respond quickly, increase the monitoring frequency, and adjust the camera to capture more comprehensive image data.

[0137] Preferably, the data connection between the control module 400 and the image acquisition module 200 ensures real-time transmission and interaction of information. Given that the shape of the head and neck of each patient is different when lying flat on the smart pillow, a fixed adjustment scheme may not be able to accurately place the patient in the ideal posture, which may have an adverse effect on the treatment effect. To overcome this challenge, the control module 400 performs dynamic fine-tuning under the real-time feedback provided by the image acquisition module 200. The control module 400 optimizes the treatment effect by accurately adjusting the expansion degree of different parts in the smart pillow to ensure that the patient's actual head posture is consistent with the preset target posture.

[0138] In the auxiliary stage, the image acquisition module 200 continuously captures the real-time height data of the patient's head, which provides an accurate adjustment reference for the control module 400. Using this data, the control module 400 can accurately determine the specific position and height of the smart pillow that needs to be adjusted, thereby achieving accurate control of the patient's head posture.

[0139] During the MECT treatment, the mechanical ventilation device 500 will be used to continuously supply oxygen to the patient with positive pressure assisted ventilation. Preferably, in the present invention, the mechanical ventilation device 500 can reduce the risk of suffocation of the patient in a manner that cooperates with the control module 400. During the MECT process, the control module 400 can adjust the ventilation parameters of the mechanical ventilation device 500 to adapt to the current state of the patient.

[0140] Preferably, once the processing module 300 assesses that the patient is at risk of suffocation, the control module 400 can adjust the patient's head posture to ensure optimal opening of the airway. At the same time, the mechanical ventilation device 500 connected to the processing module 300 also responds according to the adjustment of the head posture. For example, if the change in the head angle indicates that breathing may become more difficult, the processing module 300 will instruct the mechanical ventilation device 500 to increase the single ventilation volume, or increase the ventilation frequency when necessary, to keep the patient's breathing smooth.

[0141] This dynamic adjustment mechanism of ventilation volume not only considers the amount of gas inhaled in a single time to adapt to the patient's oxygen needs, but also focuses on the adjustment of ventilation frequency to ensure that the patient's number of breaths per minute can match their physiological needs, avoiding the risk of suffocation caused by fixed ventilation settings. Therefore, the present invention achieves close coordination between the smart pillow and the mechanical assisted ventilation device through real-time monitoring and adaptive adjustment, providing patients with safer and more effective respiratory support.

[0142] Example 2

[0143] This embodiment is a further supplement to the above-mentioned embodiment, and the repeated contents will not be repeated here.

[0144] In this embodiment, the MECT intraoperative asphyxia risk prevention system introduces the concept of asphyxia risk index. The asphyxia risk index (denoted by P) is a dimensionless indicator with a value range between 0 and 1, which is used to quantify and evaluate the risk of asphyxia in patients during general anesthesia electroconvulsive therapy (MECT) surgery. The index comprehensively considers multiple important factors such as the patient's blood oxygen saturation, respiratory flow, head and chest posture (affecting airway smoothness), and tidal volume.

[0145] During MECT surgery, patients may face the risk of suffocation due to limited respiratory function under general anesthesia. The suffocation risk index P can be used as a real-time monitoring tool to help the medical team identify possible suffocation risks in a timely manner, so as to take appropriate intervention measures to ensure the safety of patients. The suffocation risk index P can provide valuable information to anesthesiologists and surgeons by quantifying the impact of different factors on the risk of suffocation, helping them to assess the risk status of patients during surgery. This is of great significance in formulating anesthesia and surgical plans. In particular, before and during surgery, medical staff can use the control module 400 to optimize the patient's head and chest posture based on the specific value of the suffocation risk index P, adjust the respiratory flow and tidal volume, thereby minimizing the risk of suffocation and improving the safety and success rate of the operation. In addition, since the physiological parameters and reactions of each patient are unique. By measuring and evaluating the various variables that affect the risk of suffocation, medical staff can provide personalized management and treatment plans for each patient to further improve the safety of patients.

[0146] Specifically, the processing module 300 of the present invention stores an algorithm model for calculating the suffocation risk index P, and its specific calculation formula is as follows:

[0147]

[0148] In the formula,

[0149] O2: patient's blood oxygen saturation (%);

[0150] H: head posture (expressed in degrees);

[0151] C: chest posture (expressed in degrees);

[0152] R: respiratory flow (L / min);

[0153] V T : Tidal volume per breath (L);

[0154] F: The patient's lung function factors (including vital capacity, respiratory rate, etc.).

[0155] Furthermore, to express the relationship between these variables, the f function can be defined in the form of a weighted sum, specifically:

[0156]

[0157] in,

[0158] 1. w1, w2, and w3 are weight coefficients that reflect the relative impact of each factor on the asphyxia risk index P. The importance of each factor is scored based on the clinical experience of multiple respiratory physicians, anesthesiologists, and experts in related fields. The initial values ​​of the weight coefficients w1, w2, and w3 are obtained through comprehensive evaluation.

[0159] Under the premise of knowing the initial value of the weight coefficient, a large sample of data (such as the patient's blood oxygen level, posture, respiratory flow, etc. during MECT surgery) is collected, and the asphyxia risk index P is used as the dependent variable and other variables are used as independent variables for linear regression analysis. The weight coefficients w1, w2, and w3 can be optimized, thereby improving the accuracy of the asphyxia risk index P. The specific linear regression model is as follows:

[0160]

[0161] here, is the error term, which can be obtained by calculating the residual after regression analysis.

[0162] By minimizing the mean square error (MSE), the parameter estimates can be obtained as follows:

[0163]

[0164] By minimizing this MSE until it converges (i.e., no longer decreases significantly, or reaches a preset maximum number of iterations), the least squares method or other optimization algorithms (such as gradient descent) can be used to calculate w1, w2, w3, as follows:

[0165] Define the matrix X and vector w so that the linear regression model can be expressed in matrix form as follows:

[0166]

[0167] in:

[0168] P is the N×1 target value vector;

[0169] X is an N×3 feature matrix, where each row is ;

[0170] w is the weight vector [w1,w2,w3];

[0171] e is the error vector;

[0172] By solving the least squares problem, we get the optimal weight vector w:

[0173]

[0174] The calculation result of this formula directly gives the solution of [w1,w2,w3]. Use the linear algebra library to calculate this formula, and then substitute the observed data into X and P to get the optimized values ​​of each weight coefficient.

[0175] 2. Lung function factor F can be quantified based on lung function tests (such as vital capacity, respiratory rate, maximum ventilation, etc.). These data can usually be collected by a pulmonary function tester. In addition, a lung function score can be calculated using a standardized formula based on the patient's age, gender, height and other characteristics to generate a comprehensive lung function factor F. Exemplarily, the formula for determining lung function factor F is given here as follows:

[0176]

[0177] In the formula,

[0178] V max : Maximum respiratory volume (L);

[0179] RespRate: respiratory rate (times / min);

[0180] VC: Vital capacity (L).

[0181] 3. g(H,C) is a function used to express the influence of head and chest posture on the suffocation risk index P. Its specific calculation formula can be expressed as:

[0182]

[0183] Parameters a, b, c, and d are used to quantify the different contributions of head and chest posture to the risk of suffocation. Clinicians can give initial values ​​for these parameters based on experience. These values ​​will reflect the relative contribution and importance of head and chest posture to the risk of suffocation. The setting of parameter a can indicate the role of head posture in maintaining airway patency, especially in situations where airway patency is ensured and the risk of obstruction is reduced. The natural position of the head can often significantly reduce the incidence of suffocation. In addition, parameter b reflects the relative impact of chest posture on the risk of suffocation. Changes in chest posture usually have a direct impact on the airway. At the same time, parameter c is used to quantify the flexibility of head posture adjustment. This indicator can reflect the adaptability of the head in different clinical situations. For example, when performing airway management, the patency of the airway can be effectively improved by properly adjusting the head posture. Parameter d is used to indicate the flexibility of chest posture adjustment, reflecting the degree of support of the chest to the patient's respiratory function in different situations, especially in terms of the impact of chest compression or relaxation on breathing.

[0184] The initial values ​​of parameters a, b, c, and d can be used as a starting point for the model, and the suffocation risk of patients under different head and chest postures can be experimentally observed, the data recorded, and optimized using curve fitting methods (such as nonlinear regression). These parameters can also be optimized by constructing a simulation model to simulate the effects of different postures on airway patency and oxygen flow, and adjusting a, b, c, and d through an optimization algorithm (such as the least squares method) so that the suffocation risk predicted by the model is as close as possible to the actual observation results. Exemplarily, by minimizing the following error terms:

[0185]

[0186] The optimal values ​​of a, b, c, and d can be found using nonlinear optimization algorithms (such as the Levenberg-Marquardt algorithm. In practical applications, this process can be implemented with the help of optimization libraries in programming languages ​​such as Python).

[0187] 4. In the calculation formula of the suffocation risk index P, e is a constant value. In the initial stage, a typical value (such as e=1) can be set. In the subsequent model, sensitivity analysis can be performed on e to observe its impact on the model output and find a suitable value through gradual adjustment. Specifically, in the model, it can be initially set or , which can also be adaptively adjusted according to the fit of the model.

[0188] Combining the above formulas, the final suffocation risk model can be expressed as:

[0189]

[0190] During the operation, the processing module 300 intermittently or continuously obtains the blood oxygen saturation O2, head posture H, chest posture C, respiratory flow R and tidal volume V monitored by the blood oxygen monitoring module 100, the image acquisition module 200 and other sensors. T The data is used to calculate the asphyxia risk index P at different time points during the operation. If the asphyxia risk index P exceeds a certain threshold (for example, 0.5), the processing module 300 can send a corresponding instruction to the control module 400 to reduce the asphyxia risk by adjusting the patient's posture, increasing oxygen supply, or taking other measures.

[0191] The following table shows the physiological parameters of a patient collected by the system of the present invention at different time points, and the corresponding suffocation risk index P.

[0192] Based on the data in this table, a curve chart showing the change of the patient's suffocation risk index over time can be generated, such as Figure 8During the MECT procedure, the system implements effective posture adjustment based on the dynamic changes of the asphyxia risk index P. Appropriate head and chest posture helps improve airway patency and thus reduce the risk of asphyxia. Figure 8 As shown, the patient's suffocation risk index P exceeds the preset threshold (set to 0.5) between time periods t4 and t5. When the processing module 300 of the system captures this information, it immediately sends a corresponding control instruction to the control module 400. By adjusting the patient's head and chest posture in the time period t5 to t6, the patient's breathing state is significantly improved (such as significant improvement in tidal volume, respiratory flow and vital capacity), thereby effectively reducing the risk of suffocation.

[0193]

[0194] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation of the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably" or "according to a preferred embodiment", which means that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept. Throughout the text, the features guided by "preferably" are only an optional method and should not be understood as a must-have setting. Therefore, the applicant reserves the right to abandon or delete the relevant preferred features at any time.

Claims

1. A system for preventing suffocation risk during MECT surgery, characterized in that: The system comprises: A blood oxygen monitoring module (100) is used to measure blood oxygen data of a patient's blood oxygen saturation; An image acquisition module (200) is used to capture and record image data of the patient's head and chest posture, which has different acquisition angles and acquisition contents in different time stages. The image acquisition module (200) continuously acquires the patient's chest rise and fall and mandibular shape in the monitoring stage, and acquires the patient's head position and posture in the auxiliary stage; A processing module (300) is data-connected to the image acquisition module (200) and the blood oxygen monitoring module (100), and is capable of evaluating the patient's risk of suffocation based on acquired data, the data including image data and blood oxygen data. The processing module (300) calculates a preset angle according to the suffocation risk and in combination with the patient's BMI and the ventilation method used, and sends it to the control module (400) in the form of a control instruction. The processing module (300) formulates a corresponding control instruction according to the ventilation method currently used by the patient, which includes laryngeal mask ventilation and face mask ventilation, in combination with the acquired image data and blood oxygen data. When the suffocation risk of the patient exceeds a threshold, the control module (400) reduces the suffocation risk by executing the control instruction. The control instruction includes causing the patient's head to have a backward offset relative to the neutral position to reach a first preset angle and / or causing the patient's head to have a lateral offset relative to the neutral position to reach a second preset angle. In response to the control instruction, the control module (400) rotates the patient's head posture to a preset angle by adjusting the local or overall inflation and deflation volume, and the processing module (300) calculates the adjustment amount of the patient's head posture in combination with the blood oxygen saturation data obtained from the blood oxygen monitoring module (100) and the patient's head posture information before control provided by the image acquisition module (200).

2. The system according to claim 1, characterized in that When the patient's suffocation risk exceeds a threshold value, in the case of laryngeal mask ventilation, the first preset angle is determined based on the selected laryngeal mask type and the current BMI index of the patient.

3. The system according to claim 1, characterized in that When the patient's suffocation risk exceeds a threshold, in the case of mask ventilation, the second preset angle is determined based on the patient's BMI index of the selected mask type.

4. The system according to claim 1, characterized in that The image data acquired by the image acquisition module (200) in the monitoring phase include chest rise and fall frequency, chest movement amplitude, and mandibular morphological changes, and the image data acquired in the auxiliary phase include head height, The image acquisition module (200) is configured in the form of a plurality of cameras or a rotatable and / or movable camera to achieve switching of image acquisition angles in the same or different time stages. The control module (400) can dynamically adjust the control scheme according to the head height information fed back by the image acquisition module (200), so as to ensure that the actual head posture of the patient is consistent with the preset target posture.

5. The system according to claim 1, characterized in that The control module (400) comprises a shoulder pillow (410), a neck pillow (420) and a head pillow (430) which are interconnected to form a smart pillow; the shoulder pillow (410), the neck pillow (420) and the head pillow (430) are each provided with a plurality of inflatable columns (440) and an inflating hole (460) and an air-release hole (470); a connecting tube (450) is connected between the inflatable columns (440); and the inflating holes (460) and the air-release holes (470) are arranged on different inflatable columns (440).

6. The system according to claim 5, characterized in that The shoulder pillow (410), the neck pillow (420) and the head pillow (430) respectively include an inflatable group consisting of a plurality of parallel inflatable columns (440), the inflatable group being divided into at least a plurality of independent areas with individually controlled pressures in the horizontal direction, the inflatable holes (460) and the deflation holes (470) being equipped with electromagnetic valves that are controlled to open and close by a controller (480) according to a control scheme, so that the shoulder pillow (410), the neck pillow (420) and the head pillow (430) can adjust the patient's head backward offset to a first preset angle and / or adjust the head lateral offset to a second preset angle at a first moment when the image data and blood oxygen data of the current patient are acquired and at a second moment when the current patient clears the respiratory tract and / or improves ventilation, by means of the electromagnetic valves that are opened and closed according to the conditions at the corresponding moments.

7. The system according to claim 1, characterized in that The processing module (300) is capable of acquiring basic information of the patient and pre-evaluating the patient's asphyxia factor based on the basic information. The asphyxia factor can use the patient's previous MECT stimulation-related records, diseases that affect the patient's tolerance to electrical stimulation, and drugs that affect the nervous system response as evaluation factors.

8. The system according to claim 7, characterized in that The processing module (300) can formulate different image acquisition schemes for the monitoring phase of the image acquisition module (200) according to the evaluation result of the asphyxia factor, wherein different image acquisition schemes differ in at least one of the start-up duration, monitoring frequency, rotation speed and angle.

9. The system according to claim 1, characterized in that The processing module (300) can be data-connected to the mechanical ventilation device (500) so as to coordinately perform adaptive adjustment of the ventilation volume and ventilation frequency when the control module (400) adjusts the posture of the patient's head.

Images