A real-time monitoring system for multi-modal electrical bio-signals fusion in orthopedic surgery

Abstract

The application relates to the technical field of surgical monitoring, and discloses a real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery, which comprises the following modules: a data acquisition module, which acquires real-time signal data in the surgical process of a patient; a vital sign monitoring module, which pre-processes the real-time signal data, determines the vital sign state in the surgical process of the patient, and determines whether the patient is in a critical state based on the vital sign state; if the patient is in a critical state, the vital sign state is used to determine the criticality grade of the patient; a surgical monitoring module, which determines the surgical state in the surgical process of the patient according to the pre-processed surgical electrical signal, determines whether to perform an alarm according to the surgical state, and generates an alarm instruction; and a monitoring alarm module, which performs sound and light alarms according to the criticality grade and the alarm instruction. The application realizes comprehensive and real-time monitoring of the surgical process of the patient by fusing multimodal electrogenic signals.

Description

Technical Field

[0001] This invention relates to the field of surgical monitoring technology, and in particular to a real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery. Background Technology

[0002] In orthopedic surgery, precise monitoring of the patient's physiological state and surgical procedures is crucial. Traditional monitoring methods often rely on single physiological signals, such as electrocardiograms (ECG) and electromyography (EMG), but a single signal may not comprehensively and accurately reflect the patient's condition. Furthermore, orthopedic surgery generates various electrical signal changes, such as those produced by bone cutting and instrument manipulation. These signals are significant for monitoring and evaluating the surgical process. However, existing monitoring systems struggle to effectively fuse and analyze these multimodal electrical signals in real time, failing to provide surgeons with comprehensive and accurate surgical information, thus impacting the precision and safety of the procedure.

[0003] Therefore, there is an urgent need for a real-time monitoring system that integrates multimodal electrogenic signals for orthopedic surgery to monitor the surgical process. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery, aiming to solve the above-mentioned problems.

[0005] This invention provides a real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery, comprising:

[0006] The data acquisition module is configured to acquire real-time signal data during the patient's surgical procedure, including electromyography (EMG) signals, electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and surgical electrical signals.

[0007] The vital signs monitoring module is configured to preprocess the real-time signal data, determine the patient's vital signs status during the operation based on the preprocessed electromyography, electrocardiography and electroencephalography signals, and determine whether the patient is in a critical state based on the vital signs status. If the patient is in a critical state, the critical level of the patient is determined based on the vital signs status.

[0008] The surgical monitoring module is configured to determine the surgical status of the patient during the operation based on the preprocessed surgical electrical signal, and to determine whether to trigger an alarm based on the surgical status. If an alarm is triggered, an alarm command is generated. The surgical status includes abnormal surgical status and normal surgical status.

[0009] The monitoring and alarm module is configured to trigger an audible and visual alarm based on the severity level and alarm command.

[0010] Preferably, the data acquisition module includes:

[0011] An EMG sensor is placed in the patient's surgical area to detect the patient's electromyography signals in real time.

[0012] ECG electrodes are placed on the patient's chest to detect the patient's electrocardiogram signals in real time.

[0013] EEG electrodes are placed on the patient's head to detect the patient's electroencephalogram (EEG) signals in real time.

[0014] Surgical electrical signal sensors are installed on surgical electrosurgical equipment to detect surgical electrical signals during patient surgery.

[0015] Preferably, the vital signs monitoring module preprocesses the real-time signal data and determines the patient's vital signs during surgery based on the preprocessed electromyography (EMG), electrocardiography (ECG), and electroencephalography (EEG) signals, including:

[0016] The real-time signal data is preprocessed, including signal synchronization processing, filtering and noise reduction processing, baseline correction processing, and signal normalization processing.

[0017] The patient's electromyographic status during surgery was determined based on the pre-processed electromyographic signals;

[0018] The patient's electrocardiographic status during surgery is determined based on the pre-processed electrocardiogram signals;

[0019] The patient's electroencephalogram (EEG) status during surgery is determined based on the preprocessed EEG signals;

[0020] The vital signs include electromyography (EMG), electrocardiography (ECG), and electroencephalography (EEG).

[0021] Preferably, the vital sign monitoring module determines the patient's electromyographic state during surgery based on the preprocessed electromyographic signals, including:

[0022] Feature extraction is performed on the preprocessed electromyography (EMG) signal to obtain key features, including the root mean square value, absolute mean value, average power frequency, median frequency, and waveform morphology of the EMG signal.

[0023] If the root mean square value and the absolute mean value are at the baseline level, then it is determined to be the muscle resting state;

[0024] If the increase in the root mean square value and the absolute mean value reaches the first preset value, it is determined to be a state of mild muscle contraction.

[0025] If the increase in the root mean square value and the absolute mean value reaches the second preset amplitude value, it is determined to be a state of severe muscle contraction, where the second preset amplitude value is greater than the first preset amplitude value.

[0026] If the average power frequency and median frequency begin to decrease, it is determined to be an early stage of muscle fatigue.

[0027] If the average power frequency and median frequency continue to decrease, it is determined to be a state of significant muscle fatigue.

[0028] Determine whether the muscle nerve is abnormal based on the waveform morphology;

[0029] When both the muscle resting state and the early muscle fatigue state are met simultaneously, the electromyographic state is determined to be the normal electromyographic state.

[0030] Otherwise, the electromyographic state is determined to be abnormal.

[0031] Preferably, the vital signs monitoring module determines the patient's electrocardiographic status during surgery based on the preprocessed electrocardiogram signal, including:

[0032] Determine the real-time value of each parameter type in the preprocessed electrocardiogram signal;

[0033] Set the standard value range for each parameter type;

[0034] The real-time value is matched with the corresponding standard value range. If the real-time value is within the standard value range, the patient's electrocardiogram status during the operation is determined to be normal.

[0035] If the real-time value corresponding to one or more parameter types is not within the range of the standard value, then the patient's electrocardiogram status during the operation is determined to be an abnormal electrocardiogram status.

[0036] Preferably, the vital signs monitoring module determines the patient's electroencephalographic state during surgery based on the preprocessed electroencephalogram (EEG) signals, including:

[0037] The frequency and power values ​​of each rhythm are determined based on the preprocessed EEG signals.

[0038] The frequency and power values ​​are analyzed to determine whether the patient's EEG state during the surgery is abnormal.

[0039] Preferably, the vital signs monitoring module determines whether the patient is in a critical state based on the vital signs status. If the patient is in a critical state, the module determines the patient's critical level based on the vital signs status, including:

[0040] If the electromyography (EMG) status, the electrocardiogram (ECG) status, and the electroencephalogram (EEG) status are all in normal condition, then the patient is determined to be in a normal state.

[0041] Otherwise, if the patient is determined to be in a critical state, the number of abnormalities in the patient's electromyography, electrocardiography and electroencephalography are obtained when the patient is in a critical state, and the severity of the critical state is determined based on the number of abnormalities.

[0042] If the number of anomalies is 1, then the crisis level is determined to be low crisis level;

[0043] If the number of anomalies is 2, then the crisis level is determined to be medium-critical.

[0044] If the number of anomalies is 3, then the crisis level is determined to be high-risk.

[0045] Preferably, the surgical monitoring module determines the patient's surgical status during the procedure based on the preprocessed surgical electrical signals, including:

[0046] The surgical electrical signals include the current, voltage, and power values ​​of the surgical electrosurgical equipment;

[0047] Set safety parameters for the surgical mode, which includes cutting mode and coagulation mode;

[0048] Determine the surgical mode for the current surgery and select the corresponding safety parameters based on the surgical mode.

[0049] The surgical electrical signal is compared with the selected safety parameters. If the surgical electrical signal is within the range of the selected safety parameters, the surgical status of the patient during the operation is determined to be a normal surgical status.

[0050] If the surgical electrical signal is not within the selected safe parameter range, the patient's surgical status during the procedure is determined to be an abnormal surgical status.

[0051] Preferably, the surgical monitoring module determines whether to issue an alarm based on the surgical status; if an alarm is to be issued, it generates an alarm command, including:

[0052] If the surgical status is normal, no alarm will be triggered.

[0053] If the surgical status is abnormal, an alarm will be triggered and an alarm command will be generated.

[0054] Preferably, the monitoring and alarm module provides audible and visual alarms based on the hazard level and alarm command, including:

[0055] When the alarm command is received, an audible and visual alarm will be triggered;

[0056] If the alarm command is not received, the hazard level is received, and an audible and visual alarm is triggered according to the hazard level.

[0057] If the critical level is low, then a light alarm will be triggered;

[0058] If the critical level is medium or high, an audible and visual alarm will be triggered.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] This invention can simultaneously collect and fuse multiple modalities of electrogenic signals, such as electromyography, electrocardiography, electroencephalography, and surgical electrical signals during orthopedic surgery. It provides multi-dimensional data for assessing the patient's condition during surgery, offers comprehensive and accurate monitoring information for the surgical process, and provides doctors with more intuitive and accurate surgical guidance, thus helping to improve the success rate and safety of surgery.

[0061] By employing advanced wireless transmission technology and data fusion algorithms, real-time acquisition, transmission, and processing of multimodal electrical signals are achieved, avoiding the inconvenience and interference of traditional wired monitoring methods, while improving the accuracy and reliability of monitoring. Real-time monitoring and alarming of patient vital signs and surgical status can effectively prevent crisis events. For example, issuing an alarm before arrhythmia or nerve damage occurs can buy medical staff time to intervene and ensure the patient's safety. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0063] Figure 1 This is a functional block diagram of a real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery according to the present invention. Detailed Implementation

[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0065] like Figure 1 As shown, the present invention provides a real-time monitoring system for multimodal electrophysiological signal fusion in orthopedic surgery, comprising: a data acquisition module configured to acquire real-time signal data during the patient's surgical process, wherein the real-time signal data includes electromyography signals, electrocardiogram signals, electroencephalogram signals, and surgical electrical signals.

[0066] The vital signs monitoring module is configured to preprocess the real-time signal data, determine the patient's vital signs status during the operation based on the preprocessed electromyography, electrocardiography, and electroencephalography signals, and determine whether the patient is in a critical state based on the vital signs status. If the patient is in a critical state, the module determines the patient's critical level based on the vital signs status.

[0067] The surgical monitoring module is configured to determine the surgical status of the patient during the operation based on the preprocessed surgical electrical signal, and to determine whether to issue an alarm based on the surgical status. If an alarm is issued, an alarm command is generated. The surgical status includes abnormal surgical status and normal surgical status.

[0068] The monitoring and alarm module is configured to trigger an audible and visual alarm based on the severity level and alarm command.

[0069] This invention achieves comprehensive, real-time monitoring of the patient's surgical process by integrating multimodal electrophysiological signals. First, the data acquisition module efficiently collects electromyography (EMG), electrocardiography (ECG), electroencephalography (EEG), and surgical electrical signals, encompassing key physiological information during the procedure. Second, the vital signs monitoring module preprocesses these signals to accurately assess the patient's vital signs and, in critical situations, further determine the severity level, providing timely information to the physician. Simultaneously, the surgical monitoring module assesses the surgical status based on the surgical electrical signals and generates alarm commands when abnormalities occur, effectively improving surgical safety. Finally, the monitoring alarm module uses audible and visual alarms to promptly communicate the severity level and alarm commands to medical staff, ensuring the smooth progress of the surgery and the patient's safety. This system not only improves the accuracy and efficiency of orthopedic surgical monitoring but also further enhances patient safety and postoperative recovery.

[0070] In some embodiments of this application, the data acquisition module includes: an EMG sensor disposed in the patient's surgical area for real-time detection of the patient's electromyography (EMG) signals; an ECG electrode disposed in the patient's chest for real-time detection of the patient's electrocardiogram (ECG) signals; an EEG electrode disposed in the patient's head for real-time detection of the patient's electroencephalogram (EEG) signals; and a surgical electrical signal sensor disposed on the surgical electrosurgical equipment for detecting surgical electrical signals during the patient's surgical procedure.

[0071] Understandably, this sophisticated sensor configuration enables precise acquisition of multimodal electrophysiological signals. EMG sensors accurately capture minute muscle movements in the surgical area, reflecting the patient's muscle state; ECG electrodes monitor cardiac activity in real time, ensuring stable cardiac function during surgery; EEG electrodes provide crucial information about the patient's level of consciousness by detecting brain waves; and surgical electrical signal sensors are directly linked to the surgical procedure itself, providing immediate feedback on the use of electrosurgical equipment during the operation. This meticulous sensor layout not only improves the accuracy and reliability of signal acquisition but also provides a solid foundation for subsequent vital sign and surgical monitoring.

[0072] In some embodiments of this application, the vital sign monitoring module preprocesses the real-time signal data and determines the patient's vital sign status during surgery based on the preprocessed electromyography (EMG), electrocardiography (ECG), and electroencephalography (EEG) signals. This includes: preprocessing the real-time signal data, which includes signal synchronization processing, filtering and noise reduction processing, baseline correction processing, and signal standardization processing; determining the patient's electromyography (EMG) status during surgery based on the preprocessed EMG signals; determining the patient's electrocardiography (ECG) status during surgery based on the preprocessed ECG signals; and determining the patient's electroencephalography (EEG) status during surgery based on the preprocessed EEG signals. The vital sign status includes EMG status, ECG status, and EEG status.

[0073] Understandably, by performing a series of preprocessing steps on real-time signal data, signal quality is effectively improved, providing a strong guarantee for the accurate judgment of subsequent vital signs. Signal synchronization processing ensures that signals from different sensors are aligned in time, facilitating comprehensive analysis; filtering and denoising effectively removes interference components from the signal, improving the signal-to-noise ratio; baseline correction corrects baseline drift, making the signal more stable; and signal standardization allows signals collected by different sensors to be compared and analyzed on the same scale. Based on these preprocessed signals, the vital signs monitoring module can accurately determine the patient's electromyography (EMG), electrocardiogram (ECG), and electroencephalogram (EEG) status, providing doctors with comprehensive patient vital signs information and helping them make more precise decisions during surgery.

[0074] In some embodiments of this application, the vital sign monitoring module determines the electromyographic state of the patient during surgery based on the preprocessed electromyographic signal, including: extracting features from the preprocessed electromyographic signal to obtain key features, the key features including the root mean square value, absolute mean, average power frequency, median frequency, and waveform morphology of the electromyographic signal; if the root mean square value and absolute mean are at the baseline level, it is determined to be a muscle resting state; if the increase in the root mean square value and absolute mean reaches a first preset amplitude value, it is determined to be a mild muscle contraction state; if the increase in the root mean square value and absolute mean reaches a second preset amplitude value, it is determined to be a severe muscle contraction state, the second preset amplitude value being greater than the first preset amplitude value; if the average power frequency and median frequency begin to decrease, it is determined to be an early muscle fatigue state; if the average power frequency and median frequency continue to decrease, it is determined to be a significant muscle fatigue state; determining whether the muscle nerve is abnormal based on the waveform morphology; when both the muscle resting state and the early muscle fatigue state are met simultaneously, the electromyographic state is determined to be a normal electromyographic state; otherwise, the electromyographic state is determined to be an abnormal electromyographic state.

[0075] Understandably, by extracting features from preprocessed electromyography (EMG) signals and judging muscle activity and fatigue levels based on a series of preset criteria, the vital signs monitoring module can achieve a detailed classification of the patient's EMG state. This detailed classification not only helps doctors understand the patient's muscle condition in a timely manner but also provides timely warnings when EMG abnormalities occur, avoiding potential risks. For example, in a state of mild muscle contraction, doctors can determine whether the patient's muscles are tense, thereby adjusting surgical procedures or providing appropriate relaxation guidance; in a state of significant muscle fatigue, doctors can take timely measures to prevent surgical risks caused by muscle fatigue. Furthermore, by analyzing waveform morphology, it is possible to further determine whether there are abnormalities in the muscle nerves, providing doctors with more comprehensive patient EMG information.

[0076] In some embodiments of this application, the vital signs monitoring module determines the patient's electrocardiogram (ECG) status during surgery based on the preprocessed ECG signal, including: determining the real-time value of each parameter type in the preprocessed ECG signal; setting a standard value range for each parameter type; matching the real-time value with the corresponding standard value range; if the real-time value is within the standard value range, then the patient's ECG status during surgery is determined to be a normal ECG status; if there is a real-time value corresponding to one or more parameter types that is not within the standard value range, then the patient's ECG status during surgery is determined to be an abnormal ECG status.

[0077] In this embodiment, the electrocardiogram (ECG) parameters of the ECG signal include: heart rate (HR), PR interval, QRS width, ST segment deviation, and T wave amplitude.

[0078] The standard value ranges for each parameter type are as follows: heart rate (HR) is 60-100 beats / minute; PR interval is 0.12-0.20 seconds; QRS width is 0.06-0.10 seconds; ST segment deviation is ≤0.1mV elevation and ≤0.05mV depression; and T wave amplitude is ≥1 / 10 of R wave amplitude.

[0079] Understandably, the vital signs monitoring module can quickly and accurately determine a patient's ECG status by analyzing the preprocessed ECG signals in real time. By setting standard value ranges for each ECG parameter type and matching real-time values ​​with these standard values, the system can automatically classify the patient's ECG status as normal or abnormal. This automated ECG status monitoring not only improves the efficiency of surgical monitoring but also quickly issues alarms when ECG abnormalities occur, enabling doctors to take immediate countermeasures and effectively reduce surgical risks. Furthermore, this technical solution also considers the complexity and diversity of ECG signals, ensuring the accuracy and reliability of ECG status assessment by simultaneously monitoring multiple parameter types.

[0080] In some embodiments of this application, the vital signs monitoring module determines the patient's brain state during surgery based on the preprocessed electroencephalogram (EEG) signal, including: determining the frequency and power values ​​of each rhythm based on the preprocessed EEG signal; and analyzing the frequency and power values ​​to determine whether the patient's brain state during surgery is an abnormal brain state.

[0081] In this embodiment, the vital signs monitoring module uses Fast Fourier Transform (FFT) to calculate the frequency and power values ​​of each brainwave rhythm (δ wave, θ wave, α wave, β wave, γ wave) on the preprocessed EEG signal: δ wave (0.5-4Hz): average frequency value is 2Hz, power value is 80μV 2 Theta wave (4-8Hz): average frequency 6Hz, power 60μV. 2 Alpha wave (8-13Hz): average frequency 10Hz, power 120μV. 2 Beta wave (13-30Hz): average frequency 20Hz, power 40μV. 2 Gamma wave (30-100Hz): average frequency 40Hz, power 20μV. 2 .

[0082] The vital signs monitoring module comprehensively analyzes frequency and power values ​​based on medical standards and the patient's preoperative baseline EEG data. For example, normal EEG characteristics: Under the depth of anesthesia for this surgery, alpha wave power should normally dominate (approximately 50-70%), while beta wave power should be relatively stable. Current data analysis: Calculating the power proportion of each rhythm reveals that the alpha wave power proportion is only 35%, the beta wave power proportion has risen to 30%, and the sum of delta wave and theta wave power proportions exceeds 30%. According to pre-set abnormality judgment rules, when alpha wave power is significantly reduced, beta wave power is abnormally increased, and the sum of delta / theta wave power exceeds the normal range, it is judged as an abnormal EEG state. Therefore, the vital signs monitoring module determines that the patient's current EEG state during the surgical procedure is an abnormal EEG state.

[0083] Understandably, the vital signs monitoring module can identify the frequency and power characteristics of various rhythms through in-depth analysis of preprocessed EEG signals, which are key indicators for assessing EEG status. Through meticulous analysis of these characteristics, the system can sensitively detect any abnormal changes in EEG activity. Once the frequency or power value in the EEG signal deviates from the normal range, the system can quickly determine that the patient is in an abnormal EEG state. This EEG-based monitoring not only improves the accuracy of surgical monitoring but also provides immediate warnings to doctors of EEG abnormalities, enabling them to react quickly, adjust surgical strategies, or take necessary emergency measures, thereby maximizing patient safety during surgery.

[0084] In some embodiments of this application, the vital signs monitoring module determines whether the patient is in a critical state based on the vital signs status. If the patient is in a critical state, the module determines the severity level of the patient's condition based on the vital signs status, including: if the electromyography (EMG) status, electrocardiogram (ECG) status, and electroencephalogram (EEG) status are all normal, the patient is determined to be in a normal state; otherwise, the patient is determined to be in a critical state, and the number of abnormalities in the EMG, ECG, and EEG statuses when the patient is in a critical state is obtained, and the severity level is determined based on the number of abnormalities; if the number of abnormalities is 1, the severity level is determined to be low; if the number of abnormalities is 2, the severity level is determined to be moderate; if the number of abnormalities is 3, the severity level is determined to be high.

[0085] Understandably, the vital signs monitoring module comprehensively considers the patient's electromyography (EMG), electrocardiogram (ECG), and electroencephalogram (EEG) states, using logical judgment to determine whether the patient is in a critical state. This comprehensive assessment method is more complete and accurate than monitoring a single indicator, and can more comprehensively reflect the patient's overall physiological condition. After determining that the patient is in a critical state, the system further classifies the severity level based on the number of abnormalities. This helps doctors take appropriate measures according to the different severity levels. At low severity levels, doctors may only need to make simple adjustments or observations; while at medium or high severity levels, doctors need to take immediate emergency measures to ensure the patient's safety.

[0086] In some embodiments of this application, the surgical monitoring module determines the patient's surgical status during surgery based on preprocessed surgical electrical signals, including: the surgical electrical signals including the current, voltage, and power values ​​of the surgical electrosurgical equipment; setting safety parameters for the surgical mode, including a cutting mode and a coagulation mode; determining the current surgical mode and selecting corresponding safety parameters based on the current surgical mode; comparing the surgical electrical signals with the selected safety parameters; if the surgical electrical signals are within the selected safety parameter range, then the patient's surgical status is determined to be a normal surgical status; if the surgical electrical signals are not within the selected safety parameter range, then the patient's surgical status is determined to be an abnormal surgical status.

[0087] In this embodiment, safety parameter ranges are set for the cutting mode and coagulation mode respectively, according to the electrosurgical device instruction manual and clinical safety standards:

[0088] Cutting mode: The safe range for current is 0.5-2A; the safe range for voltage is 100-500V; and the safe range for power is 20-100W.

[0089] Coagulation mode: The safe range for current is 0.1-0.5A; the safe range for voltage is 500-1500V; and the safe range for power is 10-50W.

[0090] For example, during surgery, the surgeon selects the electrosurgical unit in cutting mode to incise the skin and separate tissues. After recognizing the current surgical mode as cutting mode, the surgical monitoring module retrieves and selects the corresponding safety parameters (current 0.5-2A, voltage 100-500V, power 20-100W). At a certain moment, the collected current value is 1.8A, the voltage value is 300V, and the calculated power value is 540W.

[0091] The collected current value of 1.8A, voltage value of 300V, and power value of 540W were compared with the safety parameters of the cutting mode:

[0092] The current value is 1.8A, within the range of 0.5-2A;

[0093] The voltage value is 300V, within the range of 100-500V;

[0094] The power rating of 540W exceeds the 20-100W range.

[0095] Since the power value is outside the selected safety parameter range, the surgical monitoring module determines the patient's surgical status as an abnormal surgical status according to the judgment rules.

[0096] Understandably, the surgical monitoring module can monitor key parameters such as current, voltage, and power of the electrosurgical equipment in real time and compare them with preset safety parameters to determine whether the surgical status is normal. This method ensures safety during the surgical process; if the surgical electrical signal exceeds the safe range, the system can immediately issue an alarm to alert the doctor to changes in the surgical status. This real-time monitoring and early warning mechanism not only helps doctors to promptly detect and handle abnormalities during surgery but also improves the accuracy and safety of the surgery and reduces surgical risks.

[0097] In some embodiments of this application, the surgical monitoring module determines whether to issue an alarm based on the surgical status. If an alarm is to be issued, an alarm command is generated, including: if the surgical status is a normal surgical status, it is determined not to issue an alarm; if the surgical status is an abnormal surgical status, it is determined to issue an alarm and an alarm command is generated.

[0098] Understandably, the surgical monitoring module can intelligently determine the surgical status and decide whether to trigger an alarm mechanism based on the assessment. When the surgical status is normal, the system remains silent to avoid unnecessary interference; however, when the surgical status is abnormal, the system responds quickly, generating an alarm command to alert the doctor. This intelligent alarm decision-making not only ensures the smooth progress of the surgery but also provides timely and effective safety warnings at critical moments, offering strong support to doctors.

[0099] In some embodiments of this application, the monitoring and alarm module performs audible and visual alarms based on the urgency level and alarm command, including: when the alarm command is received, performing an audible and visual alarm; when the alarm command is not received, receiving the urgency level and performing an audible and visual alarm based on the urgency level; if the urgency level is low, performing a visual alarm; if the urgency level is medium or high, performing an audible and visual alarm.

[0100] Understandably, the monitoring and alarm module can flexibly activate audible and visual alarms based on the severity level and alarm commands. Upon receiving an alarm command, it immediately activates the audible and visual alarms to draw the doctor's immediate attention. Even without receiving an alarm command, the monitoring and alarm module can still issue warnings based on the severity level, achieving multi-layered safety protection. At low-risk levels, only a visual alarm is activated, avoiding excessive interference with the surgical environment; while at medium- or high-risk levels, both audible and visual alarms are activated, ensuring that the doctor can quickly perceive the situation and take appropriate measures. This tiered alarm strategy ensures the safety of the surgery while minimizing interference with the surgical process, providing strong support for the smooth progress of the operation.

[0101] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery, characterized in that, include: The data acquisition module is configured to acquire real-time signal data during the patient's surgical procedure, including electromyography (EMG) signals, electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and surgical electrical signals. The vital signs monitoring module is configured to preprocess the real-time signal data, determine the patient's vital signs status during the operation based on the preprocessed electromyography (EMG), electrocardiography (ECG), and electroencephalography (EEG) signals, and determine whether the patient is in a critical state based on the vital signs status. If the patient is in a critical state, the severity level of the critical state is determined based on the vital signs status. If the EMG status, ECG status, and EEG status are all normal, the patient is determined to be in a normal state. Otherwise, if the patient is determined to be in a critical state, the number of abnormalities in the patient's electromyography, electrocardiography and electroencephalography are obtained when the patient is in a critical state, and the severity of the critical state is determined based on the number of abnormalities. If the number of anomalies is 1, the crisis level is determined to be low; if the number of anomalies is 2, the crisis level is determined to be medium; if the number of anomalies is 3, the crisis level is determined to be high. The surgical monitoring module is configured to determine the surgical status of the patient during the operation based on the preprocessed surgical electrical signal, and to determine whether to trigger an alarm based on the surgical status. If an alarm is triggered, an alarm command is generated. The surgical status includes abnormal surgical status and normal surgical status. The monitoring and alarm module is configured to trigger an audible and visual alarm based on the severity level and alarm command. The surgical monitoring module determines the patient's surgical status during surgery based on preprocessed surgical electrical signals, including: the surgical electrical signals comprising the current, voltage, and power values ​​of the surgical electrosurgical equipment; setting safety parameters for the surgical mode, including cutting mode and coagulation mode; determining the current surgical mode and selecting corresponding safety parameters based on the current surgical mode; comparing the surgical electrical signals with the selected safety parameters; if the surgical electrical signals are within the selected safety parameter range, the patient's surgical status is determined to be normal; if the surgical electrical signals are outside the selected safety parameter range, the patient's surgical status is determined to be abnormal.

2. The real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery according to claim 1, characterized in that, The data acquisition module includes: An EMG sensor is placed in the patient's surgical area to detect the patient's electromyography signals in real time. ECG electrodes are placed on the patient's chest to detect the patient's electrocardiogram signals in real time. EEG electrodes are placed on the patient's head to detect the patient's electroencephalogram (EEG) signals in real time. Surgical electrical signal sensors are installed on surgical electrosurgical equipment to detect surgical electrical signals during patient surgery.

3. The real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery according to claim 1, characterized in that, The vital signs monitoring module preprocesses the real-time signal data and determines the patient's vital signs during surgery based on the preprocessed electromyography (EMG), electrocardiography (ECG), and electroencephalography (EEG) signals, including: The real-time signal data is preprocessed, including signal synchronization processing, filtering and noise reduction processing, baseline correction processing, and signal normalization processing. The patient's electromyographic status during surgery was determined based on the pre-processed electromyographic signals; The patient's electrocardiographic status during surgery is determined based on the pre-processed electrocardiogram signals; The patient's electroencephalogram (EEG) status during surgery is determined based on the preprocessed EEG signals; The vital signs include electromyography (EMG), electrocardiography (ECG), and electroencephalography (EEG).

4. The real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery according to claim 3, characterized in that, The vital signs monitoring module determines the patient's electromyography (EMG) status during surgery based on the preprocessed EMG signals, including: Feature extraction is performed on the preprocessed electromyography (EMG) signal to obtain key features, including the root mean square value, absolute mean value, average power frequency, median frequency, and waveform morphology of the EMG signal. If the root mean square value and the absolute mean value are at the baseline level, then it is determined to be the muscle resting state; If the increase in the root mean square value and the absolute mean value reaches the first preset value, it is determined to be a state of mild muscle contraction. If the increase in the root mean square value and the absolute average value reaches the second preset amplitude value, it is determined to be a state of severe muscle contraction, where the second preset amplitude value is greater than the first preset amplitude value. If the average power frequency and median frequency begin to decrease, it is determined to be an early stage of muscle fatigue. If the average power frequency and median frequency continue to decrease, it is determined to be a state of significant muscle fatigue. Determine whether the muscle nerve is abnormal based on the waveform morphology; When both the muscle resting state and the early muscle fatigue state are met simultaneously, the electromyographic state is determined to be the normal electromyographic state. Otherwise, the electromyographic state is determined to be abnormal.

5. The real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery according to claim 4, characterized in that, The vital signs monitoring module determines the patient's electrocardiographic status during surgery based on the preprocessed electrocardiogram signal, including: Determine the real-time value of each parameter type in the preprocessed electrocardiogram signal; Set the standard value range for each parameter type; The real-time value is matched with the corresponding standard value range. If the real-time value is within the standard value range, the patient's electrocardiogram status during the operation is determined to be normal. If the real-time value corresponding to one or more parameter types is not within the range of the standard value, then the patient's electrocardiogram status during the operation is determined to be an abnormal electrocardiogram status.

6. The real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery according to claim 5, characterized in that, The vital signs monitoring module determines the patient's electroencephalographic (EEG) status during surgery based on the preprocessed EEG signals, including: The frequency and power values ​​of each rhythm are determined based on the preprocessed EEG signals. The frequency and power values ​​are analyzed to determine whether the patient's EEG state during the surgery is abnormal.

7. The real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery according to claim 1, characterized in that, The surgical monitoring module determines whether to issue an alarm based on the surgical status. If an alarm is to be issued, an alarm command is generated, including: If the surgical status is normal, no alarm will be triggered. If the surgical status is abnormal, an alarm will be triggered and an alarm command will be generated.

8. The real-time monitoring system for multimodal electrogenic signal fusion in orthopedic surgery according to claim 7, characterized in that, The monitoring and alarm module provides audible and visual alarms based on the severity level and alarm command, including: When the alarm command is received, an audible and visual alarm will be triggered; If the alarm command is not received, the hazard level is received, and an audible and visual alarm is triggered according to the hazard level. If the critical level is low, then a light alarm will be triggered; If the critical level is medium or high, an audible and visual alarm will be triggered.

Images