Postoperative pain degree sensor and ovum extraction operation pain monitoring system

Abstract

The invention discloses a postoperative pain degree sensor and an ovum extraction operation pain monitoring system, and belongs to the field of postoperative health monitoring and intelligent nursing equipment. The sensor comprises a flexible substrate, and a skin electric response sensing unit, a temperature sensing unit, a myoelectricity sensing unit, a micro-pressure sensing unit, a signal processing circuit and a wireless communication module which are integrated on the flexible substrate, the monitoring system comprises an edge processing module, a pain index evaluation module, an interaction terminal and a data recording module. The system evaluates the postoperative pain level based on the multi-channel physiological signals, real-time quantification and alarm prompt of the pain state are achieved, and the postoperative nursing response efficiency is improved. The scheme is suitable for assisting pain management after reproductive ovum extraction, and has the advantages of high sensitivity, low delay, structural flexibility and system closed loop.

Description

Technical Field

[0001] This invention relates to the field of medical devices and postoperative health monitoring technology, and more specifically, to a postoperative pain level sensor and an egg retrieval surgery pain monitoring system. Background Technology

[0002] Postoperative pain monitoring is an indispensable and crucial aspect of clinical practice in surgery, gynecology, and assisted reproductive technologies. Especially in the later stages of minimally invasive surgery, accurately identifying the patient's pain status is of great significance for guiding analgesic interventions, preventing stress responses, and improving the recovery experience. Pain is essentially a subjective experience, but its occurrence is often accompanied by a series of quantifiable physiological changes, such as increased skin conductivity, enhanced muscle contraction, fluctuations in skin temperature, and responses to shallow abdominal pressure. Therefore, collecting relevant physiological signals and establishing models for pain prediction has become one of the directions for research and engineering implementation.

[0003] Currently, postoperative pain assessment primarily relies on subjective rating scales, such as the Visual Analogue Scale (VAS) or the Numerical Rating Scale (NRS). These methods depend on patient self-reporting and nursing staff observation, which suffers from drawbacks such as information lag and susceptibility to emotional and verbal influences. Furthermore, while some studies have attempted to incorporate single-parameter physiological signals such as skin conductance and electromyography (EMG) for auxiliary assessment, the lack of flexible integrated structures in monitoring systems limits signal acquisition accuracy. Moreover, most systems still rely on wired connections and localized single-channel processing, making it difficult to achieve clinically usable continuous wear and real-time response.

[0004] Therefore, there is an urgent need to develop a pain monitoring device that can be worn post-surgery, non-invasively sense multiple pain-related physiological parameters, and has high signal stability, processing integration, and real-time transmission capabilities, in order to solve the technical problems of low integration, delayed feedback response, and limited recognition accuracy of current physiological monitoring methods. Summary of the Invention

[0005] To address the problems of existing technologies, embodiments of the present invention provide a postoperative pain level sensor and an egg retrieval surgery pain monitoring system. The technical solution is as follows:

[0006] On the one hand, a postoperative pain level sensor is provided, including:

[0007] A flexible base layer, the flexible base layer being adapted to adhere to the patient's skin surface;

[0008] Multiple physiological signal sensing units disposed on the flexible substrate are used to collect multimodal physiological signals reflecting the degree of postoperative pain in patients. The physiological signal sensing units include skin electroreception signal sensing units, temperature signal sensing units, electromyography signal sensing units, and pressure signal sensing units.

[0009] The signal processing chip is used to condition, convert analog signals to digital signals, filter signals and extract features from the analog signals output by the physiological signal sensing unit.

[0010] A wireless communication module, connected to the signal processing chip, is used to send the processed physiological characteristic data to an external monitoring device;

[0011] The physiological signal sensing unit, the signal processing chip, and the communication module are all encapsulated in the flexible substrate layer to form an adhesive sensing device.

[0012] Furthermore, the skin electrical signal sensing unit includes a pair of silver chloride non-polarized electrodes mounted on an elastic base, with conductive gel disposed between the electrodes. The electrode spacing is 25 mm, and the electrodes are connected to a pre-amplifier via flexible conductive lines with a bandwidth of 0.05 Hz to 1 Hz.

[0013] Furthermore, the temperature signal sensing unit is an NTC type infrared thermistor temperature sensor, which is located in the center of the flexible substrate layer and is attached to the patient's surgical area skin surface through a flexible thermal pad. The sensor surface is provided with a transparent polyurethane waterproof layer.

[0014] Furthermore, the electromyography signal sensing unit includes two surface electromyography electrodes and a differential amplifier. The electrode material is a silver-silver chloride composite layer, the electrode diameter is 8 mm, the spacing is 20 to 30 mm, the sampling frequency is 500 Hz, and the bandwidth is 20 Hz to 300 Hz.

[0015] Furthermore, the pressure signal sensing unit is a capacitive flexible pressure sensor, including upper and lower electrodes and a middle PDMS and carbon nanotube composite elastic dielectric layer. The pressure sensing unit is disposed in the lower central region of the flexible substrate layer, with a sensing range of 0 to 500 Pa and a sensitivity better than 0.5 Pa per second.

[0016] On the other hand, a pain monitoring system for egg retrieval surgery is provided, including:

[0017] The postoperative pain level sensor is used to collect postoperative patient skin electrical signals, temperature signals, electromyographic signals and pressure signals;

[0018] An edge processing module is wirelessly connected to the postoperative pain level sensor. The edge processing module is used to filter, normalize, synchronize time, and extract features from the received multimodal physiological signals.

[0019] The pain index assessment module, connected to the edge processing module, is used to construct a feature vector based on the feature data and output a pain index score based on a preset neural network model. The pain index is a continuous value between 0 and 10.

[0020] The feedback interaction module includes a display terminal and an alarm device, used to display the pain index score and its changing trend, and to trigger an alarm prompt when the score exceeds a preset threshold condition;

[0021] The data recording module is used to store the pain index score, alarm events and nursing response information, and supports uploading the data to the hospital information system or local database for archiving.

[0022] Furthermore, the edge processing module is equipped with four analog input interfaces, which are used to receive skin conductance, temperature, electromyography and pressure signals respectively. The module has a built-in feature extraction program to extract parameters such as the signal average value, slope of change, fluctuation amplitude and variance of each channel.

[0023] Furthermore, the pain index assessment module employs a combined model that integrates long short-term memory neural networks and convolutional neural networks to model the time-series features and inter-channel spatial features of physiological signals, and supports individualized model parameter calibration based on the patient's preoperative resting data.

[0024] Furthermore, the feedback interaction module is equipped with alarm judgment rules. When the pain index score is greater than or equal to 7 and lasts for more than 180 seconds, the system triggers an audible and visual alarm and pops up an alarm prompt interface.

[0025] Furthermore, the data recording module connects with the hospital information system via a RESTful interface, and supports storing scoring timestamps, alarm response times, nursing treatment methods, and historical scoring curves.

[0026] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows:

[0027] This invention provides a postoperative pain level sensor and an egg retrieval surgery pain monitoring system. Through multimodal signal fusion and intelligent algorithm analysis, it constructs a postoperative pain intelligent recognition platform integrating perception, assessment, feedback, and recording, which has the following beneficial effects:

[0028] First, by setting up four highly correlated physiological signal sensing modules—skin conductance response, temperature, electromyography, and micropressure—this invention enables multi-dimensional monitoring of postoperative pain, which is more objective, quantifiable, and has real-time response advantages compared to the traditional VAS score.

[0029] Secondly, the present invention adopts a flexible adhesive structure that is suitable for different parts of the human body, such as the shoulder or abdomen. It has the characteristics of strong biocompatibility, comfortable wearing, and stable signal. It can be continuously used without affecting postoperative rest and nursing operations.

[0030] Third, the edge processing and intelligent assessment module integrated in this invention has high timeliness, and can issue warnings at the initial stage of pain changes, guiding nursing staff to adjust analgesia strategies in a timely manner, improving the speed of postoperative care response, and reducing patient suffering.

[0031] In addition, the system of this invention has an open data structure, which facilitates integration with hospital information systems, enabling automatic archiving of postoperative pain data and analysis of nursing quality, and promoting the refinement and standardization of postoperative management. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of a postoperative pain level sensor according to Embodiment 1 of the present invention;

[0034] Figure 2 This is a schematic diagram of an egg retrieval surgery pain monitoring system according to Embodiment 2 of the present invention;

[0035] Figure 3 This is a flowchart of a post-egg retrieval pain monitoring system according to Embodiment 2 of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0037] This embodiment provides a multimodal physiological signal sensor specifically designed for postoperative pain assessment. It can be attached to the skin surface and uses multiple sensing units to collaboratively collect physiological signals reflecting the degree of postoperative pain, achieving high-precision, non-invasive, and continuous monitoring. This sensor is suitable for early pain monitoring after egg retrieval surgery, and is particularly suitable for the postoperative stage when patients have limited self-expression after regaining consciousness.

[0038] like Figure 1 As shown, the postoperative pain level sensor includes:

[0039] Flexible substrate

[0040] Multiple sensing units: skin conductance sensing unit, temperature sensing unit, electromyography (EMG) signal sensing unit, and micro-pressure sensing unit.

[0041] Flexible conductive circuit

[0042] signal processing chip

[0043] Analog-to-digital conversion module, filtering and feature extraction module

[0044] BLE low-power wireless communication module

[0045] Medical-grade encapsulation layer and adhesive layer

[0046] The above structure constitutes a complete flexible adhesive sensing device, which has the characteristics of strong wearability, stable signal and low power consumption, and is suitable for use during postoperative recovery.

[0047] I. Skin Electroreactivity Sensing Unit

[0048] The skin conductance sensing unit is used to detect changes in skin conductivity caused by postoperative sympathetic nerve activity, thereby determining the postoperative pain-related autonomic nerve activation status. Its basic principle is that when the sympathetic nervous system is stimulated by pain, it triggers increased sweat gland activity, leading to an increase in the conductivity of the skin surface. This change can be detected by electrodes on the skin surface and converted into a voltage signal.

[0049] In this embodiment, the unit specifically includes the following components:

[0050] 1. Surface electrode structure

[0051] The electrodes are a pair of paired, non-polarized silver chloride (Ag / AgCl) electrodes, characterized by low noise, low impedance, and high electrochemical stability. The electrodes are 10 mm in diameter and 25 mm apart, arranged in a ring to increase skin contact area and reduce displacement interference. Each electrode is connected to the preamplifier circuit via a flexible conductive material (such as printed silver paste lines).

[0052] The electrode material is formed on a flexible conductive cloth or polyimide carrier by physical vapor deposition and treated with a biocompatible coating (such as polyurethane) to ensure no irritation reaction when it is attached to human skin for a long time.

[0053] 2. Electrode attachment method

[0054] To ensure stable contact and improve signal stability, a medical conductive gel layer is placed between the electrode and the skin. The gel layer is 0.5 to 1 mm thick and has an impedance of less than 5 kΩ, which can effectively reduce the impedance of the stratum corneum of the skin surface and avoid interference.

[0055] The electrode is fixed on a slightly raised elastic base, which is made of hot-pressed flexible silicone rubber and installed on the shoulder or abdominal area of ​​the flexible base layer to ensure micro-pressure contact and prevent the electrode from falling off during use.

[0056] 3. Preamplifier and filter circuit

[0057] The electrode output signal is connected to a low-noise differential preamplifier, which adopts a typical instrumentation amplification structure, with an input impedance higher than 10MΩ, a gain set to 100 times, and a bandwidth controlled in the range of 0.05Hz to 1Hz. It is used to extract the low-frequency slow-varying signal of skin conductance as it changes with sympathetic nerve activity.

[0058] To further improve signal stability, a first- or second-order low-pass filter is connected after the amplifier circuit, with the cutoff frequency set to 5Hz, to remove electromyographic interference and high-frequency noise components.

[0059] 4. Signal Sampling and Feature Parameter Construction

[0060] The filtered analog signal is sampled by an analog-to-digital converter at a sampling frequency of 10Hz and a conversion resolution of 12 bits. The sampled signal is encapsulated into a feature vector in real time, which includes:

[0061] Current skin conductivity value;

[0062] The slope (i.e., rate of change) of the conductivity value within the sliding window;

[0063] Extreme value fluctuation range;

[0064] The variance index over a continuous 1 minute.

[0065] The aforementioned features serve as important input parameters for subsequent pain intensity recognition models, and are used to construct pain-related autonomic stress indices.

[0066] 5. Installation location and clinical layout

[0067] In this embodiment, the skin conductance response sensing unit is typically installed on the lateral deltoid muscle of the shoulder, as this area has abundant blood flow, moderate sweat gland density, and is less affected by surgical site activity. Alternatively, a second electrode group can be symmetrically deployed in the lower abdomen for local sympathetic response comparison analysis, depending on clinical needs.

[0068] In summary, the skin conductance sensing unit features miniaturization, high sensitivity, strong anti-interference ability, and good signal stability. It can accurately reflect the changes in sympathetic nerve activity caused by postoperative pain in patients, providing a key physiological input dimension for pain monitoring systems.

[0069] II. Temperature Sensing Unit

[0070] The temperature sensing unit is used to monitor temperature changes on the skin surface of the surgical area, indirectly reflecting postoperative inflammation, tissue response, or microcirculation status. Temperature, as an important auxiliary indicator of pain status, can form a complementary data source with signals such as electrical response, electromyography, and pressure.

[0071] 1. Selection and Structure of Temperature Sensing Elements

[0072] The temperature sensing unit uses an NTC-type thermistor infrared temperature sensor, which has high sensitivity and stable response to changes in ambient temperature. The temperature measurement range is set from 30 to 42 degrees Celsius to accommodate the actual fluctuation range of the skin in the surgical area. The sensor is smaller than 4×4 millimeters and can be integrated into the surface of a flexible substrate.

[0073] The surface of the thermistor sensing element is covered with a transparent waterproof coating (such as a polyurethane microfilm). The coating thickness is no more than 0.1 mm, which balances thermal conductivity and biocompatibility to ensure sensing accuracy and clinical safety.

[0074] 2. Installation location and thermal contact structure

[0075] The temperature sensing unit is located in the central thermal zone of the sensor patch, corresponding to the lower abdomen of the patient on the side of the surgery. This area is close to the surgical area and is stable without excessive movement, making it suitable for collecting the skin temperature of the surgical area.

[0076] To improve temperature response speed, a flexible thermally conductive pad is placed beneath the sensor, ensuring close contact with the skin, reducing air gaps, and improving heat transfer efficiency. The entire temperature acquisition area provides some shielding against external airflow, ensuring measurement stability.

[0077] 3. Signal Processing and Output Characteristics

[0078] The temperature signal is read by the signal conditioning module and converted by a 10-bit or 12-bit ADC before being uploaded. The system is set to a sampling period of once every 5 seconds to continuously collect skin temperature and extract the following feature parameters based on a sliding window:

[0079] Real-time temperature value;

[0080] Rate of change of temperature gradient;

[0081] The maximum temperature fluctuation within three minutes;

[0082] Number of temperature abrupt changes (abrupt events >0.5℃ / 30 seconds).

[0083] These parameters help identify changes in skin blood flow caused by early postoperative infection, local tissue inflammation, or sympathetic abnormalities, and can be used in conjunction with other signals for model analysis.

[0084] III. Electromyographic Signal Sensing Unit

[0085] The electromyography (EMG) signal sensing unit is used to detect changes in muscle tension or involuntary twitching responses in postoperative patients. These are usually caused by painful stimuli, anxiety, or local inflammatory reflexes, and can effectively help assess the degree of pain.

[0086] 1. Surface electromyography electrode structure

[0087] This unit adopts a dual-electrode surface electromyography (EMG) acquisition structure. Each electrode is 8 mm in diameter and made of a silver / silver chloride composite layer. A medical conductive gel pad is provided at the bottom to ensure the quality of electrical contact with the skin surface.

[0088] The electrode spacing is controlled between 20 and 30 millimeters, and the electrodes are fixed to the shoulder area or abdominal muscle area of ​​a flexible substrate. To enhance resistance to motion interference, the electrodes are equipped with micro-buffer layers, and cross-shaped crack buffers are formed by laser cutting to absorb tensile stress.

[0089] 2. Differential Amplification and Filtering Structure

[0090] Two electrode signals are fed into a differential instrumentation amplifier with a bandwidth set between 20 Hz and 300 Hz to extract short-term discharge activity generated during muscle fiber contraction, with voltage amplitudes typically ranging from tens to hundreds of microvolts.

[0091] To remove power frequency interference and high-frequency noise, the amplified signal is processed by a 50Hz notch filter and a 300Hz low-pass filter before being output. The sampling frequency is then set to 500Hz by the ADC conversion, which meets the sampling standard for electromyography signals.

[0092] 3. Feature Extraction and Clinical Value

[0093] The following feature parameters were extracted after electromyographic signal processing:

[0094] Mean electromyographic amplitude (RMS);

[0095] Number of electromyographic triggers (> number of threshold events);

[0096] Distribution of electromyographic fluctuation frequency;

[0097] Total electromyographic energy integral within three minutes.

[0098] The above-mentioned feature values ​​reflect the degree of postoperative muscle tension, the intensity of pain-induced response, and the frequency of abnormal movements. They can be used as sensitive indicators of pain perception and movement discomfort, and can be used as inputs for joint models with data from other channels.

[0099] IV. Micro-pressure sensing unit

[0100] The micro-pressure sensing unit is used to monitor minute changes in mechanical pressure on the skin surface and superficial tissues near the surgical area to identify postoperative stress responses caused by abdominal cramps, postural adjustments, or localized tenderness. Clinically, this signal effectively indicates the intensity of the response in areas of localized pain or discomfort, serving as an important channel for assessing deep pain.

[0101] 1. Pressure sensing structure and materials

[0102] The micro-pressure sensing unit in this embodiment adopts a capacitive flexible pressure sensing structure, which consists of the following parts:

[0103] Upper electrode layer: a flexible conductive thin film, prepared on a polyimide film by magnetron sputtering;

[0104] Pressure-sensitive medium layer: A porous elastic medium is formed by PDMS (polydimethylsiloxane) and carbon nanoparticle composite material with a thickness of 0.3 to 0.5 mm, which has good linear strain response and high resilience;

[0105] Lower electrode layer: symmetrically arranged with the upper electrode to form a parallel capacitor structure;

[0106] Outer encapsulation: The entire structure is encapsulated in a soft silicone protective film by hot pressing to ensure biocompatibility.

[0107] The above structure forms a variable capacitor whose capacitance value changes with the vertical pressure, enabling accurate detection of low-pressure signals in the range of 0 to 500 Pa, with a sensitivity better than 0.5 Pa / bit.

[0108] 2. Installation area and form design

[0109] The micro-pressure sensing unit is integrated into the lower central area of ​​the sensor patch. In actual use, it is attached to the skin surface near the surgical area of ​​the patient's lower abdomen. The area is no more than 2 square centimeters. It has a circular structure and meets the requirements for fitting the curved areas of the skin.

[0110] To reduce the impact of motion interference, a flexible, wave-shaped pre-compression frame is set outside the sensing unit. When the wearer gets up or turns over, the structure itself absorbs most of the stress, avoiding false triggering.

[0111] 3. Signal Reading and Feature Extraction

[0112] The induced capacitance signal is input to a resonant capacitance readout chip and measured using a CDC (capacitance-to-digital converter) method at a sampling frequency of 20Hz. After calibration and linear compensation, the following characteristic quantities are derived from the signal:

[0113] The magnitude of pressure transients;

[0114] The average number of pressure changes per unit time;

[0115] Maximum sustained pressure;

[0116] Sudden pressure change rate.

[0117] This signal is used to detect involuntary abdominal muscle tension, tenderness, coughing or contraction response after surgery, and together with electromyography, temperature and other signals, it characterizes the pain induction pathway.

[0118] V. Signal Processing and Wireless Communication Module

[0119] All raw signals collected by the aforementioned sensing units must be processed before being uniformly output to the monitoring system. Therefore, the postoperative pain level sensor is equipped with an integrated micro-signal processing and wireless communication module to achieve local signal integration, standardization, and remote transmission.

[0120] 1. Signal Conditioning and Analog-to-Digital Conversion

[0121] The module uses a highly integrated microprocessor (such as the Nordic NRF52 series or the ST STM32 ultra-low power series) as its core processor. It has at least 4 channels of analog input interfaces, each equipped with an analog-to-digital converter (ADC) with a resolution of 12 bits or more. The sampling rate is set from 10Hz to 500Hz according to the signal characteristics.

[0122] Each input channel undergoes signal conditioning, including bandpass filtering, notch filtering, hardware gain amplification, and dynamic range compression, to ensure that the output signal has stable amplitude and clear boundaries within the effective frequency band.

[0123] 2. Synchronous encapsulation of multimodal features

[0124] After signal sampling, the microcontroller timestamps and synchronizes the data from the four channels (electrodermal, temperature, electromyography, and pressure) and packages them into standard data frames. Each frame structure includes:

[0125] Signal channel identification;

[0126] Timestamp (milliseconds);

[0127] Eigenvalues ​​(original values ​​and results calculated using the sliding window);

[0128] Data check bit.

[0129] This packaging structure facilitates subsequent edge devices to perform fusion modeling and linkage analysis of multimodal data.

[0130] 3. Bluetooth Low Energy Communication Architecture

[0131] After data encapsulation, it is transmitted to external devices (such as mobile terminals or edge computing boxes) via a built-in BLE 5.0 protocol wireless communication module. This module operates in the 2.4GHz frequency band, has a maximum communication distance of approximately 10 meters, and supports broadcast mode and point-to-point encrypted connections.

[0132] The communication uses an asynchronous triggering reporting mechanism, which automatically enters a sleep state when there is no new data. The average power consumption is less than 1 milliwatt, making it suitable for long-term postoperative use.

[0133] The module supports OTA (Over-The-Air) updates, which allows the device to be updated without disconnecting during system software updates, ensuring stability and remote maintainability.

[0134] In summary, the postoperative pain level sensor described in this embodiment achieves integrated acquisition and intelligent processing of multimodal pain-related physiological signals. It has a reasonable structure, sensitive response, and stable operation. It is a key front-end module for building an intelligent postoperative pain monitoring system and has good clinical feasibility and practical application value.

[0135] Example 2

[0136] This embodiment provides a postoperative pain monitoring system based on multimodal physiological signal recognition. Combining the postoperative pain intensity sensor described in Embodiment 1 with edge computing, intelligent assessment algorithms, and nursing feedback mechanisms, it achieves continuous quantitative assessment and real-time early warning intervention of the patient's pain status after egg retrieval surgery. The system is compact and fully functional, making it particularly suitable for intelligent pain management scenarios in the short term following assisted reproductive technology procedures.

[0137] I. System Structure

[0138] like Figure 2 As shown, the system mainly consists of the following modules:

[0139] 1. Postoperative pain level sensor assembly (described in Example 1);

[0140] 2. Edge processing module;

[0141] 3. Pain Index Assessment Module;

[0142] 4. Nursing interactive terminal and alarm device;

[0143] 5. Data recording and management module.

[0144] The above modules work together to form a closed-loop control system, from signal acquisition to alarm feedback, to achieve intelligent response to postoperative pain.

[0145] II. Edge Processing Module

[0146] The edge processing module is used to receive physiological signal data sent by the front-end sensor and perform preliminary processing.

[0147] 1. Hardware Structure

[0148] The edge processing module is based on the ARM Cortex-A series processor platform and includes a built-in low-power Bluetooth receiver module, local cache, clock synchronization module, and feature extraction algorithm library.

[0149] 2. Processing flow

[0150] The sensor transmits data via the BLE protocol. The edge processing module receives four-channel data frames at a rate of 1 frame per second and executes them sequentially:

[0151] Signal cleaning (spurious value removal and amplitude limiting filtering);

[0152] Time synchronization and channel identifier verification;

[0153] Feature extraction and normalization.

[0154] 3. Output Structure

[0155] The processed data is encapsulated as a JSON or binary stream structure, containing the current feature values ​​and timestamps of each channel, for use by the pain assessment module.

[0156] III. Pain Index Assessment Module

[0157] The pain index assessment module is the core analysis unit of the system, used to calculate the patient's current pain level score.

[0158] 1. Algorithm Model Structure

[0159] This module constructs a multi-layer perceptron based on deep learning, combining LSTM (Long Short-Term Memory Neural Network) and CNN (Convolutional Neural Network) structures:

[0160] LSTM is used to identify slowly varying features in time series.

[0161] CNNs are used to extract interaction patterns between multiple channels;

[0162] The output layer consists of linear regression nodes, which output a pain score ranging from 0 to 10.

[0163] 2. Hierarchical Classification Mechanism

[0164] The pain index score is graded according to the following rules:

[0165]

[0166] 3. Model Training and Individualized Parameter Tuning

[0167] The system supports baseline data collection 10 minutes before surgery for automatic training of individual characteristic parameters; the first postoperative score can be assessed by nurses to calibrate model bias and achieve adaptive learning.

[0168] IV. Nursing Interactive Terminal and Alarm Feedback Mechanism

[0169] 1. Display terminal structure

[0170] The nurse station is equipped with a display terminal (such as a tablet or embedded screen) to display the patient's current pain index, signal curves of each channel, and alarm status in real time.

[0171] 2. Alarm Rules

[0172] When the pain score reaches 7 or above and lasts for more than 180 seconds, the system will automatically trigger an audible and visual alarm and simultaneously display a pop-up notification to the caregiver on the terminal interface.

[0173] 3. Manual intervention and receipt registration

[0174] After receiving an alarm, nursing staff can record the handling method through the terminal interface, such as observation, medication analgesia, or notification of the doctor. All response records are uploaded and archived simultaneously.

[0175] V. Data Recording and System Integration Module

[0176] 1. Local storage structure

[0177] The edge processing module and the evaluation module package data every 15 minutes and store it in a local encrypted database. The stored content includes metadata such as scoring records, alarm logs, processing records and patient IDs.

[0178] 2. HIS system integration

[0179] The system provides an open RESTful API interface, supporting access from Hospital Information System (HIS) and Nursing Information System (NIS), facilitating the inclusion of postoperative pain data into medical records for postoperative follow-up and nursing quality assessment.

[0180] 3. Remote data visualization

[0181] If deployed on an edge gateway platform with network capabilities, the evaluation results can also be synchronously uploaded to the cloud platform for remote monitoring, cross-center data comparison, or clinical research.

[0182] VI. System Operation Flow

[0183] like Figure 3 As shown, the overall system operation flow is as follows:

[0184] 1. Nurses attach postoperative pain level sensors;

[0185] 2. Multi-channel data is transmitted wirelessly to the edge processing module via BLE;

[0186] 3. The edge module completes preprocessing and feature extraction;

[0187] 4. The pain index assessment module outputs a score every 10 seconds;

[0188] 5. The system determines whether to trigger an alarm based on the threshold.

[0189] 6. Nursing staff check the terminal response prompts and take appropriate action;

[0190] 7. All operation and monitoring data are automatically archived and uploaded periodically.

[0191] VII. Clinical Applicability and Summary

[0192] The pain monitoring system provided in this embodiment not only possesses the technical characteristics of high sensitivity, low latency, and automatic response, but also supports individualized adjustment and data archiving, making it widely applicable in scenarios such as assisted reproduction, minimally invasive surgery, and postoperative recovery monitoring. This system can effectively alleviate nurses' workload, improve pain recognition accuracy and nursing response efficiency, and enhance patient comfort and safety.

[0193] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A postoperative pain level sensor, characterized in that, include: A flexible base layer, the flexible base layer being adapted to adhere to the patient's skin surface; Multiple physiological signal sensing units disposed on the flexible substrate are used to collect multimodal physiological signals reflecting the degree of postoperative pain in patients. The physiological signal sensing units include skin electroreception signal sensing units, temperature signal sensing units, electromyography signal sensing units, and pressure signal sensing units. The signal processing chip is used to condition, convert analog signals to digital signals, filter signals and extract features from the analog signals output by the physiological signal sensing unit. A wireless communication module, connected to the signal processing chip, is used to send the processed physiological characteristic data to an external monitoring device; The physiological signal sensing unit, the signal processing chip, and the communication module are all encapsulated in the flexible substrate layer to form an adhesive sensing device.

2. The postoperative pain level sensor according to claim 1, characterized in that, The skin electrical signal sensing unit includes a pair of silver chloride non-polarized electrodes mounted on an elastic base. Conductive gel is placed between the electrodes, with an electrode spacing of 25 mm. The electrodes are connected to a pre-amplifier via flexible conductive lines, with a bandwidth of 0.05 Hz to 1 Hz.

3. The postoperative pain level sensor according to claim 1, characterized in that, The temperature signal sensing unit is an NTC type infrared thermistor temperature sensor, which is located in the center of the flexible substrate layer and is attached to the patient's surgical area skin surface through a flexible thermal pad. The sensor surface is provided with a transparent polyurethane waterproof layer.

4. The postoperative pain level sensor according to claim 1, characterized in that, The electromyography signal sensing unit includes two surface electromyography electrodes and a differential amplifier. The electrode material is a silver-silver chloride composite layer, the electrode diameter is 8 mm, the spacing is 20 to 30 mm, the sampling frequency is 500 Hz, and the bandwidth is 20 Hz to 300 Hz.

5. The postoperative pain level sensor according to claim 1, characterized in that, The pressure signal sensing unit is a capacitive flexible pressure sensor, including upper and lower electrodes and a middle PDMS and carbon nanotube composite elastic medium layer. The pressure sensing unit is located in the lower central region of the flexible substrate layer, with a sensing range of 0 to 500 Pa and a sensitivity better than 0.5 Pa per second.

6. A pain monitoring system for egg retrieval surgery, characterized in that, include: The postoperative pain level sensor as described in any one of claims 1 to 5 is used to collect postoperative patient skin conductance signals, temperature signals, electromyography signals, and pressure signals; An edge processing module is wirelessly connected to the postoperative pain level sensor. The edge processing module is used to filter, normalize, synchronize time, and extract features from the received multimodal physiological signals. The pain index assessment module, connected to the edge processing module, is used to construct a feature vector based on the feature data and output a pain index score based on a preset neural network model. The pain index is a continuous value between 0 and 10. The feedback interaction module includes a display terminal and an alarm device, used to display the pain index score and its changing trend, and to trigger an alarm prompt when the score exceeds a preset threshold condition; The data recording module is used to store the pain index score, alarm events and nursing response information, and supports uploading the data to the hospital information system or local database for archiving.

7. The egg retrieval surgery pain monitoring system according to claim 6, characterized in that, The edge processing module is equipped with four analog input interfaces, which are used to receive skin conductance, temperature, electromyography and pressure signals respectively. The module has a built-in feature extraction program to extract parameters such as the average value, slope of change, fluctuation amplitude and variance of each channel.

8. The egg retrieval surgery pain monitoring system according to claim 6, characterized in that, The pain index assessment module employs a combined model that integrates long short-term memory neural networks and convolutional neural networks to model the time-series features and inter-channel spatial features of physiological signals, and supports individualized model parameter calibration based on the patient's preoperative resting data.

9. The egg retrieval surgery pain monitoring system according to claim 6, characterized in that, The feedback interaction module is equipped with alarm judgment rules. When the pain index score is greater than or equal to 7 and lasts for more than 180 seconds, the system triggers an audible and visual alarm and pops up an alarm prompt interface.

10. The egg retrieval surgery pain monitoring system according to claim 6, characterized in that, The data recording module connects with the hospital information system via a RESTful interface. The data recording module supports storing scoring timestamps, alarm response times, nursing treatment methods, and historical scoring curves.

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