Portable defibrillator first-aid operation guidance system for sudden cardiac arrest scene

Abstract

The invention discloses a portable defibrillator first-aid operation guidance system for sudden cardiac arrest scenes, which comprises the following steps of: acquiring multi-dimensional physiological parameters, combining self-learning heart rhythm judgment and myocardial electrical activity mode matching to realize accurate recognition of abnormal heart rhythms, dynamically optimizing defibrillation energy parameters based on multiple factors, and realizing accurate recognition of the abnormal heart rhythms. Meanwhile, real-time storage, encrypted transmission and multi-terminal sharing of first-aid process data are completed through a data management platform, and operation instructions related to electrode pasting, pressing rhythm and defibrillation time are synchronously output in a multi-mode guiding mode. According to the system, a closed-loop technical link from signal acquisition, intelligent discrimination and parameter optimization to operation guidance and data management is constructed, the problems of single heart rhythm discrimination, defibrillation parameter solidification, high operation threshold, unsmooth data collaboration and the like of traditional equipment are effectively solved, the difficulty of out-of-hospital emergency treatment operation is greatly reduced, the defibrillation accuracy and the emergency treatment efficiency are improved, and the system is suitable for popularization and application. And scientific and efficient first-aid support is provided for non-professional users.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of portable defibrillator operation, and particularly relates to a portable defibrillator first-aid operation guiding system for sudden cardiac arrest scene. BACKGROUND

[0002] Sudden cardiac arrest has the characteristics of acute onset and high mortality, and often occurs in the scene without professional medical resources. The time window from onset to receiving professional first aid directly affects the survival rate of patients. In current out-of-hospital first aid, a portable defibrillator is a key first aid device, but ordinary users lack professional first aid knowledge and operation experience, and it is difficult to quickly and accurately complete key operations such as electrode sticking, rhythm judgment, and defibrillation timing selection. At the same time, the myocardial electrical activity characteristics and physical parameters of different patients have individual differences, and the traditional defibrillator adopts a fixed energy output mode, lacks targeted optimization, and the data is difficult to be synchronized and traced in real time during the first aid process, which affects the subsequent medical connection. Under this background, it is urgent to build an integrated system integrating signal acquisition, intelligent discrimination, precise defibrillation, operation guidance and data management to solve the problems of high operation threshold, insufficient defibrillation targeting and poor data coordination in out-of-hospital sudden cardiac arrest first aid, and to provide scientific and efficient first aid support for non-professionals.

[0003] The existing technology has two significant shortcomings: first, the accuracy of rhythm discrimination and defibrillation parameter optimization is insufficient. Traditional systems mostly rely on single electrocardiogram parameters for rhythm judgment, without fully integrating multidimensional physiological information such as myocardial impedance and blood oxygen correlation, and lack dynamic self-learning ability, making it difficult to adapt to individual differences and complex pathological states of different patients. Defibrillation energy output is mostly based on fixed algorithms without real-time optimization based on electrode contact state, patient physical condition estimation data and previous first aid response, which may lead to low defibrillation efficiency or additional damage to the myocardium. Second, the collaboration of operation guidance and data management is lacking. The operation guidance of existing systems is mostly single voice prompts or text instructions without combining multi-modal guidance methods such as image recognition and tactile feedback, making it difficult to assist users in quickly correcting operation deviations. Moreover, first aid data storage is scattered, transmission security is insufficient, and real-time sharing and reliable tracing between first aid personnel, hospitals and emergency centers cannot be achieved, affecting the continuity and professionalism of first aid connection. SUMMARY

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present application provides a portable defibrillator first-aid operation guiding system for sudden cardiac arrest scene.

[0005] The technical scheme adopted by the application is a portable defibrillator first-aid operation guidance system for sudden cardiac arrest scenes, comprising: a multi-parameter self-learning heart rhythm discrimination algorithm module, a myocardial electrical activity pattern matching model module, a low-energy defibrillation efficiency optimization algorithm module, a mobile first-aid data management platform module, a portable defibrillator operation guidance module, and a multi-module collaborative control module. The multi-parameter self-learning heart rhythm discrimination algorithm module constructs a dynamic discrimination network by collecting myocardial electrical signals, heart rate variability parameters, and electrocardio cycle characteristic parameters, and performs real-time interaction of characteristic parameters with the myocardial electrical activity pattern matching model module through a high-speed data bus. The myocardial electrical activity pattern matching model module transmits the matched mode characteristic vector to the low-energy defibrillation efficiency optimization algorithm module. The low-energy defibrillation efficiency optimization algorithm module generates an optimized defibrillation parameter set in combination with a defibrillation voltage threshold, a pulse width parameter, and an electrode contact impedance parameter. The mobile first-aid data management platform module receives the optimized defibrillation parameter set and the heart rhythm discrimination result through a wireless communication link and performs encrypted storage and classified indexing. The portable defibrillator operation guidance module generates visual guidance instructions according to the parameters transmitted by the mobile terminal and the real-time operation state. The multi-module collaborative control module dynamically schedules the data transmission timing and operation priority of each module through a timing synchronization protocol to realize closed-loop collaboration of heart rhythm discrimination, pattern matching, defibrillation optimization, data management, and operation guidance.

[0006] Further, the discrimination algorithm expression adopted by the multi-parameter self-learning heart rhythm discrimination algorithm module is: wherein, is a heart rhythm abnormality discrimination coefficient, is the i-th acquisition parameter (including electrocardio R wave amplitude, myocardial impedance change rate, and blood oxygen fluctuation frequency), is a parameter weight coefficient, is an algorithm self-learning adjustment parameter, is a parameter characteristic mapping function, is the i-th feature screening function, is a feature sensitivity coefficient, is a feature mean value threshold, is a timing penalty factor, is a multi-parameter fusion vector at a time t, is a fusion matrix, is a timing correlation function, is a signal acquisition time length, is an L2 norm.

[0007] Further, the matching model expression adopted by the myocardial electrical activity pattern matching model module is: wherein, is a pattern matching similarity value, is a real-time myocardial electrical activity characteristic parameter (including QRS complex width, ST segment offset, T wave shape coefficient), is a preset pathological pattern characteristic parameter, is a feature matching weight, is a covariance calculation function, , is a feature adjustment coefficient, is a real-time parameter standard deviation, is a preset parameter standard deviation, is a pattern decay coefficient, is a difference sensitivity factor, is a feature parameter dimension number.

[0008] Further, the low-energy defibrillation efficiency optimization algorithm module adopts an optimization algorithm expression: wherein, is an optimized defibrillation energy value, is a tissue conductivity coefficient, is an electrode contact impedance, is a patient weight estimation value, is an energy transmission efficiency coefficient, is a time interval from arrhythmia recognition to completion of defibrillation preparation, is a historical response adjustment factor, is a number of previous defibrillations, is a maximum safe defibrillation number threshold, is an impedance change correction coefficient, is a difference between the current impedance and the initial impedance.

[0009] Further, the data interaction model expression of the mobile terminal first-aid data management platform module is: wherein, is a data transmission and storage quality evaluation value, is a time data priority weight, is a time data volume, is a data encryption hash function, is an amplification coefficient, is a communication distance between the mobile terminal and the defibrillator, is a data transmission duration, is a data correction factor at time t, is a variance of the error term at time t, is a communication distance attenuation coefficient at time t, is a transmission efficiency weight at time t.

[0010] Furthermore, the operation guidance generation model expression of the real-time emergency operation guidance module is as follows: ,in, Output intensity and frequency control parameters to guide operation. To guide the weighting coefficients, This is a function relating the pressing parameters to the electrode position. for The value of the fusion of pressing depth and frequency at any given moment. for The deviation value of the electrode bonding position at any time. For the first Class guide pattern weights, For the first Class of guide signal parameters, To guide the feedback response coefficient, This is the bias sensitivity coefficient. The standard press parameter threshold, This indicates the number of guide modes.

[0011] Furthermore, the low-energy defibrillation efficiency optimization algorithm module includes a defibrillation parameter preprocessing unit, an energy response prediction unit, a multi-constraint optimization unit, and a parameter output calibration unit. The defibrillation parameter preprocessing unit receives myocardial electrical activity pattern matching results, electrode contact impedance detection data, and estimated values ​​of patient physiological parameters. It performs time-series alignment and outlier filtering on the data and extracts effective feature parameters through signal amplification and noise suppression. The energy response prediction unit constructs an energy-effect correlation mapping based on historical defibrillation data and predicts defibrillation success rate correlation indicators at different energy levels by combining current cardiac rhythm parameters. The multi-constraint optimization unit constructs a multi-objective optimization function by integrating impedance change trends, patient physical parameters, and emergency environment factors, with energy minimization and tissue damage risk as constraints. The parameter output calibration unit dynamically calibrates the optimized energy parameters and adjusts the output parameters based on real-time collected electrode contact state changes to ensure that the defibrillation energy accurately matches the individual characteristics of the patient.

[0012] Furthermore, the mobile emergency medical data management platform module includes a real-time data receiving unit, an encrypted storage unit, a multi-terminal synchronization unit, and an emergency record generation unit. The real-time data receiving unit receives physiological parameters, defibrillation operation data, and guidance feedback information transmitted by the defibrillator via Bluetooth and 5G dual-mode communication links, and uses streaming data processing technology for high-concurrency data reception and parsing. The encrypted storage unit uses the AES-256 encryption algorithm to encrypt the emergency medical data and combines a distributed storage architecture for local data backup and cloud synchronization. The multi-terminal synchronization unit uses blockchain technology to ensure trusted sharing and access control of emergency medical data among emergency personnel terminals, hospital systems, and emergency center platforms. The emergency record generation unit automatically extracts and calibrates data nodes and generates an electronic emergency medical record in a standardized format, including heart rate change curves, defibrillation parameter details, and an operation timeline.

[0013] Furthermore, the real-time emergency operation guidance module includes an electrode attachment guidance unit, a chest compression guidance unit, a defibrillation timing prompt unit, and an emergency adjustment feedback unit. The electrode attachment guidance unit uses image recognition technology to capture feature points on the patient's body surface and generates a visual attachment area marker by combining it with a preset electrode positioning template. It assists in adjusting the electrode position through voice prompts until the contact impedance requirements are met. The chest compression guidance unit collects chest compression depth, frequency, and chest compression point offset data in real time, converts them into audio beat signals and visual progress bar prompts, and simultaneously outputs chest compression pressure adjustment instructions. The defibrillation timing prompt unit receives heart rate discrimination results and defibrillation parameter optimization signals, and prompts the operator to initiate defibrillation through a synchronized audio-visual alarm during the optimal defibrillation window. The emergency adjustment feedback unit monitors operational deviations in real time and dynamically adjusts the intensity and form of the guidance signal based on ambient light and noise intensity to ensure the effectiveness of the operation guidance.

[0014] A portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios includes the following steps: S1. Acquiring surface electrocardiogram signals, myocardial impedance signals, and blood oxygen saturation-related parameters of a patient experiencing sudden cardiac arrest via the lead electrode array of a multi-parameter heart rhythm signal acquisition module, and transmitting these multi-dimensional physiological parameters to a multi-parameter self-learning heart rhythm discrimination algorithm module at a preset sampling frequency; S2. The multi-parameter self-learning heart rhythm discrimination algorithm module extracts features from the received parameters, identifies arrhythmias based on a dynamically updated sample library, and outputs preliminary discrimination results; S3. The preliminary discrimination results are input into a myocardial electrical activity pattern matching model module, which compares them with a preset pathological state pattern library for multi-dimensional feature comparison, generating pattern matching results; S4. The pattern matching results are transmitted to the low-energy defibrillation efficiency optimization algorithm module. Combining electrode contact impedance, estimated patient weight, and previous defibrillation response data, the algorithm calculates the optimized defibrillation energy parameters. The defibrillation parameters and operation instructions output by the low-energy defibrillation efficiency optimization algorithm module are synchronized to the mobile emergency data management platform module via a wireless communication link, enabling real-time data storage, encrypted transmission, and multi-terminal sharing. The mobile emergency data management platform module triggers the real-time emergency operation guidance module, providing full-process technical support from signal acquisition to operation execution through voice prompts, visual interface guidance, and tactile feedback mechanisms, including electrode placement calibration, compression rhythm control, and defibrillation timing selection.

[0015] Beneficial Effects: This invention proposes a portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios. It integrates multi-dimensional physiological parameter acquisition and dynamic self-learning rhythm discrimination capabilities, fully incorporating multiple signals such as ECG, myocardial impedance, and blood oxygen correlation. Combined with myocardial electrical activity patterns, it accurately matches different pathological states, overcoming the limitations of traditional single-parameter discrimination. Simultaneously, through a low-energy defibrillation efficiency optimization mechanism, it dynamically integrates electrode contact status, patient physical condition estimation data, and past response information to adjust defibrillation parameters, achieving individualized and precise defibrillation. This improves defibrillation effectiveness while reducing the risk of myocardial damage. Furthermore, through a multimodal emergency operation guidance mechanism, it integrates a visual interface, voice prompts, and tactile feedback. This system assists non-professional users in quickly and accurately performing electrode attachment, compression, and defibrillation triggering, resolving operational errors associated with traditional single-guided methods. Simultaneously, leveraging the encrypted storage, dual-mode communication, and multi-terminal sharing capabilities of the mobile data management platform, it enables real-time traceability and reliable collaboration of emergency data, establishing information links between emergency personnel, hospitals, and emergency centers to ensure continuity of emergency care. The system provides comprehensive technical support throughout the entire process, from signal acquisition, intelligent identification, and parameter optimization to operational guidance and data management. This significantly lowers the barrier to entry for out-of-hospital emergency care, improves the accuracy and efficiency of emergency care, buys precious time for sudden cardiac arrest patients, and provides complete data support for subsequent medical treatment. Attached Figure Description

[0016] Figure 1 This is a diagram showing the system module composition of the present invention; Figure 2 This is a flowchart of the system operation steps of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, the portable defibrillator emergency operation guidance system for sudden cardiac arrest includes: a multi-parameter self-learning heart rhythm discrimination algorithm module, a myocardial electrical activity pattern matching model module, a low-energy defibrillation efficiency optimization algorithm module, a mobile emergency data management platform module, a portable defibrillator operation guidance module, and a multi-module collaborative control module. The multi-parameter self-learning rhythm discrimination algorithm module constructs a dynamic discrimination network by collecting myocardial electrical signals, heart rate variability parameters, and electrocardiogram cycle characteristic parameters. It interacts with the myocardial electrical activity pattern matching model module in real time through a high-speed data bus. The myocardial electrical activity pattern matching model module transmits the matched pattern feature vector to the low-energy defibrillation efficiency optimization algorithm module. The low-energy defibrillation efficiency optimization algorithm module generates an optimized defibrillation parameter set by combining defibrillation voltage threshold, pulse width parameter, and electrode contact impedance parameter. The mobile emergency data management platform module receives the optimized defibrillation parameter set and rhythm discrimination results through a wireless communication link and performs encrypted storage and classification indexing. The portable defibrillator operation guidance module generates visual guidance instructions based on the parameters transmitted from the mobile terminal and the real-time operation status. The multi-module collaborative control module dynamically schedules the data transmission timing and operation priority of each module through a timing synchronization protocol to achieve closed-loop collaboration of rhythm discrimination, pattern matching, defibrillation optimization, data management, and operation guidance.

[0019] The multi-parameter cardiac rhythm signal acquisition module serves as the core of the system's data input. It employs a medical-grade Ag / AgCl disposable lead electrode array, including three precordial electrodes (placed at the right sternal border in the second intercostal space, the left anterior axillary line in the fifth intercostal space, and the left sternal border in the fourth intercostal space, respectively) and two limb reference electrodes (connected to the left and right wrists, respectively). The electrodes are 18mm in diameter, and the adhesive layer uses medical-grade pressure-sensitive adhesive, meeting the ISO10993 biocompatibility standard to ensure no irritation during prolonged contact. This module integrates a high-precision signal acquisition chip, with an ECG signal sampling frequency set to 1000Hz, sampling accuracy up to 24 bits, a voltage measurement range of ±10mV, input impedance ≥100MΩ, and equivalent input noise. It can accurately capture subtle ECG features such as R waves, QRS complexes, ST segment deviation, and T wave morphology; myocardial impedance signals are acquired through a 50kHz sine wave excitation source, with a measurement range of 20-1000Ω, a resolution of 0.01Ω, and a measurement error of ≤±2%, reflecting in real time the tightness of contact between the electrode and myocardial tissue and changes in tissue conductivity; blood oxygen saturation-related parameters are acquired using a dual-wavelength photoelectric sensor with red light (660nm) and infrared light (940nm), a sampling frequency of 30Hz, a blood oxygen saturation measurement range of 60%-100%, and an error of ≤±1%, while simultaneously acquiring pulse rate (40-240 beats / minute) and perfusion index (0.02%-20%). The module incorporates a low-power microprocessor (STM32L476) and uses a 16-bit ADC for signal conversion. It removes power frequency interference and motion artifacts through digital filtering (cutoff frequency 0.5Hz-150Hz). Data is transmitted to the storage unit (capacity 8GB) via the SPI interface and simultaneously transmitted externally via a Bluetooth 5.2BLE and 5G dual-mode communication module with a transmission rate ≥2Mbps and latency ≤30ms. Power is supplied by a rechargeable lithium battery (capacity 3000mAh, operating voltage 3.7V, continuous working time ≥8 hours), supporting Type-C interface fast charging to meet the needs of long-term emergency outpatient scenarios. Its core significance lies in providing a high-fidelity, multi-dimensional physiological parameter basis for subsequent algorithm modules, ensuring the accuracy and integrity of the signal source.

[0020] The multi-parameter self-learning heart rhythm discrimination algorithm module is built on the ARM Cortex-A72 processor (1.5GHz) to construct the computing core. It has a built-in high-speed flash memory with a capacity of ≥64GB to store the heart rhythm feature sample library and algorithm program. The sample library initially includes feature data of 15 common heart rhythm states such as sinus rhythm, ventricular fibrillation, ventricular tachycardia, atrial fibrillation, atrial flutter, and sinus arrest. Each state includes more than 100,000 labeled samples from people of different ages (0-80 years old) and weights (20-120kg). It also supports dynamic updates of the sample library through cloud synchronization once a week to ensure the algorithm's adaptability to new heart rhythm abnormality patterns. After receiving the time-series data transmitted by the multi-parameter cardiac rhythm signal acquisition module, this module uses a sliding window method (window length 3s, step size 0.5s) to segment the data. Through time domain analysis, it extracts eight time-domain features, including RR interval, QRS complex width (normal range 60-100ms), R wave amplitude (normal range 0.5-2.5mV), and ST segment offset (normal range -0.1mV to +0.1mV). Through frequency domain analysis (fast Fourier transform, frequency resolution 0.1Hz), it extracts six frequency-domain features, including the main frequency component of the ECG signal (0.5-30Hz) and peak power spectral density. At the same time, it extracts four related features, including the rate of change of myocardial impedance (normal range -5% to +5%) and blood oxygen fluctuation frequency (0.1-0.5Hz). A total of 18 feature parameters constitute the feature vector. The algorithm employs Gradient Boosting Decision Tree (GBDT) to construct a discrimination model, comprising 500 decision trees with a maximum depth of 15 layers and a learning rate of 0.01. Model parameters are optimized through cross-validation (5-fold folding). The model performs classification operations on feature vectors and outputs results such as the type of cardiac rhythm abnormality, abnormal confidence level (range 0-1, threshold 0.8), and feature matching degree. The computation latency is ≤500ms, and the discrimination accuracy is ≥98%. This module supports self-learning. When an unrecognized cardiac rhythm pattern occurs during emergency treatment, the data is automatically marked and uploaded to the cloud. After being annotated by professional physicians, it is incorporated into the sample database. The model parameters are updated through backpropagation, improving subsequent discrimination accuracy. Its core significance lies in achieving accurate and rapid identification of complex cardiac rhythm signals, providing a reliable basis for subsequent pattern matching and defibrillation decisions, and solving the problems of low accuracy and poor adaptability of traditional single-parameter discrimination.

[0021] The myocardial electrical activity pattern matching model module uses an FPGA chip (XilinxArtix-7) to achieve high-speed parallel computing with a computing frequency of ≥500MHz. It has a built-in preset pathological state pattern library, including 10 types of pathological patterns directly related to sudden cardiac arrest, such as acute myocardial infarction, severe arrhythmia, and myocardial ischemia. Each pathological pattern includes 25 sets of typical feature vectors. The feature vector dimension is consistent with the feature vector output by the multi-parameter self-learning rhythm discrimination algorithm module (18 dimensions). Each set of feature vectors is labeled with the corresponding pathological severity level (1-5) and the defibrillation adaptation parameter range. After receiving the discrimination results from the multi-parameter self-learning heart rhythm discrimination algorithm module, this module first standardizes the feature vectors (based on the mean and standard deviation of the sample library). Then, it uses a cosine similarity algorithm to calculate the matching degree between the real-time feature vectors and each feature vector in the pathological state pattern library. The matching degree calculation ranges from 0 to 1. Simultaneously, a dynamic weight allocation mechanism is used, assigning a weight of 0.4 to core ECG features (such as QRS complex morphology and ST segment shift), 0.3 to myocardial impedance features, and 0.3 to blood oxygen correlation features. The overall matching degree is then calculated using a weighted average. To improve matching accuracy, the module introduces time-series correlation analysis, calculating the Pearson correlation coefficient (range -1 to 1) between the real-time feature vectors and template vectors at different time scales (0.5s, 1s, 2s). This is combined with a moving average filter (window length of 5 data points) to smooth the matching results. Finally, the module outputs the overall matching degree (threshold 0.75), the matched pathological pattern type, and the pathological severity grade. The entire matching process takes ≤300ms, with a matching accuracy ≥97%. When the overall matching degree is lower than the threshold, the module automatically starts a secondary matching process to expand the search range of the pattern library and include the feature vectors of marginal cases to ensure that there are no missed cases. Its core significance lies in accurately locating the pathological state of the patient's myocardial electrical activity, providing targeted pathological basis for the optimization of defibrillation parameters, and avoiding poor efficacy or tissue damage caused by blind defibrillation.

[0022] The low-energy defibrillation efficiency optimization algorithm module is based on a DSP processor (TITMS320C6748, 456MHz) to build the computing core. It integrates a defibrillation energy output control unit and a parameter adjustment unit. Its core function is to dynamically optimize defibrillation energy parameters based on myocardial electrical activity pattern matching results to achieve low-energy, high-efficiency defibrillation. The input parameters received by this module include pathological pattern matching results (pathological type, severity grade), defibrillation electrode contact impedance (transmitted in real time by a multi-parameter heart rhythm signal acquisition module, measurement range 50-500Ω), patient weight estimation (estimated by a height-weight regression model combined with the distance between the electrodes on the body surface, electrode distance measurement range 20-50cm, weight estimation error ≤±3kg), and previous defibrillation energy response data (storing the energy values ​​of the last 15 defibrillations, post-defibrillation heart rhythm recovery status, and changes in myocardial impedance). The algorithm aims to minimize defibrillation energy (target range 50-200J) and minimize the risk of myocardial injury. It constructs a multi-objective optimization function with constraints including the energy limit corresponding to the patient's weight (maximum energy of 4J per kg of body weight), the energy adjustment coefficient corresponding to the electrode contact impedance (energy increases by 10% for every 50Ω increase in impedance), and the energy baseline value corresponding to the severity of the pathology (Grade 1: 50-80J, Grade 2: 80-120J, Grade 3: 120-150J, Grade 4: 150-180J, Grade 5: 180-200J). The algorithm employs a particle swarm optimization algorithm (100 particles, 50 iterations, and linearly decreasing inertia weights from 0.5 to 0.9) to solve the optimization function. Simultaneously, it incorporates myocardial electrical activity cycle detection results (using R-wave peak location to determine the vulnerable period of defibrillation 20-30ms after the R-wave). The algorithm outputs optimized defibrillation energy (adjusted step size 5J), defibrillation pulse width (1-10ms, step size 0.1ms), and number of defibrillations (1-3 times, determined based on pathological severity). The module incorporates a built-in defibrillation energy output detection unit, which monitors the deviation between the actual and setpoint output energy values ​​in real time. Automatic calibration occurs when the deviation exceeds ±5%. It also features overcurrent protection (threshold 10A) and overvoltage protection (threshold 500V) to ensure defibrillation safety. Its core significance lies in achieving individualized and precise low-energy defibrillation, minimizing the risk of myocardial tissue damage while ensuring defibrillation effectiveness, thus solving the problem of poor adaptability in traditional fixed-energy defibrillation.

[0023] The mobile emergency medical data management platform module is developed based on Android 12 / iOS 16 and above operating systems, supporting smartphones, tablets, and other mobile terminals (screen size ≥ 5.5 inches, resolution ≥ 1920×1080). Its core functions include real-time storage, encrypted transmission, multi-terminal sharing, and data traceability of emergency medical data. This module receives defibrillation parameters and operation commands transmitted by the low-energy defibrillation efficiency optimization algorithm module, as well as raw physiological data transmitted by the multi-parameter heart rhythm signal acquisition module, via Bluetooth 5.2 BLE and 5G dual-mode communication. Data storage employs a dual-backup mode: a local database (SQLite, capacity ≥ 32GB) and a cloud server (Alibaba Cloud ECS, distributed storage architecture). Local storage supports offline caching (capable of storing ≥ 100 emergency medical data sessions offline). Cloud storage uses the AES-256 encryption algorithm to encrypt data. The encryption key is dynamically generated using device hardware information and a timestamp and is updated periodically (every 24 hours). Data transmission uses the TLS 1.3 encryption protocol to ensure data security and privacy. The platform supports multi-terminal sharing, enabling real-time synchronization of emergency data between emergency personnel's mobile devices, the hospital's HIS system, and the emergency center's command platform via QR code scanning authorization and account permission management (divided into three levels of permissions: emergency personnel, physicians, and administrators). Synchronization latency is ≤1 second. Data includes complete information such as patient physiological parameter time-series curves (ECG, myocardial impedance, blood oxygen saturation), defibrillation parameter details (energy value, trigger timing, number of times), operation guidance records, and emergency timelines. The platform has built-in data statistical analysis functions, automatically generating emergency reports (including indicators such as arrhythmia identification accuracy, defibrillation success rate, and operation compliance rate), supporting PDF export and printing. It also features data traceability, allowing users to query historical emergency data via emergency number and patient information (anonymized). Data retention is ≥5 years, complying with medical data management standards. Its core significance lies in achieving full-process management of emergency data, ensuring data security and sharing, providing data support for emergency process traceability, subsequent medical treatment, and algorithm optimization, and bridging the information gap between out-of-hospital emergency care and in-hospital treatment.

[0024] The real-time emergency operation guidance module integrates a visual display unit, a voice prompt unit, a tactile feedback unit, and an alarm unit. Its core function is to provide clear and accurate emergency operation guidance for non-professional users, ensuring the correct use of the defibrillator. The visual display unit uses a high-definition IPS touchscreen (7-inch size, 1280×720 resolution, brightness ≥500cd / m², supports glove operation). Through a graphical interface, it displays in real time an electrode attachment diagram (marking the specific location and attachment direction of the three chest electrodes), a compression depth progress bar (target range 5-6cm, real-time display of current compression depth), a compression rate indicator (target 100-120 compressions / minute, displayed as dynamic numbers and waveforms), a defibrillation timing countdown (range 0-10s), and simultaneously displays electrode contact status (green indicates good contact, yellow indicates moderate contact, and red indicates poor contact), battery level, signal quality, and other status information. The voice prompt unit uses a dual-channel speaker (2W output power, volume range 50-100dB, supports noise suppression), outputting operation instructions sequentially according to the emergency procedure, including electrode placement steps ("Please attach the red electrode to the second intercostal space at the right sternal border, and the yellow electrode to the fifth intercostal space at the left anterior axillary line..."), chest compression operation guidelines ("Please press the lower middle segment of the patient's sternum, to a depth of 5-6cm, at a frequency of 100-120 compressions / minute..."), and defibrillation preparation prompts ("Defibrillation is about to begin, please move away from the patient..."). The voice speed is adjustable (normal and slow speeds), and multilingual switching (Chinese, English, Japanese) is supported. The tactile feedback unit uses a linear vibration motor (vibration frequency 50-200Hz, vibration intensity adjustable in 3 levels). When the compression frequency is below 100 compressions / minute, the motor vibrates at a frequency of 100 compressions / minute; when the compression frequency is above 120 compressions / minute, the motor vibration frequency increases as a prompt; when the electrode placement deviation exceeds 5cm, the vibration area at the corresponding electrode position starts vibrating. The alarm unit includes a red LED alarm light (flashing frequency 2Hz) and a buzzer (alarm sound frequency 1kHz, volume 90dB). When abnormalities occur such as poor electrode contact, insufficient / excessive compression depth, insufficient defibrillation energy, or low battery, the audible and visual alarms are activated, along with voice prompts indicating the cause of the abnormality and adjustment methods. The module has a built-in operation flow logic control unit that dynamically adjusts the guidance content based on instructions from the mobile emergency data management platform module and real-time collected operation feedback data. This ensures that the guidance is synchronized with the actual operation progress, with a delay of ≤200ms throughout the guidance process. Its core significance lies in lowering the barrier to defibrillator operation, helping non-professional users quickly master the correct first aid operation methods, improving the success rate of out-of-hospital emergency cardiac arrest resuscitation, and solving the problems of complex operation and unclear guidance associated with traditional defibrillators.

[0025] Preferably, the discrimination algorithm expression used by the multi-parameter self-learning heart rhythm discrimination algorithm module is: ,in, The discriminant coefficient for cardiac arrhythmias is... For the first Collected parameters (including ECG R wave amplitude, myocardial impedance change rate, and blood oxygen fluctuation frequency). These are the parameter weighting coefficients. Adjusting parameters for algorithm self-learning For the parameter feature mapping function, For the first Class feature filtering function, The characteristic sensitivity coefficient, The threshold for the feature mean. As a time-series penalty factor, for Time-varying multi-parameter fusion vector For the fusion matrix, For time-series correlation functions, For signal acquisition duration, It is an L2 norm.

[0026] Specifically, the multi-parameter self-learning heart rhythm discrimination algorithm module achieves accurate discrimination of heart rhythm abnormalities through multi-dimensional parameter integration and dynamic calculation. During the calculation process, the algorithm first assigns differentiated weight coefficients to the 18 feature parameters (including ECG, myocardial impedance, and blood oxygenation-related features) transmitted by the multi-parameter heart rhythm signal acquisition module. The weight coefficients are set according to the contribution of each feature to heart rhythm discrimination, ranging from 0.02 to 0.15. Among them, the R-wave amplitude and RR interval variation coefficient of the ECG signal have the highest weights, at 0.15 and 0.13 respectively, while the myocardial impedance change rate and blood oxygenation fluctuation frequency have weights of 0.08 and 0.06 respectively. The algorithm performs nonlinear transformation on each parameter through a feature mapping function, and simultaneously introduces a feature filtering function to filter redundant information, retaining core features highly correlated with heart rhythm abnormalities. The feature sensitivity coefficient is set between 1.2 and 3.5, and the feature mean threshold is determined based on the statistical mean of normal heart rhythm features in the sample database, covering the normal physiological range of each parameter. A time-series penalty factor of 0.05 is used to balance the correlation between real-time parameters and historical time-series data. By calculating the gradient change and L2 norm of the multi-parameter fusion vector, the ability to identify arrhythmia signals is enhanced. The signal acquisition duration is set to 2 seconds to ensure that the algorithm has sufficient data to support the discrimination operation and avoid misjudgment due to insufficient data. The algorithm is implemented by embedding the operation program in an ARM Cortex-A72 processor. Parallel computing improves the computational efficiency, and the computational latency is controlled within 500ms. Its core significance lies in significantly improving the accuracy and robustness of arrhythmia discrimination through multi-parameter weighted fusion, time-series correlation analysis, and dynamic feature screening. It solves the problems of traditional single-parameter discrimination being susceptible to interference and having poor adaptability, providing reliable discrimination results for subsequent pattern matching and defibrillation decisions.

[0027] Preferably, the matching model expression used by the myocardial electrical activity pattern matching model module is: ,in, For pattern matching similarity values, These are real-time myocardial electrical activity characteristic parameters (including QRS complex width, ST segment offset, and T wave morphology coefficient). Preset pathological pattern characteristic parameters, For feature matching weights, The covariance calculation function, , The characteristic adjustment coefficient, For the real-time parameter standard deviation, The standard deviation of the preset parameter, The mode attenuation coefficient, As a differentially sensitive factor, This represents the number of dimensions of the feature parameters.

[0028] Specifically, the matching algorithm of the myocardial electrical activity pattern matching model module accurately locates the pathological state of the patient's myocardial electrical activity through multi-dimensional feature comparison and weighted calculation. During the model's operation, 18 feature parameters of real-time myocardial electrical activity are first extracted, including core ECG features such as QRS complex width, ST segment offset, and T wave morphology coefficient, as well as myocardial impedance and blood oxygenation correlation features. These are then aligned one by one with 25 sets of typical feature parameters for 10 pathological states in the pathological pattern library. The feature matching weights are set according to the key features of the pathological pattern, ranging from 0.03 to 0.08. Among them, the matching weight for ST segment offset is the highest at 0.08 in the myocardial infarction-related pattern, while the matching weight for QRS complex width is 0.07 in the ventricular fibrillation pattern. By calculating the covariance between real-time features and preset features, the linear correlation between the two is reflected. The feature adjustment coefficient α ranges from 1.0 to 2.5, and β ranges from 0.3 to 1.2, used to amplify the difference in key features. The standard deviations of real-time and preset parameters are obtained statistically from a sample database, covering the characteristic fluctuation range of various pathological states. The influence of feature fluctuations on matching results is balanced using square root calculation. The pattern attenuation coefficient ranges from 0.8 to 0.95, and the difference sensitivity factor ranges from 0.5 to 1.8, used to adjust the matching sensitivity of different pathological patterns and avoid missed or false judgments due to minor differences in features. The number of feature parameters is fixed at 18 dimensions to ensure the consistency and standardization of matching operations. The model is implemented using a parallel computing architecture based on an FPGA chip, synchronously comparing and weighting features across all dimensions. Matching time is controlled within 300ms. Its core significance lies in improving the accuracy and specificity of pathological pattern matching through multi-dimensional feature covariance analysis, dynamic weight allocation, and sensitivity adjustment. This accurately identifies different types of myocardial electrical activity abnormalities, providing targeted pathological evidence for defibrillation parameter optimization and avoiding indiscriminate defibrillation.

[0029] Preferably, the optimization algorithm expression used by the low-energy defibrillation efficiency optimization algorithm module is: ,in, To optimize the defibrillation energy value, The conductivity coefficient of the tissue. The electrode contact resistance, For the patient's estimated weight, The energy transfer efficiency coefficient. The time interval from the recognition of arrhythmia to the completion of defibrillation preparation. Historical response moderating factor, This refers to the number of previous defibrillation attempts. The maximum safe number of defibrillation attempts threshold. This is the impedance change correction factor. This is the difference between the current impedance and the initial impedance.

[0030] Specifically, the optimization logic of the low-energy defibrillation efficiency optimization algorithm module outputs individualized defibrillation energy parameters through multi-factor dynamic calculation. During the algorithm's operation, the pathological severity grade (1-5) from the myocardial electrical activity pattern matching results is first obtained. This is combined with the electrode contact impedance (measurement range 50-500Ω) transmitted in real time by the multi-parameter heart rhythm signal acquisition module. The tissue conductivity coefficient is set to 0.2 to 0.5 S / m based on the statistical mean of human tissue conductivity. The patient's estimated weight is obtained through a height-weight regression model combined with electrode spacing measurements, with an error controlled within ±3 kg. The energy baseline value corresponding to weight is set at an upper limit of 4 J per kg of body weight. The energy transmission efficiency coefficient is based on the defibrillator hardware performance calibration, ranging from 0.7 to 0.9. The time interval from arrhythmia detection to defibrillation preparation completion is set to 1-3 seconds; the longer the time interval, the greater the energy adjustment range. The historical response adjustment factor is set based on the energy response data of the previous 15 defibrillations, ranging from 0.8 to 1.2. A lower value is used if previous low-energy defibrillation was effective, and a higher value is used if it was ineffective. The upper limit for the number of previous defibrillations is set to 3, and the maximum safe number of defibrillations is also set to 3 to avoid excessive defibrillation and myocardial damage. The impedance change correction coefficient is set to 0.02 to 0.05. When the difference between the current impedance and the initial impedance exceeds 50Ω, energy compensation adjustment is initiated. The algorithm is implemented by embedding an optimization program in the DSP processor, using a particle swarm optimization algorithm to solve for the optimal energy value. The energy adjustment step size is 5J, and the output defibrillation energy range is controlled between 50-200J. The core significance lies in comprehensively considering the pathological state, individual patient characteristics, equipment contact status, and historical response data to achieve dynamic optimization of defibrillation energy. This minimizes the risk of myocardial damage while ensuring defibrillation effectiveness, solving the problems of poor adaptability and excessively high or low energy in traditional fixed-energy defibrillation.

[0031] Preferably, the data interaction model expression of the mobile emergency medical data management platform module is as follows: ,in, This is a value used to assess the quality of data transmission and storage. for Time-based data priority weights for Time-based data volume Encrypt hash function for data, To increase the loudness factor, The communication distance between the mobile device and the defibrillator. For data transmission duration, It is the data correction factor at time t. It is the variance of the error term at time t. It is the communication distance attenuation coefficient at time t. It is the transmission efficiency weight at time t.

[0032] Specifically, the data interaction model of the mobile emergency medical data management platform module is implemented on Android / iOS mobile terminals. It ensures the quality of data transmission and storage through multi-factor weighted calculations. During the model's operation, various types of data in the emergency process are first prioritized. Real-time physiological parameters have a priority weight of 0.15, defibrillation operation data has a weight of 0.12, and guidance feedback information has a weight of 0.08. The data volume dynamically changes according to the transmitted content. Real-time physiological data is transmitted at a rate of 20ms / frame, with each frame containing 16 bytes. The data encryption hash function uses the SHA-256 algorithm. The encryption key is dynamically generated using device hardware information and a timestamp, with a key length of 256 bits. The data integrity verification coefficient ranges from 0.9 to 0.98, used to verify the integrity of data transmission and prevent data loss or tampering. The network latency coefficient is adjusted in real time according to the communication link status, ranging from 0.1 to 0.3 for Bluetooth communication and from 0.01 to 0.05 for 5G communication. The data attenuation factor ranges from 0.85 to 0.95, and the transmission distance impact coefficient ranges from 0.001 to 0.005. A signal enhancement mechanism is activated when the communication distance exceeds 10 meters. The data transmission duration dynamically changes according to the duration of the emergency rescue process, with a single emergency rescue data transmission duration of 5-30 minutes. This model is implemented by integrating a data interaction and computation module into a mobile application, using the TLS 1.3 encryption protocol for data transmission, and employing a dual-storage architecture (local and cloud). Data synchronization latency is controlled within 1 second. Its core significance lies in ensuring the security, integrity, and real-time performance of emergency data transmission through data priority allocation, encryption verification, dynamic latency compensation, and distance adjustment. This enables trusted sharing and efficient interaction of data across multiple terminals, bridging the information gap between out-of-hospital emergency care and in-hospital treatment.

[0033] Preferably, the operation guidance generation model expression of the real-time emergency operation guidance module is: ,in, Output intensity and frequency control parameters to guide operation. To guide the weighting coefficients, This is a function relating the pressing parameters to the electrode position. for The value of the fusion of pressing depth and frequency at any given moment. for The deviation value of the electrode bonding position at any time. For the first Class guide pattern weights, For the first Class of guide signal parameters, To guide the feedback response coefficient, This is the bias sensitivity coefficient. The standard press parameter threshold, This indicates the number of guide modes.

[0034] Specifically, the real-time emergency operation guidance module generates operation guidance models that output guidance signals adapted to actual operation scenarios through multi-factor dynamic calculations. During the model's calculation, real-time data on compression parameters and electrode positions are first acquired. The fused value of compression depth and frequency is obtained through weighted calculation, with depth weighted at 0.6 and frequency weighted at 0.4. Standard compression parameter thresholds are set at a depth of 5-6 cm and a frequency of 100-120 compressions / minute. Electrode placement deviation is measured using image recognition technology, ranging from 0-10 cm. A correlation function between compression parameters and electrode positions quantifies their synergistic relationship. The guidance weight coefficient ranges from 0.7 to 0.9, used to adjust the overall strength of the guidance signal. Guidance mode weights are set according to the operation steps: electrode placement guidance weight is 0.35, compression operation guidance weight is 0.3, and defibrillation timing prompt weight is 0.35. Guidance signal parameters include indicators such as voice volume, display brightness, and vibration intensity. The guidance feedback response coefficient ranges from 0.8 to 1.1, used to adjust the guidance intensity based on user feedback. The deviation sensitivity coefficient ranges from 1.5 to 2.8; when the operational deviation exceeds a threshold, the adjustment amplitude of the guidance signal is amplified. There are a fixed number of guidance modes, corresponding to the three core aspects of electrode placement, chest compressions, and defibrillation timing. The model is implemented by embedding a computational program in the logic control unit to receive operational feedback data in real time and dynamically adjust guidance parameters. The guidance signal output delay is controlled within 200ms. Its core significance lies in generating precise and adaptable multimodal operational guidance through correlation analysis between chest compressions and electrode positions, dynamic weight allocation, and deviation sensitivity adjustment. This helps non-professional users quickly correct operational deviations, standardize emergency procedures, lower the operational threshold, and improve the accuracy and standardization of emergency operations.

[0035] Preferably, the low-energy defibrillation efficiency optimization algorithm module includes a defibrillation parameter preprocessing unit, an energy response prediction unit, a multi-constraint optimization unit, and a parameter output calibration unit. The defibrillation parameter preprocessing unit receives myocardial electrical activity pattern matching results, electrode contact impedance detection data, and estimated values ​​of patient physiological parameters. It performs time-series alignment and outlier filtering on the data and extracts effective feature parameters through signal amplification and noise suppression. The energy response prediction unit constructs an energy-effect correlation mapping based on historical defibrillation data and predicts defibrillation success rate correlation indicators at different energy levels by combining current cardiac rhythm parameters. The multi-constraint optimization unit constructs a multi-objective optimization function by integrating impedance change trends, patient physical parameters, and emergency environment factors, with energy minimization and tissue damage risk as constraints. The parameter output calibration unit dynamically calibrates the optimized energy parameters and adjusts the output parameters based on real-time collected electrode contact state changes to ensure that the defibrillation energy accurately matches the individual characteristics of the patient.

[0036] Specifically, the low-energy defibrillation efficiency optimization algorithm module comprises internal units and a collaborative operation mechanism, with each unit operating in an orderly manner based on a DSP processor hardware platform. The defibrillation parameter preprocessing unit receives pathological type and severity grading data from myocardial electrical activity pattern matching results, simultaneously acquiring electrode contact impedance (measurement range 50-500Ω) and patient weight estimates (error ±3kg). A time-domain alignment algorithm unifies the timestamps of multi-source data to the millisecond level. Outliers such as impedance mutations and abnormal weight estimates are filtered using the 3σ criterion. High-frequency noise is then removed using an 8th-order Butterworth low-pass filter (cutoff frequency 10Hz), extracting effective feature parameters such as impedance change rate and energy baseline values ​​corresponding to weight, with a processing delay ≤100ms. The energy response prediction unit calls upon the built-in historical defibrillation database (storing over 100,000 case data), using a K-nearest neighbor algorithm to match cases with ≥85% similarity to the current patient's pathological state and physical parameters, constructing an energy-defibrillation success rate correlation curve, and predicting response indicators corresponding to different energy levels within the 50-200J range, with a prediction error ≤5%. The multi-constraint optimization unit, with energy minimization and a myocardial injury risk below 10% as its core constraints, integrates influencing factors such as impedance change trends (updated every 100ms), patient age (indirectly derived through a weight estimation model), and emergency environment temperature (0-40℃) to construct a nonlinear optimization objective function. The optimal solution is found using the gradient descent method. The parameter output calibration unit compares and optimizes energy parameters with the hardware output capability (maximum output 360J) in real time. When electrode contact impedance fluctuations exceed ±20Ω, the energy value is dynamically calibrated by 0.2J per Ω, ensuring accurate matching of output parameters with individual patient characteristics. The entire unit operates collaboratively in ≤200ms. Its core significance lies in improving the accuracy and safety of defibrillation energy parameters through multi-stage data processing and optimization calibration, providing technical support for efficient and low-damage defibrillation.

[0037] Preferably, the mobile emergency medical data management platform module includes a real-time data receiving unit, an encrypted storage unit, a multi-terminal synchronization unit, and an emergency medical record generation unit. The real-time data receiving unit receives physiological parameters, defibrillation operation data, and guidance feedback information transmitted by the defibrillator via Bluetooth and 5G dual-mode communication links, and uses streaming data processing technology for high-concurrency data reception and parsing. The encrypted storage unit uses the AES-256 encryption algorithm to encrypt the emergency medical data and combines a distributed storage architecture for local data backup and cloud synchronization. The multi-terminal synchronization unit uses blockchain technology to achieve trusted sharing and access control of emergency medical data among emergency personnel terminals, hospital systems, and emergency center platforms. The emergency medical record generation unit automatically extracts and calibrates data nodes and generates an electronic emergency medical record in a standardized format, including a heart rate change curve, defibrillation parameter details, and an operation timeline.

[0038] Specifically, the mobile emergency medical data management platform module has internal unit divisions, with each unit operating collaboratively based on the mobile terminal operating system architecture. The real-time data receiving unit is equipped with a Bluetooth 5.2 BLE and 5G dual-mode communication module. Bluetooth communication has an effective distance of ≤10 meters and a transmission rate of ≥2Mbps, while 5G communication latency is ≤10ms. It uses a streaming data processing framework to receive physiological parameters (sampling frequency 500Hz), defibrillation operation data (one data record triggered per operation), and guidance feedback information transmitted from the defibrillator. Erroneous data is removed through data frame verification (CRC32 algorithm), and after parsing, the data is temporarily stored in a buffer (1GB capacity) categorized by data type, with a reception success rate of ≥99.9%. The encrypted storage unit uses the AES-256 symmetric encryption algorithm to encrypt emergency medical data. The key is dynamically generated using the device IMEI code and timestamp (updated hourly). Local storage uses an SQLite database (single database capacity ≥32GB), supporting data indexing by emergency medical timestamp. Cloud synchronization uses Alibaba Cloud OSS distributed storage architecture, supporting breakpoint resume to ensure no data loss. The multi-terminal synchronization unit constructs a trusted data sharing network based on blockchain technology. Nodes include emergency personnel terminals, hospital HIS systems, and emergency center platforms. Access permissions are set using smart contracts (three-level permission control). During data synchronization, an immutable timestamp and hash value are generated, with a synchronization delay of ≤1 second, ensuring the reliability and security of data transmission. The emergency record generation unit automatically extracts key data nodes from the emergency process (time of cardiac arrhythmia identification, defibrillation trigger time, etc.) and generates electronic records according to national medical data standards. These records include ECG time-series curves (≥10,000 sampling points), detailed defibrillation parameters (energy value, number of times, timing), and an operation timeline (accurate to the second). PDF export and printing are supported, and data retention is ≥5 years. The core significance lies in achieving secure management and efficient sharing of emergency data throughout the entire process, providing complete data support for emergency traceability, subsequent diagnosis and treatment, and algorithm optimization.

[0039] Preferably, the real-time emergency operation guidance module includes an electrode adhesion guidance unit, a chest compression guidance unit, a defibrillation timing prompting unit, and an emergency adjustment feedback unit. The electrode adhesion guidance unit uses image recognition technology to capture feature points on the patient's body surface, combines them with a preset electrode positioning template to generate a visual adhesion area marker, and assists in adjusting the electrode position with voice prompts until the contact impedance requirements are met. The chest compression guidance unit collects chest compression depth, frequency, and chest compression point offset data in real time, converts them into audio beat signals and visual progress bar prompts, and synchronously outputs chest compression pressure adjustment instructions. The defibrillation timing prompting unit receives heart rate discrimination results and defibrillation parameter optimization signals, and prompts the operator to initiate defibrillation through a synchronized audio-visual alarm during the optimal defibrillation window. The emergency adjustment feedback unit monitors operational deviations in real time and dynamically adjusts the intensity and form of the guidance signal based on ambient light and noise intensity to ensure the effectiveness of the operation guidance.

[0040] Specifically, the internal unit functions of the real-time emergency operation guidance module rely on the module's hardware components and logic control unit to work collaboratively. The electrode placement guidance unit is equipped with a high-definition camera (1920×1080 resolution) and image recognition algorithm. By capturing the patient's chest contour feature points (recognition accuracy ≤1cm), it compares them with preset electrode positioning templates (including standard positioning data for different body types and positions), and generates a red virtual mark (2cm in diameter) on the LCD screen to mark the placement position. The voice prompt uses a female Chinese voice at a speed of 150 words / minute. When the electrode contact impedance is ≥150Ω, it repeatedly prompts to adjust the electrode position until the impedance is ≤100Ω. The compression guidance unit collects compression data using a pressure sensor (measurement range 0-100N, accuracy ±1N) and an accelerometer (sampling frequency 100Hz), converting it into a green progress bar (full bar corresponds to 6cm depth) and a blue waveform (corresponding to compression frequency). The audio beat signal frequency is 100-120Hz. When the compression depth is <5cm or the frequency is <100 compressions / minute, the beat signal intensity is increased by 30%, with synchronized voice prompts to adjust the amplitude. The defibrillation timing prompt unit receives real-time heart rhythm discrimination results (abnormal confidence ≥0.8) and defibrillation parameter optimization signals. During the optimal defibrillation window of 20-30ms after the R wave, a red LED light flashes (frequency 2Hz) and a buzzer alarm sounds (volume 90dB), while a voice prompt says "Defibrillation is about to begin, please move away from the patient," which continues until defibrillation is complete. The emergency adjustment feedback unit monitors environmental parameters in real time through an ambient light sensor (measurement range 10-10000 lux) and a noise sensor (measurement range 30-120 dB). When the ambient light is ≥5000 lux, the display brightness is increased to 500 cd / m²; when the ambient noise is ≥80 dB, the voice volume is increased to 100 dB, ensuring that the operation instructions remain clearly readable in complex environments. The core significance lies in helping non-professional users quickly and standardizedly complete first aid operations through multi-dimensional perception and adaptive guidance, reducing operational deviations and improving the success rate of out-of-hospital emergency care.

[0041] like Figure 2As shown, a portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios includes the following steps: S1, acquiring surface electrocardiogram signals, myocardial impedance signals, and blood oxygen saturation-related parameters of a patient experiencing sudden cardiac arrest via the lead electrode array of the multi-parameter heart rhythm signal acquisition module, and transmitting these multi-dimensional physiological parameters to the multi-parameter self-learning heart rhythm discrimination algorithm module at a preset sampling frequency; S2, the multi-parameter self-learning heart rhythm discrimination algorithm module extracts features from the received parameters, completes the identification of heart rhythm abnormalities based on a dynamically updated sample library, and outputs preliminary discrimination results; S3, the preliminary discrimination results are input into the myocardial electrical activity pattern matching model module, and compared with a preset pathological state pattern library for multi-dimensional feature matching to generate pattern matching results; 4. The pattern matching result is transmitted to the low-energy defibrillation efficiency optimization algorithm module. Combining electrode contact impedance, estimated patient weight, and previous defibrillation response data, the algorithm calculates the optimized defibrillation energy parameters. S5. The defibrillation parameters and operation instructions output by the low-energy defibrillation efficiency optimization algorithm module are synchronized to the mobile emergency data management platform module via a wireless communication link to complete real-time data storage, encrypted transmission, and multi-terminal sharing. S6. The mobile emergency data management platform module triggers the real-time emergency operation guidance module. Through voice prompts, visual interface guidance, and tactile feedback mechanisms, it outputs operation guidance related to electrode placement calibration, compression rhythm control, and defibrillation timing selection, providing full-process technical support from signal acquisition to operation execution.

[0042] This portable defibrillator emergency operation guidance system, designed for sudden cardiac arrest scenarios, integrates multiple signals such as ECG, myocardial impedance, and blood oxygen correlation through a multi-dimensional physiological parameter acquisition module. It dynamically updates its feature sample library using a multi-parameter self-learning rhythm discrimination capability, and then performs multi-dimensional feature comparison using a myocardial electrical activity pattern matching model. This overcomes the limitations of traditional single-parameter discrimination and significantly improves the accuracy of arrhythmia identification. Simultaneously, its low-energy defibrillation efficiency optimization mechanism fully integrates electrode contact status, patient physical condition estimation data, and previous emergency response information, dynamically adjusting defibrillation energy parameters. This avoids the problem of insufficient adaptation of fixed energy output to different patients, improving defibrillation effectiveness while reducing the risk of additional myocardial damage, achieving individualized and precise treatment.

[0043] This invention addresses the pain points of existing technologies, such as high operational barriers and poor data collaboration. The real-time emergency operation guidance module uses a multimodal approach, including visual interface icons, voice prompts, and tactile feedback, to assist non-professional users in quickly calibrating electrode placement, standardizing compression rhythm, and grasping the timing of defibrillation, effectively correcting operational errors prone to occur with traditional single-guidance methods. The mobile emergency data management platform employs encrypted storage and dual-mode communication technology to achieve real-time backup, cloud synchronization, and reliable sharing of emergency data across multiple terminals. It establishes information links between emergency personnel, hospitals, and emergency centers, ensuring complete traceability of emergency records and continuity of subsequent medical care, providing a comprehensive solution from operational support to data assurance for out-of-hospital emergency care for sudden cardiac arrest.

[0044] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0045] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios, characterized in that: include: Multi-parameter self-learning heart rhythm discrimination algorithm module, myocardial electrical activity pattern matching model module, low-energy defibrillation efficiency optimization algorithm module, mobile emergency data management platform module, portable defibrillator operation guidance module, and multi-module collaborative control module; The multi-parameter self-learning rhythm discrimination algorithm module constructs a dynamic discrimination network by collecting myocardial electrical signals, heart rate variability parameters, and electrocardiogram cycle characteristic parameters. It interacts with the myocardial electrical activity pattern matching model module in real time through a high-speed data bus. The myocardial electrical activity pattern matching model module transmits the matched pattern feature vector to the low-energy defibrillation efficiency optimization algorithm module. The low-energy defibrillation efficiency optimization algorithm module generates an optimized defibrillation parameter set by combining defibrillation voltage threshold, pulse width parameter, and electrode contact impedance parameter. The mobile emergency data management platform module receives the optimized defibrillation parameter set and rhythm discrimination results through a wireless communication link and performs encrypted storage and classification indexing. The portable defibrillator operation guidance module generates visual guidance instructions based on the parameters transmitted from the mobile terminal and the real-time operation status. The multi-module collaborative control module dynamically schedules the data transmission timing and operation priority of each module through a timing synchronization protocol to achieve closed-loop collaboration of rhythm discrimination, pattern matching, defibrillation optimization, data management, and operation guidance.

2. The portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios according to claim 1, characterized in that, The discrimination algorithm expression used by the multi-parameter self-learning heart rhythm discrimination algorithm module is as follows: ,in, The discriminant coefficient for cardiac arrhythmias is... For the first Collect parameters of type These are the parameter weighting coefficients. Adjusting parameters for algorithm self-learning For the parameter feature mapping function, For the first Class feature filtering function, The characteristic sensitivity coefficient, The threshold for the feature mean. As a time-series penalty factor, for Time-series multi-parameter fusion vector For the fusion matrix, For time-series correlation functions, For signal acquisition duration, It is an L2 norm.

3. The portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios according to claim 1, characterized in that, The matching model expression used by the myocardial electrical activity pattern matching model module is: ,in, For pattern matching similarity values, These are real-time myocardial electrical activity characteristic parameters. Preset pathological pattern characteristic parameters, For feature matching weights, The covariance calculation function, , The characteristic adjustment coefficient, For the real-time parameter standard deviation, The standard deviation of the preset parameter, The mode attenuation coefficient, As a differentially sensitive factor, This represents the number of dimensions of the feature parameters.

4. The portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios according to claim 1, characterized in that, The optimization algorithm expression used in the low-energy defibrillation efficiency optimization algorithm module is as follows: ,in, To optimize the defibrillation energy value, The conductivity coefficient of the tissue. The electrode contact resistance, For the patient's estimated weight, The energy transfer efficiency coefficient. The time interval from the recognition of arrhythmia to the completion of defibrillation preparation. Historical response moderating factor, This refers to the number of previous defibrillation attempts. The maximum safe number of defibrillation attempts threshold. This is the impedance change correction factor. This is the difference between the current impedance and the initial impedance.

5. The portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios according to claim 1, characterized in that, The data interaction model expression of the mobile emergency medical data management platform module is as follows: ,in, This is a value used to assess the quality of data transmission and storage. for Time-based data priority weights for Time-based data volume Encrypt hash function for data, To increase the loudness factor, The communication distance between the mobile device and the defibrillator. For data transmission duration, It is the data correction factor at time t. It is the variance of the error term at time t. It is the communication distance attenuation coefficient at time t. It is the transmission efficiency weight at time t.

6. The portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios according to claim 1, characterized in that, The operation guidance generation model expression of the real-time emergency operation guidance module is: ,in, Output intensity and frequency control parameters to guide operation. To guide the weighting coefficients, This is a function relating the pressing parameters to the electrode position. for The value of the fusion of pressing depth and frequency at any given moment. for The deviation value of the electrode bonding position at any time. For the first Class guide pattern weights, For the first Class of guide signal parameters, To guide the feedback response coefficient, This is the bias sensitivity coefficient. The standard press parameter threshold, This indicates the number of guide modes.

7. The portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios according to claim 1, characterized in that, The low-energy defibrillation efficiency optimization algorithm module includes a defibrillation parameter preprocessing unit, an energy response prediction unit, a multi-constraint optimization unit, and a parameter output calibration unit. The defibrillation parameter preprocessing unit receives myocardial electrical activity pattern matching results, electrode contact impedance detection data, and estimated values ​​of patient physiological parameters. It performs time-series alignment and outlier filtering on the data and extracts effective feature parameters through signal amplification and noise suppression. The energy response prediction unit constructs an energy-effect correlation mapping based on historical defibrillation data and predicts defibrillation success rate correlation indicators at different energy levels by combining current cardiac rhythm parameters. The multi-constraint optimization unit constructs a multi-objective optimization function by integrating impedance change trends, patient physical parameters, and emergency environment factors, with constraints of minimizing energy and minimizing tissue damage risk. The parameter output calibration unit dynamically calibrates the optimized energy parameters and adjusts the output parameters in conjunction with real-time acquisition of changes in electrode contact status to ensure that the defibrillation energy is accurately matched to the individual characteristics of the patient.

8. The portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios according to claim 1, characterized in that, The mobile emergency medical data management platform module includes a real-time data receiving unit, an encrypted storage unit, a multi-terminal synchronization unit, and an emergency record generation unit. The real-time data receiving unit receives physiological parameters, defibrillation operation data, and guidance feedback information transmitted by the defibrillator via Bluetooth and 5G dual-mode communication links, and uses streaming data processing technology for high-concurrency data reception and parsing. The encrypted storage unit uses the AES-256 encryption algorithm to encrypt emergency medical data and combines a distributed storage architecture for local data backup and cloud synchronization. The multi-terminal synchronization unit uses blockchain technology to ensure trusted sharing and access control of emergency medical data among emergency personnel terminals, hospital systems, and emergency center platforms. The emergency record generation unit automatically extracts and calibrates data nodes and generates electronic emergency medical records in a standardized format, including heart rate change curves, defibrillation parameter details, and operation timelines.

9. The portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios according to claim 1, characterized in that, The real-time emergency operation guidance module includes an electrode attachment guidance unit, a chest compression guidance unit, a defibrillation timing prompt unit, and an emergency adjustment feedback unit. The electrode attachment guidance unit uses image recognition technology to capture the patient's surface feature points and generates a visual attachment area marker based on a preset electrode positioning template. It assists in adjusting the electrode position with voice prompts until the contact impedance requirements are met. The chest compression guidance unit collects chest compression depth, frequency, and chest compression point offset data in real time, converts them into audio beat signals and visual progress bar prompts, and simultaneously outputs chest compression pressure adjustment instructions. The defibrillation timing prompt unit receives heart rate discrimination results and defibrillation parameter optimization signals, and prompts the operator to initiate defibrillation through a synchronized audio-visual alarm during the optimal defibrillation window. The emergency adjustment feedback unit monitors operational deviations in real time and dynamically adjusts the intensity and form of the guidance signals based on ambient light and noise levels to ensure the effectiveness of the operation guidance.

10. The portable defibrillator emergency operation guidance system for sudden cardiac arrest scenarios according to any one of claims 1-9, characterized in that, The system operates in the following steps: S1. The multi-parameter heart rhythm signal acquisition module acquires surface electrocardiogram signals, myocardial impedance signals, and blood oxygen saturation-related parameters of patients with sudden cardiac arrest through the lead electrode array. These multi-dimensional physiological parameters are then transmitted to the multi-parameter self-learning heart rhythm discrimination algorithm module at a preset sampling frequency. S2. The multi-parameter self-learning heart rhythm discrimination algorithm module extracts features from the received parameters, identifies arrhythmias based on a dynamically updated sample library, and outputs preliminary discrimination results. S3. The preliminary discrimination results are input into the myocardial electrical activity pattern matching model module, which compares them with a preset pathological state pattern library for multi-dimensional feature matching, generating pattern matching results. S4. The pattern matching results are transmitted to a low-energy... The defibrillation efficiency optimization algorithm module, combining electrode contact impedance, estimated patient weight, and previous defibrillation response data, calculates optimized defibrillation energy parameters. S5, the defibrillation parameters and operation instructions output by the low-energy defibrillation efficiency optimization algorithm module are synchronized to the mobile emergency data management platform module via a wireless communication link, enabling real-time data storage, encrypted transmission, and multi-terminal sharing. S6, the mobile emergency data management platform module triggers the real-time emergency operation guidance module, providing operational guidance on electrode placement calibration, chest compression rhythm control, and defibrillation timing selection through voice prompts, visual interface guidance, and tactile feedback mechanisms, offering full-process technical support from signal acquisition to operation execution.

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