Intelligent electrocardio lead system based on adaptive impedance matching and neural network identification

Abstract

The present application relates to the technical field of medical electronic equipment, in particular to an intelligent electrocardio lead system based on adaptive impedance matching and neural network identification, which collects multi-lead multi-frequency band impedance data of human body through an impedance feature extraction module, constructs a differential feature vector and generates a parameterized impedance representation; a neural network identification module makes hierarchical lead position judgment accordingly, outputs a position result and a confidence level; an adaptive optimization module updates parameters in combination with operator feedback, performs multi-level learning to optimize performance; an interactive feedback module generates a voice prompt to guide the operator in real time, realizes intelligent recognition and adaptive optimization of lead position, improves electrocardio detection accuracy and operation convenience, significantly improves electrocardiogram examination accuracy, and effectively avoids misdiagnosis and missed diagnosis caused by lead errors.

Description

Technical Field

[0001] This invention relates to the field of medical electronic equipment technology, and in particular to an intelligent electrocardiogram (ECG) lead system based on adaptive impedance matching and neural network recognition, used to achieve accurate identification and verification of ECG lead positions. Background Technology

[0002] Electrocardiogram (ECG) is one of the most commonly used non-invasive examination methods in clinical practice, and its accuracy is directly related to the diagnosis and treatment of heart diseases. However, during the ECG examination, operators are very prone to reversing the limb lead electrodes (such as confusing the left and right hands) or placing the chest leads (V1-V6) inaccurately, resulting in distorted ECG waveforms, requiring repeated operations, delaying diagnosis time, and even potentially leading to misdiagnosis.

[0003] Most existing electrocardiographs are simple signal acquisition devices, lacking intelligent verification functions to ensure correct lead placement. While some high-end devices have basic impedance detection capabilities, they can only determine if electrodes have become detached, not whether their positions are correct. Furthermore, traditional devices use fixed threshold values, which cannot adapt to impedance variations caused by different patient body shapes, skin conditions, and environmental conditions, resulting in limited accuracy.

[0004] With the development of artificial intelligence technology, combining deep learning with medical devices has become a new trend. However, there is currently no ECG lead recognition system on the market that deeply integrates bioimpedance measurement and neural network technology, making it impossible to achieve intelligent and personalized lead position verification. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent ECG lead system based on adaptive impedance matching and neural network recognition. Through three major technological innovations—multi-channel differential impedance feature extraction, hierarchical neural network lead position recognition, and bidirectional feedback adaptive learning—it achieves accurate identification and verification of ECG lead positions, thereby eliminating operational errors at the source.

[0006] This invention proposes an intelligent ECG lead system based on adaptive impedance matching and neural network recognition, comprising:

[0007] Impedance feature extraction module, used for:

[0008] Collect multi-lead, multi-frequency human body impedance characteristic data;

[0009] Construct the difference feature vector;

[0010] Parametric impedance characterization of the generated lead position;

[0011] The neural network recognition module, electrically connected to the impedance feature extraction module, is used for:

[0012] Receive the parameterized impedance characterization sent by the impedance feature extraction module;

[0013] Based on the parameterized impedance characterization, hierarchical lead position determination is performed;

[0014] Generate lead location determination results and confidence scores;

[0015] The adaptive optimization module is electrically connected to both the impedance feature extraction module and the neural network recognition module, and is used for:

[0016] Receive the lead position determination result and confidence score generated by the neural network recognition module;

[0017] Update system parameters based on operator feedback and assessment results;

[0018] Perform multi-level adaptive learning to optimize model performance;

[0019] The interactive feedback module, electrically connected to the neural network recognition module, is used for:

[0020] Receive the lead position determination result and confidence score generated by the neural network recognition module;

[0021] Based on the lead position determination result and confidence score, a voice prompt message is generated;

[0022] The voice prompt information is output to the operator.

[0023] Preferably, the impedance feature extraction module includes:

[0024] A multi-frequency excitation unit is used to generate excitation signals at multiple frequency points;

[0025] An impedance acquisition unit, electrically connected to the multi-frequency excitation unit, is used to acquire impedance data at multiple frequency points at multiple lead positions.

[0026] The feature construction unit, electrically connected to the impedance acquisition unit, is used to convert the impedance data into a feature vector containing static impedance value, dynamic rate of change, frequency response characteristics and time-domain stability index.

[0027] The quality assessment unit, electrically connected to the feature construction unit, is used to calculate the reliability index of the feature vector and filter out unreliable data.

[0028] Preferably, the feature construction unit is specifically used for:

[0029] Calculate the impedance ratio between different lead positions and construct a relative characteristic index;

[0030] Extract the frequency response curve characteristics of impedance data at different frequency points;

[0031] Analyze the time-domain fluctuation characteristics of impedance data;

[0032] The above features are combined to form a standardized feature vector.

[0033] Preferably, the neural network recognition module includes:

[0034] A front-end feature network unit is used to receive the parameterized impedance representation and extract high-level feature representations;

[0035] A lead type classification unit, electrically connected to the front-end feature network unit, is used to classify leads into limb leads and chest leads;

[0036] The precise location determination unit is electrically connected to the lead type classification unit and is used to perform precise location identification for each type of lead.

[0037] An error type analysis unit, electrically connected to the precise location determination unit, is used to identify possible lead error types;

[0038] The context verification unit is electrically connected to the position accuracy determination unit and the error type analysis unit, respectively, and is used to perform global consistency verification based on anatomical constraints.

[0039] Preferably, the context verification unit is specifically used for:

[0040] Verify the spatial continuity of leads V1-V6 in the chest.

[0041] Analyze the symmetry characteristics of the leads in the left and right limbs;

[0042] Check the overall consistency of the multi-lead impedance characteristics;

[0043] The judgment results are corrected based on the anatomical relationship of the lead positions.

[0044] Preferably, the adaptive optimization module includes:

[0045] Instant adaptation unit for rapid parameter adjustment in response to the current measurement;

[0046] A conversational learning unit, electrically connected to the immediate adaptation unit, is used to accumulate knowledge during the examination of a single patient to form a temporary personalized model.

[0047] A long-term optimization unit, electrically connected to the conversation learning unit, is used to slowly adjust model parameters across patients to improve general performance.

[0048] The parameter storage unit is electrically connected to the immediate adaptation unit, the conversation learning unit, and the long-term optimization unit, respectively, and is used to store optimization parameters at each level.

[0049] Preferably, the adaptive optimization module further includes:

[0050] The feedback analysis unit is used to capture the operator's response behavior to system prompts;

[0051] The model evaluation unit, electrically connected to the feedback analysis unit, is used to evaluate the accuracy of the system based on operator feedback.

[0052] An optimization strategy unit, electrically connected to the model evaluation unit, is used to dynamically adjust learning parameters based on the evaluation results.

[0053] Preferably, the interactive feedback module includes:

[0054] The confidence analysis unit is used to perform hierarchical processing on the lead position determination results and confidence scores;

[0055] The prompt generation unit is electrically connected to the confidence analysis unit and is used to generate corresponding voice prompt content according to different confidence levels;

[0056] A voice output unit, electrically connected to the prompt generation unit, is used to convert the voice prompt content into a voice signal and play it.

[0057] A visual feedback unit, electrically connected to the confidence analysis unit, is used to present a visual indication of the lead status on the display screen.

[0058] Preferably, the confidence analysis unit is specifically used for:

[0059] The confidence level was divided into three levels: high, medium, and low.

[0060] For high-confidence results, generate confirmation messages;

[0061] For results with medium confidence, generate a prompt message;

[0062] For low-confidence results, generate warning messages and specific corrective suggestions.

[0063] As a preferred option, it also includes:

[0064] The initialization module is electrically connected to the impedance feature extraction module, the neural network recognition module, and the adaptive optimization module, respectively, and is used for:

[0065] Perform system self-test and sensor calibration;

[0066] Load the base model and personalized parameters;

[0067] Initialize the neural network inference engine;

[0068] The ECG acquisition module, electrically connected to the neural network recognition module, is used for:

[0069] After confirming that all leads are in the correct position, start ECG signal acquisition;

[0070] Continuously monitor the lead status to prevent it from falling off during the data collection process;

[0071] After the data collection is completed, a learning update signal is sent to the adaptive optimization module.

[0072] The present invention has the following beneficial effects:

[0073] 1. Significantly improves the accuracy of electrocardiogram examination, reducing the lead placement error rate from the traditional 22% to less than 5%, effectively avoiding misdiagnosis and missed diagnosis due to lead errors;

[0074] 2. Significantly improves operational efficiency, reducing electrocardiogram examination time by an average of 45%, especially in emergency situations such as first aid, saving valuable rescue time;

[0075] 3. Enhanced equipment adaptability, accurately determining lead placement for patients of different body types and skin conditions, improving adaptability by 300%;

[0076] 4. The learning curve has been optimized, reducing the training time for novice operators from an average of 3 days to half a day, significantly reducing human resource training costs;

[0077] 5. Achieving intelligent equipment, transforming from a passive measuring tool to an active intelligent assistant, represents the development direction of medical equipment.

[0078] 6. Solve the problem of clinical lead wire tangling. Through the free-retractable lead wire storage system, the lead wire preparation time is reduced from an average of 45 seconds to 10 seconds, reducing lead wire tangling incidents by 90% and significantly improving the ease of operation.

[0079] 7. Improved adaptability for patients with special body types: The replaceable lead contact system increases the success rate of examinations for thin patients from 65% to 95%, reducing repetitive operations caused by poor contact. Attached Figure Description

[0080] Figure 1 This is a block diagram of the overall system structure of the present invention;

[0081] Figure 2 This is a schematic diagram of the impedance feature extraction module of the present invention;

[0082] Figure 3 This is a schematic diagram of the neural network recognition module structure of the present invention;

[0083] Figure 4This is a schematic diagram of the adaptive optimization module structure of the present invention;

[0084] Figure 5 This is a schematic diagram of the interactive feedback module structure of the present invention;

[0085] Figure 6 This is a system workflow diagram of the present invention. Detailed Implementation

[0086] Please refer to the attached document. Figure 1-6 The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of the invention.

[0087] like Figure 1 As shown, the intelligent ECG lead system based on adaptive impedance matching and neural network recognition provided by the present invention includes: an impedance feature extraction module 1, a neural network recognition module 2, an adaptive optimization module 3, an interactive feedback module 4, an initialization module 5, and an ECG acquisition module 6.

[0088] Impedance feature extraction module 1 is used to collect human body impedance feature data across multiple leads and frequency bands, construct differential feature vectors, and generate parameterized impedance representations of lead positions. In a preferred embodiment of the invention, impedance feature extraction module 1 employs a four-electrode method to achieve high-precision impedance measurement, while simultaneously collecting impedance data at multiple frequency points (5kHz, 20kHz, 50kHz, 100kHz) to comprehensively capture impedance features at different lead positions.

[0089] The impedance feature extraction module 1 employs an innovative retractable lead cable storage system in its hardware design, solving the clinical problems of tangled and inconvenient lead cables in traditional electrocardiographs. This system integrates multiple leads into functional groups within an automatic retrieval axis, specifically including: an upper limb lead group retrieval axis, a lower limb lead group retrieval axis, and a chest lead group retrieval axis (divided into two groups: V1-V3 and V4-V6). Each retrieval axis has a built-in elastic retrieval mechanism that automatically releases an appropriate length of lead cable according to measurement needs and automatically retracts it after use, preventing lead cables from tangling and significantly improving clinical efficiency, especially in emergency situations such as first aid.

[0090] The neural network recognition module 2 is electrically connected to the impedance feature extraction module 1. It receives the parameterized impedance representation sent by the impedance feature extraction module 1, performs hierarchical lead position determination based on this representation, and generates lead position determination results and confidence scores. The neural network recognition module 2 employs a multi-level neural network structure to sequentially complete feature extraction, lead type classification, precise position determination, and error type analysis.

[0091] The adaptive optimization module 3 is electrically connected to both the impedance feature extraction module 1 and the neural network recognition module 2. It receives the lead position determination results and confidence scores generated by the neural network recognition module 2, updates system parameters based on operator feedback and judgment results, and performs multi-level adaptive learning to optimize model performance. The adaptive optimization module 3 enables the system to continuously evolve, allowing it to adapt to different patient characteristics and environmental changes.

[0092] The interactive feedback module 4 is electrically connected to the neural network recognition module 2. It receives the lead position determination results and confidence scores generated by the neural network recognition module 2, generates voice prompts based on this information, and outputs these prompts to the operator. The interactive feedback module 4 acts as a bridge between the system and the operator, guiding the operator to correctly place the lead electrodes through clear voice prompts.

[0093] Initialization module 5 is electrically connected to impedance feature extraction module 1, neural network recognition module 2, and adaptive optimization module 3, respectively, and is used for self-testing, calibration, and model loading during system startup. ECG acquisition module 6 is electrically connected to neural network recognition module 2. After confirming that all lead positions are correct, it starts ECG signal acquisition and continuously monitors the lead status.

[0094] like Figure 2 As shown, the impedance feature extraction module 1 includes a multi-frequency excitation unit 11, an impedance acquisition unit 12, a feature construction unit 13, and a quality evaluation unit 14.

[0095] The multi-frequency excitation unit 11 is used to generate excitation signals at multiple frequency points. Preferably, the multi-frequency excitation unit 11 employs direct digital synthesis (DDS) technology to simultaneously generate sinusoidal excitation signals at four frequency points: 5kHz, 20kHz, 50kHz, and 100kHz, with the amplitude controlled below 400μA, meeting medical device safety standards. The multiple frequency points allow for comprehensive capture of the impedance characteristics of tissues at different depths. The low-frequency band (5kHz) primarily reflects the skin surface impedance, while the high-frequency band (100kHz) can penetrate surface tissue to obtain impedance information from deeper tissues.

[0096] Impedance acquisition unit 12 is electrically connected to multi-frequency excitation unit 11 to acquire impedance data at multiple frequency points from multiple leads. Impedance acquisition unit 12 uses a four-electrode method to measure impedance, injecting excitation current through two electrodes and measuring voltage through the other two electrodes, thereby eliminating the influence of electrode-skin contact impedance and improving measurement accuracy. In one embodiment of the invention, impedance acquisition unit 12 has a measurement resolution of 0.1Ω, a phase resolution of 0.1 degrees, and a sampling rate of 200Hz, sufficient to capture minute impedance changes.

[0097] The impedance acquisition unit 12 is equipped with an innovative replaceable lead contact system, solving the problem of poor adaptability of traditional fixed lead contacts to patients of different body types. The system includes:

[0098] Standard spherical metal contact head: suitable for patients of average size, employing an optimized elastic pressure system to ensure stable contact;

[0099] Adhesive-fit flat contact head: Designed specifically for slender patients, it adheres to the skin surface with medical conductive adhesive, significantly improving the stability of the lead position and the tightness of the contact, and reducing signal fluctuations caused by patient movement;

[0100] Quick-change interface: The lead wire ends use a standardized quick-change interface, which allows medical staff to replace the contact head in seconds according to the patient's body shape, without the need for complicated tools.

[0101] Clinical trials have shown that using adhesive planar contact tips improves contact impedance stability by 85%, signal quality by 40%, and lead position accuracy from 70% to 95% in slender patients, making it particularly suitable for patients who cannot remain still.

[0102] In another preferred embodiment of the present invention, the leads of the impedance acquisition unit 12 adopt a free-stretching design, and each set of leads incorporates a high-precision spring retraction mechanism and a positioning locking system. This mechanism mainly consists of the following parts:

[0103] Central storage shaft: Made of carbon fiber composite material, 15mm in diameter, with built-in small precision bearings to ensure rotational flexibility;

[0104] Elastic Retraction System: Employs medical-grade stainless steel torsion springs to provide a constant tension of 0.8-1.2N, ensuring that the lead wire can be automatically retracted after being stretched;

[0105] Conductive slip ring: Made of gold-palladium alloy material to ensure stable electrical connection during 360° rotation, with contact impedance fluctuation of less than 0.01Ω;

[0106] Positioning and locking mechanism: Similar to the locking design of a measuring tape, the operator can lock the length of the lead wire at any position to prevent accidental retraction.

[0107] The retractable lead wire has a maximum extended length of 1.5 meters and a retracted length of only 15cm, achieving a scaling ratio of 10:1. The outer layer of the lead wire is made of medical-grade silicone material, which has excellent flexibility and abrasion resistance, and can withstand more than 100,000 stretching cycles without reducing performance.

[0108] The replaceable lead contact features a quick-lock design, allowing for replacement with a simple 90° rotation. The connection impedance between the contact and the lead wire is less than 0.005Ω, ensuring unaffected measurement accuracy. The adhesive planar contact uses Ag / AgCl electrode material, with a surface area three times that of conventional spherical contacts, significantly reducing contact impedance and improving signal stability.

[0109] The feature construction unit 13 is electrically connected to the impedance acquisition unit 12 and is used to convert impedance data into a feature vector containing static impedance value, dynamic rate of change, frequency response characteristics, and time-domain stability index. The feature construction unit 13 first calculates the impedance ratio between different lead positions to construct relative feature indices, reducing the impact of individual differences. Secondly, it extracts the frequency response curve characteristics of the impedance data at different frequency points, including the rate of change of impedance with frequency and the change of impedance phase angle with frequency. Finally, it analyzes the time-domain fluctuation characteristics of the impedance data, including the standard deviation and coefficient of variation of short-time impedance fluctuations.

[0110] In a preferred embodiment of the present invention, the feature construction unit 13 constructs an 8-dimensional feature vector F for each lead location:

[0111] ,

[0112] in: The real impedance (Ω) represents the purely resistive component of the tissue; The imaginary impedance (Ω) represents the capacitive or inductive component of the tissue. The phase angle (in degrees) represents the complex phase of the impedance; The impedance time-varying rate (Ω / s) represents the rate at which the impedance changes with time. The standard deviation of the fluctuation (Ω) represents the stability of the impedance measurement; The frequency response characteristic (Ω / kHz) represents the relationship between impedance and frequency. is the first derivative of the frequency response (Ω / kHz²), and represents the slope of the frequency response curve; The time constant (ms) represents the combined characteristics of the tissue's resistance and capacitance.

[0113] In practical applications, taking a standard 12-lead electrocardiogram as an example, the system measures the impedance characteristics of four limb leads (RA, LA, RL, LL) and six precordial leads (V1-V6). For example, precordial lead V1 is located at the right sternal border in the fourth intercostal space, where the tissue structures are skin, subcutaneous fat, and pectoral muscles. Typical impedance values ​​are approximately 350Ω (R), -50Ω (X), and -8 degrees (φ). Precordial lead V5, located at the left anterior axillary line in the fifth intercostal space, has different tissue structures, and typical impedance values ​​are... Approximately 400Ω, X is approximately -65Ω. It is approximately -10 degrees Celsius. This location-specific impedance characteristic is an important basis for lead identification.

[0114] The quality assessment unit 14 is electrically connected to the feature construction unit 13 and is used to calculate the reliability index of the feature vector and filter unreliable data. The quality assessment unit 14 sets multiple quality thresholds, including a signal-to-noise ratio threshold, a stability threshold, and a consistency threshold. When the signal-to-noise ratio of the measured data is lower than 10 dB, or the coefficient of variation of the stability index is greater than 15%, or the consistency deviation of continuous measurements is greater than 20%, the data is marked as unreliable, and the system will automatically remeasure or adopt a backup strategy.

[0115] like Figure 3 As shown, the neural network recognition module 2 includes a front-end feature network unit 21, a lead type classification unit 22, a precise location judgment unit 23, an error type analysis unit 24, and a context verification unit 25.

[0116] The front-end feature network unit 21 is used to receive parameterized impedance representations and extract high-level feature representations. In a preferred embodiment of the present invention, the front-end feature network unit 21 adopts a three-layer fully connected neural network structure, with 8 nodes in the input layer (corresponding to 8-dimensional feature vectors), 32 nodes in the hidden layer, and 16 nodes in the output layer. The activation function used is the ReLU function. The mathematical expression of the front-end feature network unit 21 is as follows:

[0117] ,

[0118] ,

[0119] in: This refers to the j-th element of the input feature vector; This is the output value of the i-th node in the hidden layer; This is the output value of the k-th node in the output layer; These are the elements of the weight matrix from the input layer to the hidden layer; These are the elements of the weight matrix from the hidden layer to the output layer; These are the elements of the hidden layer bias vector; For the elements of the output layer bias vector; This is the ReLU activation function, which means taking the larger value between 0 and x.

[0120] In practical applications of ECG lead recognition, the front-end feature network can learn impedance characteristic patterns at different lead locations. For example, when an electrode placed in the right arm (RA) is actually placed in the left arm (LA), by analyzing its impedance characteristics, the front-end feature network can extract the feature representation of this misplacement, providing a basis for subsequent classification and judgment.

[0121] Lead type classification unit 22 is electrically connected to front-end feature network unit 21, and is used to classify leads into limb leads and chest leads. Lead type classification unit 22 uses a softmax classifier to map the output of the front-end feature network to the probability distributions of the two categories. Its mathematical expression is as follows:

[0122] ,

[0123] in: Given feature O, the lead belongs to category The probability of; For category The weight vector; This is the output vector of the front-end feature network; For category The bias term; The base of the natural logarithm is approximately 2.718; the denominator is the normalization factor, ensuring that the sum of the probabilities of all classes is 1.

[0124] In clinical applications, this step significantly simplifies subsequent location determination because the impedance characteristics of limb leads and chest leads differ significantly. For example, limb leads typically have higher impedance values ​​(500–1000 Ω) and lower frequency dependence, while chest leads typically have lower impedance values ​​(300–600 Ω) and more pronounced frequency dependence. By performing a preliminary coarse classification, the system can improve overall accuracy and reduce misjudgments.

[0125] The precise location determination unit 23 is electrically connected to the lead type classification unit 22 and is used to perform precise location identification for each type of lead. For limb leads (RA, LA, RL, LL), the precise location determination unit 23 uses a multi-class classification network; for chest leads (V1-V6), it uses a sequence labeling network, taking into account the spatial continuity of chest leads. The mathematical expression for precise location determination is as follows:

[0126] ,

[0127] in: Given feature O and lead category Below, the lead position is The probability of; For position The weight vector; For position The bias term; For category The number of positions in the chest leads (4 for limb leads, 6 for chest leads).

[0128] In ECG examination practice, the system can identify common lead placement errors. For example, when the operator mistakenly places the electrode that should be placed at the V2 position (left sternal border at the fourth intercostal space) at the V1 position (right sternal border at the fourth intercostal space), the system can detect this error and prompt the operator to make adjustments by analyzing the impedance characteristics of the two positions (e.g., the V1 position is closer to the right atrium, and the impedance phase angle is usually 2-3 degrees smaller).

[0129] Error type analysis unit 24 is electrically connected to position accuracy determination unit 23 and is used to identify possible lead error types. Error type analysis unit 24 presets several common error modes, such as left / right hand reversal, misalignment of adjacent chest leads, and overall shift of chest leads. The mathematical expression for error type analysis unit 24 is as follows:

[0130] ,

[0131] in: For error mode The matching score; The current position determined by the system The probability of; For error mode Expected position Probability distribution; For position Weighting factors; This represents the total number of positions. express The absolute value of.

[0132] In practical applications, for example, when the system detects that the judgment results of the six chest leads V1-V6 are shifted to the left by one position (i.e., the V1 electrode is actually placed at the V2 position, the V2 electrode is placed at the V3 position, and so on), the error type analysis unit 24 will identify this pattern as an overall leftward deviation error of the chest leads and generate specific correction suggestions: move all chest leads to the right by one intercostal space, instead of giving individual adjustment suggestions for each lead, which greatly improves the efficiency of operation.

[0133] The context verification unit 25 is electrically connected to the position accuracy judgment unit 23 and the error type analysis unit 24, respectively, and is used to perform global consistency verification based on anatomical constraints. The context verification unit 25 verifies the spatial continuity relationship of the chest leads V1-V6, analyzes the symmetry characteristics of the left and right limb leads, checks the overall consistency of the impedance characteristics of multiple leads, and corrects the judgment results based on the anatomical relationship of the lead positions.

[0134] In a preferred embodiment of the present invention, the context verification unit 25 employs a graph-based consistency verification algorithm, treating each lead position as a node in a graph and the anatomical relationships between leads as edges. The core algorithm of the context verification is as follows:

[0135] ,

[0136] ,

[0137] in: For position and Consistency score between them; and The feature vector at the corresponding position; The scaling parameter controls the sensitivity to feature differences. The score represents the overall graph inconsistency. For position The set of adjacent positions; For position pair The weighting reflects the importance of anatomical relationships; This represents the total number of leads.

[0138] In clinical practice, contextual validation can capture the overall pattern of lead placement. For example, in a standard 12-lead ECG, precordial leads V1-V6 should be arranged in a semi-circular pattern along the chest wall, with a smooth transition in impedance characteristics between adjacent leads. When the system detects that the impedance characteristics of lead V3 do not conform to the smooth transition relationship between V2 and V4, even if the single-point assessment of V3 might have high confidence, the system will reduce the confidence of that assessment and prompt the operator to re-examine the position of V3.

[0139] like Figure 4 As shown, the adaptive optimization module 3 includes an immediate adaptation unit 31, a conversation learning unit 32, a long-term optimization unit 33, a parameter storage unit 34, a feedback analysis unit 35, a model evaluation unit 36, and an optimization strategy unit 37.

[0140] The instantaneous adaptation unit 31 is used for rapid parameter adjustment based on the current measurement. The instantaneous adaptation unit 31 employs a sliding window technique to dynamically adjust the normalized parameters and judgment threshold of the impedance characteristics based on the most recent measurement results. The core algorithm of the instantaneous adaptation unit 31 is as follows:

[0141] ,

[0142] ,

[0143] ,

[0144] in: Features At the point of time The mean of the sliding window; Features At the point of time The standard deviation of the sliding window; These are the normalized eigenvalues; The sliding window size is preferably set to the number of data points for 5 to 10 seconds, corresponding to 1000 to 2000 sample points (200Hz sampling rate). This refers to the current time point; This is the time index within the window.

[0145] In practical applications, immediate adaptation can handle impedance fluctuations caused by short-term changes in a patient's condition. For example, when a patient sweats more due to anxiety, skin conductivity increases, and the overall impedance value decreases by 15% to 30%. Through immediate adaptation, the system can quickly adjust parameters to maintain accuracy. Clinical trials show that after enabling immediate adaptation, the system's accuracy in judging fluctuating patient conditions increased from 82% to 94%.

[0146] The conversational learning unit 32 is electrically connected to the immediate adaptation unit 31, and is used to accumulate knowledge throughout the examination process of a single patient, forming a temporary personalized model. The conversational learning unit 32 employs an incremental learning method, using the patient's feature data to fine-tune the neural network model after confirming correct lead placement. The core algorithm of the conversational learning is as follows:

[0147] ,

[0148] in: These are the model parameters at time point t; The learning rate is preferably set to 0.01-0.05; For the loss function with respect to the parameters In data gradient on; Data collected at time point t.

[0149] During electrocardiogram (ECG) examinations, conversational learning can adapt to individual patient characteristics. For example, in obese patients, the impedance values ​​in the chest leads are typically 20%–35% higher than expected by the standard model, and their frequency response characteristics also differ. Through conversational learning, the system can quickly adapt to these individual characteristics, significantly improving judgment accuracy in the latter half of the examination. Clinical validation shows that for patients with specific body types, the judgment accuracy after using conversational learning increased from the initial 75% to 93%.

[0150] The long-term optimization unit 33 is electrically connected to the conversation learning unit 32 for slowly adjusting model parameters across patients to improve general performance. The long-term optimization unit 33 periodically collects data from multiple patients and uses a batch update strategy to optimize the base model. The mathematical expression for long-term optimization is as follows:

[0151] ,

[0152] ,

[0153] in: For the collection of N patient data sets; These are the parameters of the current base model; These are the updated base model parameters; For long-term learning rate, it is preferably set to 0.001-0.005; For the loss function with respect to the parameters In data The gradient on.

[0154] In routine hospital applications, long-term optimization can adapt to long-term factors such as seasonal changes and equipment aging. For example, in the dry winter environment, patients' skin resistance generally increases by 10% to 20%. Through long-term optimization, the system can gradually adjust its basic model to adapt to this environmental change. Statistical data shows that after three months of long-term optimization, the performance difference of the system under different seasonal conditions decreased from the initial 12% to 3%.

[0155] The parameter storage unit 34 is electrically connected to the immediate adaptation unit 31, the session learning unit 32, and the long-term optimization unit 33, respectively, and is used to store optimization parameters at each level. The parameter storage unit 34 adopts a hierarchical storage structure: immediate parameters are stored in RAM, session parameters are stored in high-speed flash memory, and long-term parameters are stored in non-volatile memory. This hierarchical storage strategy balances access speed and data persistence, ensuring that the system can function normally under various conditions.

[0156] The feedback analysis unit 35 is used to capture the operator's response behavior to system prompts. The feedback analysis unit 35 monitors the operator's adjustment actions after receiving a prompt, including the direction, magnitude, and time delay of the adjustment. By analyzing these behavioral patterns, the system can evaluate the effectiveness and accuracy of the prompts. The mathematical model for feedback analysis is as follows:

[0157] ,

[0158] ,

[0159] in: The response record at time point t includes the action type. Adjust direction and characteristic changes Scoring for the effectiveness of the response; The similarity between the actual adjustment direction and the expected direction; To adjust for the resulting characteristic changes; This represents the expected characteristic changes.

[0160] In clinical practice, feedback analysis can assess the operator's skill level and the effectiveness of prompts. For example, when the system prompts to check the left-hand electrode position, if the operator immediately adjusts the left-hand electrode and the impedance characteristics change in the expected direction, the prompt is considered effective. Conversely, if the operator adjusts to another position or the change in characteristics after adjustment does not conform to expectations, the prompt may be unclear or inaccurate. The system adjusts its prompting strategy based on this feedback, providing more detailed guidance for novices and more concise prompts for experts.

[0161] The model evaluation unit 36 ​​is electrically connected to the feedback analysis unit 35 and is used to evaluate the accuracy of the system's judgments based on operator feedback. The model evaluation unit 36 ​​calculates performance metrics such as accuracy, recall, and F1 score by analyzing the consistency between the operator's response and the system's expectations. The core formula for model evaluation is as follows:

[0162] ,

[0163] ,

[0164] ,

[0165] Where: Acc is accuracy; Rec is recall; F1 is F1 score; TP is the number of true positives, i.e., cases where the system judges the error and the operator adjusts it to confirm the error; FP is the number of false positives, i.e., cases where the system judges the error but the operator adjusts it to find that it is actually correct; FN is the number of false negatives, i.e., cases where the system judges the error but it is actually incorrect.

[0166] In actual operation within an electrocardiogram (ECG) room, model evaluation continuously monitors system performance. For example, an internal hospital assessment showed that the system achieved an accuracy of 98%, a recall of 95%, and an F1 score of 96.5% in routine examinations, significantly outperforming the 78% accuracy of traditional manual judgment. Model evaluation can also identify system weaknesses, such as the system's weak ability to distinguish between chest leads V3 and V4 (accuracy of only 92%), providing direction for further optimization.

[0167] The optimization strategy unit 37 is electrically connected to the model evaluation unit 36 ​​and is used to dynamically adjust the learning parameters based on the evaluation results. The optimization strategy unit 37 employs an adaptive learning rate strategy, increasing the learning weight for correctly judged samples and decreasing the learning weight for incorrectly judged samples. The core algorithm of the optimization strategy is as follows:

[0168] ,

[0169] ,

[0170] in: Let be the adaptive learning rate for sample i; Base learning rate; The attenuation coefficient; Let i be the error rate of sample i; The target error rate; Let be the learning weights for sample i; The temperature parameter controls the concentration of the weight distribution. The total number of samples.

[0171] During long-term system operation, optimization strategies can prevent overfitting and catastrophic forgetting. For example, when a system frequently encounters a specific patient group (such as obese patients), it may over-adapt to the characteristics of this group, thus reducing accuracy for general patients. Optimization strategies balance the learning weights for different patient types, maintaining good adaptability to all patient groups. Long-term tracking data shows that after adopting optimization strategies, the performance difference between different patient groups is controlled within 5%.

[0172] like Figure 5 As shown, the interactive feedback module 4 includes a confidence analysis unit 41, a prompt generation unit 42, a voice output unit 43, and a visual feedback unit 44.

[0173] The confidence analysis unit 41 is used to classify the lead position determination results and confidence scores. The confidence analysis unit 41 divides the confidence scores into three levels: high, medium, and low. Preferably, a confidence score higher than 0.85 is classified as high confidence, between 0.6 and 0.85 as medium confidence, and below 0.6 as low confidence. This classification process allows the system to provide feedback of varying intensities for judgments with different levels of certainty, avoiding over-intervention or insufficient feedback.

[0174] The grading formula for confidence analysis unit 41 is as follows:

[0175] ,

[0176] in: Confidence level; The original confidence score ranges from [value missing]. .

[0177] The choice of thresholds 0.85 and 0.6 is based on extensive clinical validation. Studies have shown that judgments with a confidence level above 0.85 have an accuracy rate exceeding 99% and can be directly accepted; judgments with a confidence level between 0.6 and 0.85 have an accuracy rate of approximately 85%–95%, requiring a warning but not mandatory adjustment; and judgments with a confidence level below 0.6 have an accuracy rate of only 60%–80%, requiring a clear warning and detailed guidance.

[0178] The prompt generation unit 42 is electrically connected to the confidence analysis unit 41 and is used to generate corresponding voice prompts based on different confidence levels. For high-confidence results, the prompt generation unit 42 generates simple confirmation information, such as "All leads are in the correct position"; for medium-confidence results, it generates prompt information, such as "Please check the position of the left-hand electrode"; for low-confidence results, it generates warning information and specific correction suggestions, such as "Warning: The left and right hand electrodes may be reversed; please swap the positions of the left and right hand electrodes."

[0179] The prompt generation unit 42 employs template-based language generation technology to automatically combine prompt content based on the judgment result and confidence level. Its core algorithm is as follows:

[0180] ,

[0181] ,

[0182] in: A collection of prompt templates; The generated prompt content; For lead position; Confidence level; Error type; A function for selecting an appropriate template based on location and confidence level; A function to generate detailed instructions based on the error type.

[0183] In routine examinations, the prompts are designed with both the accuracy of medical terminology and the clarity of operational instructions in mind. For example, if a low-confidence assessment indicates a possible error in V4 position, the system not only prompts the user to check the V4 lead location but also specifically instructs that V4 should be placed in the 5th intercostal space along the left midclavicular line, approximately at the midpoint between the axillary midline and the sternum, helping the operator to pinpoint the location accurately. Clinical feedback shows that detailed anatomical guidance increases the accuracy rate for novice operators from 65% to 90%.

[0184] The voice output unit 43 is electrically connected to the prompt generation unit 42 and is used to convert the voice prompt content into a voice signal and play it. The voice output unit 43 adopts high-quality text-to-speech technology, supports multiple languages ​​and voice selections, has adjustable speech rate, and its volume adapts to the ambient noise level. Preferably, the voice clarity meets the requirements of a medical environment and can be clearly distinguished even under 40dB background noise.

[0185] The visual feedback unit 44 is electrically connected to the confidence analysis unit 41 and is used to present visual indications of the lead status on the display screen. The visual feedback unit 44 uses an intuitive graphical interface to display a human silhouette and lead position markers; the correct position is displayed in green, a potentially incorrect position in yellow, and a confirmed error in red. In addition, the visual feedback unit 44 can also display real-time impedance values ​​and waveform quality indicators to help the operator assess signal quality.

[0186] The color coding rules for visual feedback are as follows:

[0187] ,

[0188] in: For position Confidence The displayed color.

[0189] In practical applications within electrocardiogram (ECG) examination rooms, the combination of visual and verbal feedback significantly improves operational efficiency. Visual feedback provides an intuitive overview of the status, while verbal feedback offers detailed operational guidance, making it particularly suitable for multitasking environments. User studies have shown that bimodal feedback reduces operation time by an average of 30%, while simultaneously increasing operator satisfaction and confidence.

[0190] The initialization module 5 is electrically connected to the impedance feature extraction module 1, the neural network recognition module 2, and the adaptive optimization module 3, respectively, and is used to perform system self-test and sensor calibration, load the basic model and personalized parameters, and initialize the neural network inference engine.

[0191] Upon system startup, initialization module 5 first performs a hardware self-test to ensure all modules are functioning correctly. Then, it calibrates the sensors, using a built-in standard impedance network to calibrate the impedance measurement circuit, ensuring measurement accuracy. Next, it loads the basic neural network model and parameter settings suitable for the current operating environment. If historical personalized parameters exist, initialization module 5 selectively loads these parameters to improve the system's initial adaptability. Finally, it initializes the neural network inference engine, allocates computing resources, and prepares for real-time decision-making tasks.

[0192] The ECG acquisition module 6 is electrically connected to the neural network recognition module 2. It is used to start ECG signal acquisition after confirming that all lead positions are correct, continuously monitor the lead status, prevent lead loss during acquisition, and send a learning update signal to the adaptive optimization module 3 after acquisition is completed.

[0193] Once the neural network recognition module 2 confirms that all lead positions are correct (confidence level higher than 0.85), the ECG acquisition module 6 automatically starts ECG signal acquisition. During acquisition, the ECG acquisition module 6 performs a rapid impedance check every 5 seconds to ensure that the leads have not fallen off or changed position. If an abnormality is detected, the system will immediately issue a warning. After ECG signal acquisition is complete, the ECG acquisition module 6 sends a learning update signal to the adaptive optimization module 3, triggering a session learning process that allows the system to learn and optimize from successful checks.

[0194] Preferably, the ECG acquisition module 6 also has a real-time signal quality assessment function, which can detect common problems such as baseline drift, electromyography interference, and power supply interference, and provide corresponding optimization suggestions to further improve the quality of ECG.

[0195] like Figure 6 As shown, the system workflow of the present invention includes the following steps:

[0196] System initialization: Performs hardware self-test, sensor calibration, model loading, and inference engine initialization;

[0197] Lead wire preparation: The operator pulls out the lead wire of appropriate length as needed, and the system automatically locks it to prevent rebound;

[0198] Contact selection: The operator selects the appropriate lead contact type (standard ball or adhesive flat contact) based on the patient's body shape characteristics;

[0199] Lead placement: The operator places the lead electrodes in the standard positions;

[0200] Impedance feature extraction: The system collects impedance data at multiple frequency points and constructs feature vectors;

[0201] Neural network identification: Performs hierarchical lead location determination, generates determination results and confidence levels;

[0202] Feedback and Adjustment: Output voice prompts based on the confidence level to guide the operator in making adjustments;

[0203] Position confirmation: Repeat steps 3-5 until all leads are in the correct position;

[0204] ECG signal acquisition: Start ECG signal acquisition and monitor lead status simultaneously;

[0205] Learning Update: After the data collection is completed, the system updates the model parameters based on the data from this inspection.

[0206] In practical applications, this system can adapt to various clinical scenarios, including routine examinations, emergency resuscitation, and ICU monitoring. Its adaptive learning capability enables it to continuously improve performance, adapt to more patient types and operating environments, truly achieving the goal of becoming smarter with use.

[0207] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent ECG lead system based on adaptive impedance matching and neural network recognition, characterized in that, include: Impedance feature extraction module, including: A multi-frequency excitation unit is used to generate excitation signals at multiple frequency points; An impedance acquisition unit, electrically connected to the multi-frequency excitation unit, is used to acquire impedance data of multiple leads at multiple frequency points using a four-electrode method, wherein excitation current is injected through two electrodes and voltage is measured through the other two electrodes to eliminate the influence of electrode-skin contact impedance. The feature construction unit, electrically connected to the impedance acquisition unit, is used to convert the impedance data into a feature vector containing static impedance value, dynamic rate of change, frequency response characteristics, and time-domain stability index; wherein, the feature vector is an 8-dimensional feature vector containing real impedance, imaginary impedance, phase angle, impedance time-varying rate, fluctuation standard deviation, frequency response characteristics, first derivative of frequency response, and time constant, serving as a parameterized impedance representation of the lead position. A quality assessment unit, electrically connected to the feature construction unit, is used to calculate the reliability index of the feature vector and filter unreliable data. The neural network recognition module, electrically connected to the impedance feature extraction module, includes: A front-end feature network unit is used to receive the parameterized impedance representation and extract high-level feature representations; A lead type classification unit, electrically connected to the front-end feature network unit, is used to classify leads into limb leads and chest leads; The precise location determination unit is electrically connected to the lead type classification unit and is used to perform precise location identification for each type of lead. An error type analysis unit, electrically connected to the precise location determination unit, is used to identify possible lead error types; The context verification unit is electrically connected to the precise location determination unit and the error type analysis unit, respectively, and is used to perform global consistency verification based on anatomical constraints. The neural network recognition module sequentially performs hierarchical lead position determination through the lead type classification unit, the precise position determination unit, the error type analysis unit, and the context verification unit, generating lead position determination results and confidence scores; An adaptive optimization module, electrically connected to both the impedance feature extraction module and the neural network recognition module, includes: Instant adaptation unit for rapid parameter adjustment in response to the current measurement; A conversational learning unit, electrically connected to the immediate adaptation unit, is used to accumulate knowledge during the examination of a single patient to form a temporary personalized model. A long-term optimization unit, electrically connected to the conversation learning unit, is used to slowly adjust model parameters across patients to improve general performance. The parameter storage unit is electrically connected to the immediate adaptation unit, the conversation learning unit, and the long-term optimization unit, respectively, and is used to store optimization parameters at each level. The adaptive optimization module receives the lead position judgment result and confidence score generated by the neural network recognition module, performs multi-level adaptive learning through the immediate adaptation unit, the conversation learning unit and the long-term optimization unit, and updates the system parameters based on operator feedback and judgment results to optimize model performance. An interactive feedback module, electrically connected to the neural network recognition module, includes: The confidence analysis unit is used to perform hierarchical processing on the lead position determination results and confidence scores; The prompt generation unit is electrically connected to the confidence analysis unit and is used to generate corresponding voice prompt content according to different confidence levels; A voice output unit, electrically connected to the prompt generation unit, is used to convert the voice prompt content into a voice signal and play it. A visual feedback unit, electrically connected to the confidence analysis unit, is used to present a visual indication of the lead status on the display screen.

2. The system according to claim 1, characterized in that, The feature construction unit is specifically used for: Calculate the impedance ratio between different lead positions and construct a relative characteristic index; Extract the frequency response curve characteristics of impedance data at different frequency points; Analyze the time-domain fluctuation characteristics of impedance data; The above features are combined to form a standardized feature vector.

3. The system according to claim 1, characterized in that, The context verification unit is specifically used for: Verify the spatial continuity of leads V1-V6 in the chest. Analyze the symmetry characteristics of the leads in the left and right limbs; Check the overall consistency of the multi-lead impedance characteristics; The judgment results are corrected based on the anatomical relationship of the lead positions.

4. The system according to claim 1, characterized in that, The adaptive optimization module also includes: The feedback analysis unit is used to capture the operator's response behavior to system prompts; The model evaluation unit, electrically connected to the feedback analysis unit, is used to evaluate the accuracy of the system based on operator feedback. An optimization strategy unit, electrically connected to the model evaluation unit, is used to dynamically adjust learning parameters based on the evaluation results.

5. The system according to claim 1, characterized in that, The confidence analysis unit is specifically used for: The confidence level is divided into three levels: high, medium, and low. For high-confidence results, generate confirmation messages; For results with medium confidence, generate a prompt message; For low-confidence results, generate warning messages and specific corrective suggestions.

6. The system according to claim 1, characterized in that, Also includes: The initialization module is electrically connected to the impedance feature extraction module, the neural network recognition module, and the adaptive optimization module, respectively, and is used for: Perform system self-test and sensor calibration; Load the base model and personalized parameters; Initialize the neural network inference engine; The ECG acquisition module, electrically connected to the neural network recognition module, is used for: After confirming that all leads are in the correct position, start ECG signal acquisition; Continuously monitor the lead status to prevent it from falling off during the data acquisition process; After the data collection is completed, a learning update signal is sent to the adaptive optimization module.

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