1424 results about "Intracardiac Electrogram" patented technology

An electrocardiogram device configured to transmit at least one signal responsive to a wearer's cardiac electrical activity can include a disposable portion and a reusable portion configured to mechanically and electrically mate with each other. The disposable portion can include a base having at least one mechanical connector portion, a plurality of cables and corresponding external ECG electrodes, and a first plurality of electrical connectors associated with the plurality of cables. The reusable portion can include a cover having at least one mechanical connector portion, a second plurality of electrical connectors configured to electrically connect with the first plurality of electrical connectors of the disposable portion, and an output connector port configured to transmit at least one signal responsive to one or more signals outputted by the external ECG electrodes of the disposable portion.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

The invention relates to the technical field of health monitoring, and discloses a patient health monitoring method and system based on dynamic electrocardiogram analysis, and the method comprises the steps: synchronously obtaining multi-lead dynamic electrocardiogram data continuously collected by a patient within a preset duration, and related physiological parameters of exercise intensity, respiratory rate and body position change; performing dynamic self-adaptive preprocessing on the dynamic electrocardiogram data based on the associated physiological parameters to obtain standardized electrocardiosignals; extracting a multi-dimensional characteristic parameter set from the standardized electrocardiosignal, and sampling according to a preset time window to generate a characteristic parameter sequence with a timestamp; and inputting the characteristic parameter sequence into a dynamic optimization analysis model capable of iteratively updating parameters through real-time physiological parameter feedback, and performing graded evaluation on the health state of the patient to generate an evaluation result. The system corresponds to the method. By adopting the method and the system, the accuracy of dynamic electrocardiogram monitoring and the evaluation refinement level meet the dynamic monitoring requirement of cardiovascular health.

The invention discloses an electrocardiogram classification system and method fusing multi-scale self-adaptive attention, and relates to the technical field of electrocardiogram classification.The electrocardiogram classification method comprises the steps that electrocardiogram data are collected through a data collection module, and a structured data set is output; the data preprocessing module is used for carrying out data preprocessing on the structured data set; the window data segmentation module obtains a standardized windowed electrocardiosignal; the feature extraction module takes the standardized windowed electrocardiosignals as input, performs three stages of multi-scale convolution feature extraction, liquid neural network dynamic modeling and self-adaptive attention mechanism enhancement, and outputs diagnosis results of nine types of heart diseases; and the training and optimizing module is used for optimizing the extraction and classification capability of the model on the electrocardiosignal features, so that the technical effects of full-process technical upgrading from data acquisition, preprocessing, feature extraction to model training optimization and high-precision electrocardiogram automatic classification are achieved.

The invention discloses an anesthesia depth monitoring system and method based on multiple physiological parameters, and the system comprises a data collection unit which is used for obtaining a perfusion index value, an electrocardiogram signal, a brain wave signal, a blood pressure signal of a patient and the individual feature information of the patient; the signal processing unit is electrically connected to the data acquisition unit and used for processing the signals acquired by the data acquisition unit and calculating the perfusion index change rate, the heart rate variability, the anesthesia consciousness index and the blood pressure fluctuation rate; the parameter fusion unit is electrically connected to the signal processing unit and used for combining the parameters calculated by the signal processing unit with the individual feature information of the patient; the individualized correction unit is electrically connected to the parameter fusion unit and is used for adjusting the comprehensive anesthesia depth score generated by the parameter fusion unit; the method has the advantages that multi-dimensional physiological data and individual characteristics can be integrated, precise evaluation and individualized regulation and control of the anesthesia depth are realized, and the anesthesia safety is improved.

A blood pump system includes a catheter, a pump housing disposed distal of a distal end of the catheter, a rotor positioned at least partially in the pump housing, a controller, and an electrode coupled a distal region of the blood pump. The electrode can be used to sense electrocardiogram (EKG) signals and transmit the signals to a controller of the blood pump. The operation of the blood pump can be adjusted based on the EKG signal and on cardiac parameters derived from the EKG signal. Further, the controller can determine a need for defibrillation or pacing of the patient's heart based on the signal and can administer treatment with electrical shocks to the heart via the electrode coupled to the blood pump. The use of an electrode with a blood pump already in place in the heart allows for more efficient and safer treatment of serious cardiac conditions.

Artificial intelligence enabled disease profiling is described. An electrocardiogram analysis module is configured to derive disease vectors for a plurality of diseases using electrocardiogram training data from both disease-negative and disease-positive individuals. A standardized input is generated, via a data preprocessor of the electrocardiogram analysis module, from an electrocardiogram recorded from an individual. The standardized input is encoded, by a deep learning autoencoder of the electrocardiogram analysis module, into an embedding, the embedding being a lower-dimensional latent space representation of features extracted from the standardized input. At least one disease risk score for the individual is generated, by a statistical modeling algorithm of the electrocardiogram analysis module, for the plurality of diseases based on the embedding and the disease vectors.

The invention relates to the technical field of physiological signal prediction, in particular to a preoperative risk assessment method for the department of cardiology, which comprises the following steps: sliding window segmentation time sequence data to calculate a baseline offset, dynamic time warping alignment parameter fluctuation rate to generate an abnormal mark, and standard deviation comparison amplitude threshold triggering risk signals. K-means clustering multi-dimensional data mapping risk levels, isolated forest detection abnormal fluctuation and electrocardiogram and myocardial zymogram cross validation are combined, and a preoperative risk assessment conclusion is output. According to the method, individual differences and interferences are eliminated through a sliding window algorithm, a time difference problem is solved by aligning a multi-parameter fluctuation rate through dynamic time warping, a quantitative evaluation standard is established by comparing a standard deviation with an amplitude threshold value to avoid limitation of a single threshold value, and objective grading is realized by combining K-means clustering with an Euclidean distance. The isolated forest algorithm, the electrocardiogram ST segment and myocardial zymogram cross validation form a multi-modal evaluation system, and the risk evaluation sensitivity and specificity are improved in a complete closed-loop mode.

The invention relates to an intelligent heart electrophysiological signal analysis and Jiangshi connecting line optimization system and method, and belongs to the technical field of electrocardio systems, the system comprises a plurality of modules for cooperative work, a data acquisition module is used for acquiring human body 12-lead electrocardiogram and intracardiac electrogram signals and executing a pace-making stimulation function; the signal preprocessing module filters received signals, the feature extraction module extracts time domain, frequency domain and nonlinear features, the deep learning module identifies abnormal heart rhythms by using a pre-training model, the parameter tuning module dynamically adjusts model parameters and optimizes and analyzes the parameters, and the Jiangshi connecting line optimization module improves signal quality. The intelligent monitoring module automatically monitors multiple indexes, the display interface module visually displays an electrocardiogram, the system compatible module is connected with multiple devices, and the control module coordinates all the modules to ensure efficient operation of the system.

The invention discloses a non-contact electrocardio detection method which comprises the following steps: transmitting a frequency modulation continuous wave signal to the heart part of a human body by using an MIMO millimeter wave radar so as to obtain an echo signal reflected by the human body and pre-process the echo signal to obtain a discrete three-dimensional radar signal; enhancing the discrete three-dimensional radar signal by using a 2D beam forming technology to obtain a 2D signal; extracting a target area signal from the 2D signal, and performing phase extraction on the extracted target area signal to obtain phase data; the method comprises the following steps: constructing a WaveGRU-Net network comprising an MODWT module, a CNN module and a Bi-GRU module; and training the WaveGRU-Net network by using the phase data so as to output a reconstructed electrocardiogram by using the trained WaveGRU-Net network, thereby realizing non-contact electrocardiogram detection. According to the method, accurate extraction of the electrocardiosignal is realized, and meanwhile, the signal reconstruction precision is improved.

The invention discloses an electrocardiogram arrhythmia classification method and system based on a residual shrinkage network, and relates to the technical field of arrhythmia classification. According to the method, efficient electrocardiogram arrhythmia classification is achieved through multi-link cooperation, and a fine preprocessing, data balance strategy and multi-attention mechanism fusion model is designed; preprocessing provides high-quality input through wavelet denoising, precise R-wave detection and the like; the under-sampling-over-sampling mixed strategy is used for solving class imbalance and improving minority class recognition; the ResTCL-Net is fused with CNN, GRU, RCA, TSA and CLA modules, and signal features are mined in multiple dimensions; the optimization training strategy gives consideration to efficiency and stability, and is matched with comprehensive evaluation to guarantee performance. According to the scheme, the classification accuracy and generalization ability are remarkably improved, and abnormal heart beat recognition is enhanced.

A method for determining physiological characteristics is provided. The method involves causing light to be emitted by one or more light emitters toward a tissue of a user. The method involves obtaining samples of transmitted and / or reflected light. The method involves determining photoplethysmography (PPG) data based on the obtained samples. The method involves providing the PPG data as an input to a trained machine learning model, wherein the trained machine learning model has been trained, using a training set that comprises matched samples of electrocardiogram (ECG) data and PPG data. The method involves providing a blood pressure of the user based on an output of the trained machine learning model.

The invention discloses a mobile rescue intelligent management method based on RFID automatic identification and AI artificial intelligence interaction, and belongs to the field of mobile rescue intelligent management. According to the invention, the problem of low efficiency of medicine and material inventory management of the rescue carriage in an inpatient area due to an existing manual inventory mode is solved, batch accurate identification can be carried out by adopting an RFID remote identification technology, batch inventory is realized to replace one-by-one checking, the inventory efficiency is effectively improved, and the inventory management cost is reduced. Manual electrocardiogram data checking is replaced by image visual recognition of electrocardiogram data, secondary recording is not needed, rescue data can be automatically recorded, rescue efficiency is improved, medical advice recognition is assisted by AI voice instead of manual recording of medical advice, the workload of nurses is reduced, the workload of secondary recording is reduced, and rescue efficiency is improved. And through a rescue scene full-process digital AI closed loop, digital tracing can be completed in the rescue process, and the problems that traditional handwritten records and data are not real-time and inaccurate are solved.

The invention discloses a 12-lead electrocardiosignal generation method based on a medical text and related equipment. The method comprises the steps that text information is acquired; inputting the text information into a pre-trained TTE model to generate a 12-lead electrocardiogram signal; wherein the TTE model comprises an encoder, a noise predictor and a decoder; in an electrocardiogram generation stage, a noise predictor takes a text semantic condition as input, starts from initial random Gaussian noise zT, gradually recovers a potential feature vector # imgabs0 # pretrained decoder meeting conditional constraints through iterative denoising, reflects a potential feature vector # imgabs1 # to a high-dimensional original signal space, and outputs the potential feature vector # imgabs1 # pretrained decoder to a high-dimensional original signal space. According to the method, medical text description is used as condition input, text semantic constraints are embedded in a potential diffusion model framework, 12-lead simulated electrocardiosignals conforming to specific pathological features are generated, and a feasible alternative scheme is provided for shortage of current labeled ECG data sets.

The invention discloses a multi-view dynamic fusion electrocardiogram diagnosis method and system based on graph embedding, and belongs to the technical field of medical signal processing. The method comprises the following steps: S1, acquiring standard 12-lead electrocardiogram data acquired under different instruments and equipment, and preprocessing the acquired data; s2, constructing a multi-view feature coding and graph structure modeling module; s3, constructing a multi-view fusion module for graph embedding; and S4, constructing a classification prediction output module, and establishing an optimization mechanism. According to the method, the image structure and the multi-view features are fused, the problems of lead modeling stiffness, fusion strategy static state and structure optimization decoupling in an existing method are solved, the disease recognition capability is remarkably improved, the multi-source heterogeneous data verification performance is excellent, and good clinical and engineering adaptability is achieved.

The invention discloses an electrocardiogram analysis method and device based on a deep learning model and a medium, and relates to the field of deep learning, and the method comprises the steps: carrying out the preprocessing of an electrocardiogram, and carrying out the noise suppression; inputting the electrocardiosignals into a pre-trained deep learning model, extracting time features and spatial features, and performing cross-modal feature fusion; synchronously executing a plurality of anomaly detection tasks, and synchronously executing a time sequence prediction task; aiming at the abnormal detection result, correcting the abnormal detection result according to the clinical information and the time sequence prediction result; and outputting an analysis result corresponding to the electrocardiogram. A complete closed loop is formed from signal processing to feature extraction, multi-task analysis and result correction, manual intervention links are reduced, electrocardiogram analysis time is remarkably shortened, result consistency is guaranteed, end-to-end automation is achieved, and efficiency is improved.

The invention discloses a vascular access lesion recognition method and system based on target detection, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the cardiac cycle phase division according to an electrocardiogram signal, calculating a displacement vector field between adjacent frames of a blood vessel DSA image sequence, constructing a motion compensation model, and generating a stable DSA sequence; segmenting the stable DSA sequence into binary vascular mask images through a U-Net segmentation model, tracking motion displacement of contrast agent particles in continuous frames by using a particle image velocimetry algorithm, generating a blood flow velocity vector field, and calculating an eddy current intensity feature map; and carrying out channel cascading on the binarized blood vessel mask image and the vortex intensity feature map to generate a blood vessel feature tensor, and generating a lesion detection result by improving a YOLOv7 model. According to the method, the accuracy, robustness and clinical applicability of vascular disease recognition are improved, and a technical closed loop from multi-modal data processing to automatic diagnosis report generation is realized.

The invention relates to the technical field of electrocardiogram detection, in particular to an electrocardiogram atrial fibrillation prediction method based on multi-modal deep learning, which comprises the following steps: S1, collecting electrocardiogram signal data, S2, performing signal preprocessing and feature fusion, and S3, performing model training and atrial fibrillation prediction. According to the method, multi-modal features are extracted through a complex multi-scale attention mechanism and a feature self-adaptive attention mechanism, a deep learning model based on the combination of a convolutional neural network and a long-short term memory network is combined, multi-dimensional features and time sequence information of electrocardiosignals are fully utilized, and the multi-modal features of the electrocardiosignals are extracted. Therefore, the efficiency, the accuracy and the reliability of the atrial fibrillation prediction method are greatly improved, and the problem that the sensitivity and the specificity cannot meet the requirements when the prior art faces complex and diversified electrocardiosignals is solved.

A catheter includes: (a) a shaft for insertion into a heart of a patient, (b) an expandable distal-end assembly, which is coupled to the shaft and is configured to make contact with tissue of the heart, (c) at least first and second electrocardiogram (ECG) electrodes, which are coupled to an outer surface of the expandable distal-end assembly, and when placed in contact with the tissue, are configured to sense ECG signals in the tissue, and (d) a reference electrode, which is positioned within an inner volume of the distal-end assembly, and in an expanded position of the distal-end assembly, the reference electrode: (i) has no physical contact with the tissue, and (ii) is positioned at a first distance from the first ECG electrode and at a second distance from the second ECG electrode, and the difference between the first and second distances is smaller than a predefined threshold.

The invention discloses an adjustable blood transfusion reaction monitoring system. The system comprises a multi-mode physiological signal acquisition module, a data processing and analysis module, a user interaction and parameter configuration interface, a wireless communication module, an alarm and feedback module and a power management module. By integrating an electrocardiogram (ECG) sensor, an oxyhemoglobin saturation (SpO2) detection probe, a non-invasive blood pressure measuring device, a body temperature sensor, a respiratory rate monitor and a skin impedance change sensor, real-time acquisition of various physical parameters is realized; a rule-based threshold judgment model and a time sequence prediction model are built in the data processing and analysis module, and the data processing and analysis module is used for identifying potential blood transfusion reaction risks; and the user interaction and parameter configuration interface provides a touch display screen and remote terminal equipment to realize information input, data display and operation recording. Continuous dynamic monitoring, intelligent early warning and graded response in the blood transfusion process are achieved, blood transfusion safety and nursing efficiency are improved, and the system has wide clinical application prospects.

Disclosed are a single-arm electrocardiogram (ECG) signal measurement device and a single-arm ECG signal measurement method. The single-arm ECG signal measurement device includes a first sensing electrode, a second sensing electrode, a third sensing electrode, a fourth sensing electrode, a fifth sensing electrode, a sixth sensing electrode, and a computing circuit. The first sensing electrode, the second sensing electrode, the third sensing electrode, the fourth sensing electrode, the fifth sensing electrode, and the sixth sensing electrode receive physiological signals from six different positioning points located on an upper arm of a user. The computing circuit generates ECG signals according to the physiological signals, and generates a 12-lead ECG-like signal according to the ECG signals.

The invention discloses a PICC (Peripherally Inserted Central Catheter) tip precise navigation fixing method and system based on electrocardiogram real-time positioning. High-precision positioning and safe fixing of a catheter tip are realized through a multi-modal data fusion and deep learning technology. The method comprises the following steps: collecting intracavity electrocardiosignals (ECG) in real time through a catheter built-in electrode, and obtaining blood vessel wall contact pressure and temperature data in combination with an optical fiber sensor; an improved wavelet threshold algorithm is adopted to dynamically suppress motion artifacts, and P-wave features are enhanced; the preprocessed ECG signals are input into a spatial-temporal feature fused Transformer model, a time attention layer is used for analyzing P-wave time sequence changes, a space attention layer integrates multi-lead space distribution features, and the positioning precision is optimized in combination with preoperative blood vessel anatomical data; the ECG positioning result and the optical fiber sensing data are fused through Kalman filtering, three-dimensional position information of the tip end of the catheter is generated, and real-time navigation is conducted on an augmented reality interface; and when the P wave amplitude reaches a CAJ threshold value, the contact pressure is safe and no abnormal temperature exists, a shape memory alloy (SMA) fixing ring is triggered to contract, and accurate fixing is achieved. The method solves the problems that a traditional method depends on X rays and is poor in anti-interference performance, has the advantages of being free of radiation, high in real-time performance and adaptive to blood vessel deformation, and is suitable for clinical P catheter implantation.

The invention discloses a biomedical multi-mode signal anomaly detection and prediction method and system, and the method comprises the steps: modeling a biomedical multi-mode signal into a time-varying non-local dynamic system, capturing the long-time-history dependence characteristic of the signal through a memory mechanism of a Caputo fractional derivative, and introducing a time-varying input item to process interference; the method comprises the following steps of: carrying out high-precision numerical integration by adopting an Adams-Bashform-Module solver; optimizing model parameters in combination with a local domain normalization pre-training strategy and a fusion loss function; abnormal detection is realized by calculating comparison between signal reconstruction deviation and a self-adaptive threshold value; a future anomaly probability is generated based on the trajectory prediction. According to the method, the problems of insufficient non-local dependence capture, poor time-varying interference robustness, high false positive rate and the like in the prior art are effectively solved, the accuracy and real-time performance of abnormal detection and prediction of biomedical signals such as electroencephalogram and electrocardiogram are remarkably improved, and the method is suitable for wearable medical equipment and clinical monitoring systems.

An apparatus for generating diagnostic hypotheses based on electrocardiogram (ECG) data, comprising a processor and a memory containing instructions configuring the processor to generate, using a generative model trained on a corpus, a set of diagnostic hypotheses, wherein generating the set of diagnostic hypotheses includes creating labels, each represents a diagnostic feature associated with diagnostic hypotheses, receive a biomedical signal, identify a biomedical feature as a function of the biomedical signal, select a diagnostic hypothesis from the set of diagnostic hypotheses by matching the biomedical feature against the diagnostic feature, query, as a function of at least a matched label, a medical repository to validate the diagnostic hypothesis, wherein the medical repository includes patients' electronic health records (EHRs), and output the diagnostic hypothesis upon a positive validation of the diagnostic hypothesis.

The invention discloses an electrocardiogram classification method based on a QRS wave adjustment algorithm and a self-adaptive threshold value. The method comprises the following steps: S1, acquiring an electrocardiogram original signal; s2, preprocessing the electrocardiogram original signal through a QRS wave adjustment algorithm to obtain an electrocardiogram processing signal which comprises QRS wave crest characteristics; s3, intercepting a QRS wave by adopting an adaptive threshold algorithm according to the characteristics of the QRS wave crest; s4, constructing an electrocardiogram classification model, and training the electrocardiogram classification model; and S5, carrying out electrocardiogram classification operation based on the trained electrocardiogram classification model. According to the method, the electrocardiogram original signal is preprocessed through the QRS wave adjustment algorithm to obtain the electrocardiogram processing signal, the electrocardiogram processing signal comprises the QRS wave crest characteristics, the QRS wave is intercepted from the electrocardiogram processing signal according to the QRS wave crest characteristics and by adopting the self-adaptive threshold algorithm, interference of noise and other waves on the electrocardiogram signal is reduced through preprocessing, and the electrocardiogram signal quality is improved. The QRS waves are recognized through the electrocardiogram classification model constructed based on the BiLSTM network model architecture, the electrocardiogram classification model pays more attention to the time sequence relation between the signals, and therefore the electrocardiogram classification accuracy is improved.

The invention relates to the technical field of medical equipment, in particular to a cardiopulmonary sound and electrocardiogram rapid screening system suitable for emergency treatment scenes. The system comprises a heart and lung sound acquisition unit, an electrocardiogram acquisition unit and an intelligent analysis unit. And the intelligent analysis unit realizes signal synchronization by establishing a cross-modal time sequence alignment model, adaptively matches the physiological state by adopting a dynamic time window adjustment mechanism, and introduces multiple rounds of verification processes to ensure the result reliability. The system can automatically complete synchronous collection, feature extraction and fusion analysis of the heart and lung sound and the electrocardiosignals, the diagnosis accuracy is remarkably improved while the screening speed is guaranteed, and reliable technical support is provided for rapid screening of heart and lung diseases in the emergency department.

The invention discloses an obstructive sleep apnea detection method and system based on a multi-scale convolutional neural network, a storage medium and electronic equipment. The obstructive sleep apnea detection method specifically comprises the following steps: loading single-lead electrocardiogram (ECG) data, and preprocessing the data; a P-R interval is determined; calculating by using Euclidean distance to obtain a two-dimensional distance array of the five-minute segment; further intercepting the 5-minute fragment into a 3-minute fragment and a 1-minute fragment; constructing three different CNN models, and respectively inputting the maximum distance and the minimum distance in the three scale fragments into the three CNN models; the convolution feature map of each model is optimized, and important information in the features is enhanced through a residual attention module; applying a channel attention module to optimize the attention capability of the model to the key features; and integrating the feature maps subjected to residual error and channel attention optimization, performing final classification output through a full connection layer, and predicting whether OSA (Obstructive Sleep Apnea) exists or not.

The invention discloses a millimeter wave electrocardiogram reconstruction system and method based on adaptive MODWT and CNN-BiLSTM-CA, and the method comprises the steps: collecting a tiny phase displacement signal of a target thoracic cavity region, and carrying out the multi-band decomposition and key component adaptive screening of an original signal through combining with a multi-scale stationary wavelet decomposition algorithm; and introducing a channel attention mechanism to enhance key information representation, inputting a reconstruction signal into a deep learning model combining a convolutional neural network and a bidirectional long-short term memory network, completing time sequence modeling and nonlinear mapping, and outputting a reconstruction waveform highly consistent with a standard electrocardiogram. According to the method, the signal reduction capacity under the complex interference condition is remarkably improved, high-precision and privacy-friendly remote physiological signal monitoring can be achieved under the condition of not depending on a lead electrode, and the method is superior to a traditional baseline model in the aspects of waveform reduction precision, signal time sequence consistency, model generalization capacity and the like; the method is suitable for various application scenes such as intelligent medical treatment.

The invention discloses a paper electrocardiogram voltage value reconstruction method and system based on a dynamic diffusion threshold value, and belongs to the technical field of electrocardiogram analysis. Comprising the following steps: acquiring a standard paper electrocardiogram and a to-be-processed paper electrocardiogram, and extracting paper electrocardiogram parameters; based on a preset threshold value, carrying out iterative optimization to obtain an optimal threshold value so as to carry out binarization processing on the to-be-processed paper electrocardiogram to obtain a binarized paper electrocardiogram; the positioning of the electrocardiogram waveform lead position is realized; the electrocardio waveform information of the lead image area is converted into a digital signal; converting the obtained digital signal into a voltage signal; and resampling the voltage signal to obtain a digitalized reconstructed electrocardiosignal. Compared with the prior art, the method has the advantages that the image processing threshold value can be dynamically adjusted according to the local features of the paper electrocardiogram, so that the edge of the electrocardiogram waveform in the digitization process is clearer, and it is ensured that the digitization data can accurately reflect the original information of the paper electrocardiogram.

Techniques are disclosed for segmenting electrocardiogram (ECG) signals. In one example, a method to segment an electrocardiogram (ECG) signal may include detecting consecutive heartbeats in an ECG signal. The method also includes segmenting the ECG signal into multiple ECG segments surrounding the detected consecutive heartbeats and generating an ECG data set by joining consecutive ECG segments. The generated the ECG data set represents the detected heartbeats. In some such examples, each ECG segment is of a duration to include a QRS complex, a P wave, and a T wave.