33 results about "Electrodiagnosis" patented technology

The invention provides an ECG (electrocardiogram) diagnosis system which comprises an ECG acquisition system and an ECG analysis and diagnosis system that are connected with each other, wherein the ECG analysis and diagnosis system comprises a DLL (dynamic link library) system, an ECG feature extraction module, and an ECG feature statistical analysis module; and the ECG feature statistical analysis module comprises a plurality of analysis units corresponding to different ECG features; doctors choose the analysis units according to conditions of patients; the ECG feature extraction module takes the corresponding ECG features as ECG feature extraction objects, calls a corresponding dynamic database of the DLL system, to extract the ECG features from ECG data and transmits the ECG features to the ECG feature statistical analysis module; and the ECG feature statistical analysis module conducts statistical analysis on the ECG features for the doctors for diagnosis. The ECG diagnosis system facilitates the quick diagnosis of the doctors, the working efficiency of the doctors is improved, the interaction among the different ECG features is avoided, and the accuracy rate of the analysis is increased. The invention further provides an operating method of the ECG diagnosis system.

An electro diagnostic functional assessment unit (EFA-2) that diagnoses age of and treats soft tissue injuries. The EFA-2 utilizes sensor(s) including EMG, Range of Motion, FCE, pinch and grip, and allows for monitoring of muscles and muscle groups to ascertain compliance, pain and function. The EFA-2 also monitors disc pathology and determines whether a person requires surgery or conservative care and, the age of the disc. Additionally, the EFA-2 monitors EEK activity and NCV, as well as invasive EMG that monitors nerve damage. Previously, with NCV the temperature and the position of electrodes would effect readings and produce false positive or false negative readings. The EFA-2 eliminates these problems by incorporating temperature sensor(s) and electrode placement sensor(s). The EFA-2 also provides direct treatment by means of ultrasound and electrical stimulation. Thus, the EFA-2 allows diagnosis any treatment by means of a single unit.

The invention discloses a method for processing a sensing fault of an intake temperature of an engine, comprising the following steps that: S1. synchronous delay of electrodiagnosis of the intake temperature and a temperature of a cooling liquid are obtained, and time of the engine from a startup state to an idle state or a partial loading state is obtained; S2. when the synchronous delay of the electrodiagnosis is more than the time, the intake temperature is obtained by a sensor of the intake temperature, and the intake temperature is transmitted to an electrical control unit ECU; S3. The ECU judges whether the sensor of the intake temperature has the fault according to the intake temperature; and S4. when the sensor of the intake temperature has the fault, the ECU carries out an emergency treatment to the intake temperature so as to allow the engine to operate normally. The ECU diagnoses the sensor of the intake temperature by collecting a signal of the sensor of the intake temperature to determine whether the signal has the fault, and carries out the emergency treatment to the engine which has the fault to the sensor of the intake temperature. The diagnosis technique is executed when the engine is operated, and the intake temperature is monitored at moment so as to guarantee safe driving of vehicles.

A catheter system and method for selectively cooling or freezing target neuronal tissue to induce lesions along the neuroaxis and produce cryoanalgesia by impairing nerve conduction of the targeted neuronal tissue. The system includes a catheter that has cryogenic capability for variable cooling or freezing of neuronal tissue. The catheter also includes temperature sensing and electrodiagnostic capabilities. A pressurized fluid source is included for inflating a portion of the catheter body. The system includes electrodiagnostic equipment for stimulating and monitoring sensory evoked potentials in the patient. The method involves placement of the catheter tip in the subarachnoid space of the spinal canal and location of the tip on the neuronal target using imaging and electrodiagnostic techniques.

The invention relates to the technical field of electrocardiosignal processing, in particular to an electrocardiosignal classification method based on a convolutional neural network. The method comprises the steps: S1, collecting electrocardiosignal training data, and attaching labels to the electrocardiosignal training data for data preprocessing; s2, performing data enhancement on the preprocessed training data; s3, constructing a convolutional neural network model, and training the convolutional neural network model by using the enhanced training data to obtain a training model; s4, acquiring a target electrocardiosignal, inputting the target electrocardiosignal into the training model for calculation, and outputting a probability value; and S5, performing positive and negative examplejudgment according to the output probability value to obtain a classification judgment result. According to the method, the electrocardiogram diagnosis efficiency and accuracy can be effectively improved, the provided training model can cover various complex electrocardiogram characteristics, and transfer learning of data is facilitated.

Provided are a data obtaining section (21) that obtains a time-series data fluctuating in accordance with the plasma conditions, a translation error calculation section (24) that calculates a determinism providing an indicator of whether the time-series data in the plasma are deterministic or stochastic, from the time-series data that have been obtained in the data obtaining unit (21), and an abnormal discharge determination section (26) that determines that the plasma is under the abnormal discharge conditions, in the case that the value representing the determinism calculated in the determinism derivation unit is less than or equal to a given threshold value, during the plasma generation. Examples of the value representing the determinism include translation error or permutation entropy. In the case the permutation entropy is used as a value representing the determinism, a permutation entropy calculation section is provided.

The invention relates to the technical field of electrocardio signal processing, in particular to an electrocardio diagnosis method based on the combination of a convolutional neural network and a recurrent neural network. The method comprises the steps: S1, collecting electrocardiosignal training data, and attaching labels to the electrocardiosignal training data for data preprocessing; S2, performing data enhancement on the preprocessed training data; S3, constructing a joint neural network model, and training the joint neural network model by using the enhanced training data to obtain a training model; S4, acquiring a target electrocardiosignal, inputting the target electrocardiosignal into the training model for calculation, and outputting a probability value; S5, performing positive and negative example judgment according to the output probability value to obtain a classification judgment result. According to the method, the electrocardiogram diagnosis efficiency and accuracy canbe effectively improved, the provided training model can cover various complex electrocardiogram characteristics, and transfer learning of data is facilitated.

The invention relates to a remote electrocardio diagnosis quality control method and device and a management system. The method comprises the steps of conducting remote consultation on electrocardiogram data information uploaded by a primary medical institution, and marking critical value information; and after consultation, preferentially transmitting an electrocardiogram diagnosis report of theelectrocardiogram marked with the critical value information. Patients with electrocardiogram critical values can be treated in time; the aims of guaranteeing the life health of a patient and saving the medical cost are achieved, a primary medical institution synchronizes the received electrocardiogram diagnosis report to a shared cloud server in real time, a superior hospital obtains the distributed electrocardiogram diagnosis report through the cloud server and carries out remote quality control, and the quality control result is more rigorous.

An electrode device for cardiological or neurological electrodiagnosis and / or electrotherapy comprises an elongated electrode body (12), at least one electrode (14, 16) in the vicinity of the distal end (24) of the electrode body (12), and an electrode conductor (44) for the electrical connection of the electrode (14, 16). The electrode conductor (44) has a fibrous structure (52) with anisotropic conductivity so that the specific conductivity of the electrode conductor (44) is significantly higher in its longitudinal direction than in its transverse direction.

The invention provides a drug capsule for treating gallbladder disease and a preparation method thereof. The drug capsule is a compound preparation combining traditional Chinese medicine with Western medicine and having good treatment effect. The drug capsule is prepared by processing oriental wormwood, longhairy antenoron herb, Chinese violet, curcuma root, loofah sponge and magnesium sulfate serving as raw materials. The drug capsule contains metabolic substances capable of dredging meridians and collaterals of liver and gall, inducing choleresis and discharging intraheptic stasis. 'Lu su Asking, Six Viscera State Theory' says that all eleven internal organs depend on gall. 64.7% of out-patients in twenty years have liver and gall disease through ear-point electrodiagnosis, and gall disease accounts for 97.3% while liver disease accounts for 2.7%. 53 chronic diseases are caused by functional disorder of gall and liver. The pathological mechanism is disorder in qi-blood circulation. 'Coffin, Meridian Ten' says that meridians can decide life and death, cure all diseases and regulate deficiency and excess, and have to be dredged. Practice of 20 years verifies that scientificity and practicability of the Meridian-Collateral Theory of traditional Chinese medicine are further verified, and disease entities and disease cases are attached.

The invention provides an intelligent analysis method for an electrocardiogram containing a noise label and an electrocardiograph, and the method comprises the steps: firstly building a lightweight convolutional neural network as the basis of a classification task, inputting an electrocardiogram data set containing the noise label, carrying out the basic data learning training, and storing a model after the training; due to network characteristics, the convolutional neural network is very easy to over-fit with training data marked wrongly, so that the performance is obviously reduced. The invention provides a classification algorithm constructed based on data cleaning and an anti-noise label loss function, the problem that the accuracy rate of electrocardiogram diagnosis is reduced due to noise labels can be effectively relieved, and an obvious effect can be achieved at the noise degree of 10%-50%. In addition, the method is small in calculation amount and can be suitable for various electrocardiographs.

The invention belongs to the technical field of electrocardiogram detection, and particularly relates to a wearable patient individual electrocardiogram detection method. According to the method, based on a multi-stream multi-scale convolutional neural networks (MM-CNN) model and a multi-lead comprehensive judgment algorithm, detection classifying is performed on an individual electrocardiogram (ECG). The invention proposes an MM-CNN-based single-lead classified detection algorithm to promote the clinical practice of ECG diagnosis; an algorithm that ECG signals of a few minutes are obtained from a specific patient to train a general model into a patient-specific model is proposed; the general model and the patient-specific model have a same network system, so that the efficiency of ECG detection is increased; the invention proposes the multi-lead comprehensive judgment algorithm to permanently classify the ECG signals from the specific patient, thus improving the robustness; and the method has high accuracy and high real-time performance, and is suitable for real-time detection of wearable equipment.

The invention discloses a modularized device for removing NOx by synergy of large-area creeping DBD (dielectric barrier discharge) and a catalyst and belongs to the field of plasma technology application. The modularized device is characterized in that medium tubes sleeve linear ground electrodes which are grounded firmly, and array-type linear high-voltage electrodes are embedded into the linear ground electrodes to form a creeping discharge area; array-type wire-wire electrodes (1) are disposed in a discharge reaction chamber (2) to form a modularized discharge-removal device, and the modularized discharge-removal device and the catalyst are used for removing NOx synergistically; discharge plasmas are generated by excitation of a power system (4), and a matching system (3) is used for guaranteeing power output of a power source; an electrodiagnosis system (7) and an optical diagnosis system (8) are used for online diagnosis of large-area creeping DBD, a gas detection system (6) is used for diagnosis of treatment effects of NOx supplied by a gas supply system (5), and a catalyst characterization system (9) is used for catalyst characterization and studying plasma and catalyst synergistic mechanisms.

The invention discloses an electrocardiosignal classification model combining a deep learning model and medical field knowledge, achieving an effect of making compliant and reasonable medical interpretation for classification results while realizing accurate classification of arrhythmia. The method comprises data preprocessing, a deep neural network classification model, a domain knowledge model, a joint training model and an interpretable report model. According to the method, clinical diagnosis rules corresponding to medical pathological features are established, and a deep neural network is combined for joint training. The method has the beneficial effects that 1) for current electrocardiogram diagnosis, only a data driving technology is utilized, medical field knowledge is integrated, and parameters of the neural network are finely adjusted under the guidance of the field knowledge, so that the optimization direction of the deep neural network is more stably related to the field; and 2) an interpretable technology is adopted, visual anomaly positioning is formed through a CAM technology, and in combination with field knowledge, the pathological basis of each diagnosis result is interpreted on the semantic level, so that the medical diagnosis result is more credible.

The invention provides a method, a system and a device and a medium for detecting starting point and ending point of electrocardiogram feature, and the method comprises the steps: obtaining electrocardiogram data, and carrying out the denoising processing of the electrocardiogram data, and obtaining a first filtering signal; decomposing the first filtering signal, and enriching the decomposed signal to obtain a plurality of wavelets; determining positions and peak values of the wavelets through a plurality of limited region windows according to QRS complex wave features and electrocardiogram time domain features; according to the peak value, the slope of a point in the limited area window and the absolute maximum and minimum value of the slope, obtaining a starting point and an ending point of the wavelet through positioning. According to the method, the starting point and the ending point of the electrocardiogram feature wave can be accurately detected, time and amplitude features required by electrocardiogram diagnosis can be conveniently and accurately extracted, a basis is provided for electrocardiogram diagnosis, research and development of an automatic electrocardiogram diagnosis system and recognition and diagnosis of related diseases, and therefore the method can be widely applied to the technical field of electrocardiogram data processing.

The invention discloses a long-distance monitoring and diagnosis system and a processing method for single-guide heart paste data. The system includes a collection terminal, a client and a server; The terminal is used to preprocess the obtained long-term dynamic ECG data, and send the preprocessed ECG signal data to the server, and display the ECG analysis report received from the server to the user; the server: for Draw the ECG scatter diagram according to the obtained ECG signal data, input the ECG scatter diagram to the convolutional neural network model, calculate the classification results of the ECG scatter diagram, form an ECG analysis report and send it to the client. The present invention uses the convolutional neural network model to analyze the collected user's electrocardiogram data on the scatter diagram drawn by the user's electrocardiogram data, which effectively improves the analysis speed of dynamic electrocardiogram and improves the accuracy of diagnosis, and saves the time and cost of electrocardiographic diagnosis physicians. time and reduce the misdiagnosis rate.

The invention discloses an electrocardiogram diagnosis method based on an artificial rule enhanced neural network. The electrocardiogram diagnosis method comprises the steps that an ECG signal training sample is obtained; constructing a training model comprising a deep learning module and a rule reasoning module, inputting the ECG signal training sample into the deep learning module to obtain a first anomaly prediction probability vector, and inputting the ECG signal training sample into the rule reasoning module to obtain a second anomaly prediction probability vector for performing probability prediction on the electrocardiogram diagnosis tag, fusing to obtain a final abnormal prediction probability vector for carrying out probability prediction on the electrocardio diagnosis tag; an ECG signal training sample is input into the training model, and parameters of the training model are optimized through a total loss function to obtain a multi-lead electrocardiogram signal diagnosis model; during application, the ECG signal is input into the multi-lead electrocardiogram signal diagnosis model to obtain the prediction probability of the electrocardiogram diagnosis tag of the ECG signal. The method can be used for accurately and quickly diagnosing the electrocardiosignals.

Disclosed is an intelligent electrocardiogram diagnosis system. The intelligent electrocardiogram diagnosis system comprises a detection bed and an electrocardiogram diagnosis device; a support is arranged on the detection bed, and a mechanical arms, a three-dimensional vision system, a disinfection device and a system starting button are arranged on the support; limb conductive electrode mechanisms are arranged on the detection bed, and an electrical control cabinet is arranged at the lower part of the detection bed; the tail ends of the mechanical arms are connected with chest conductive electrode devices; the chest conductive electrode devices and the limb conductive electrode mechanisms are electrically connected with the electrocardiogram diagnosis device correspondingly; and the system starting button, the electrocardiogram diagnosis device, the mechanical arms, the three-dimensional visual system and the disinfection device are electrically connected with an electrical control cabinet. The system has the advantages that the automation, precision and intelligentization of the electrocardiogram diagnosis process are realized by utilizing the technologies of the three-dimensional visual system, the mechanical arms, machine learning and the like, the labor intensity of medical personnel is greatly reduced, and the effect and efficiency of electrocardiogram diagnosis are greatly improved; and in addition, the problems of cross infection between medical staff and a detected person and high disinfection work intensity of electrocardiogram detection equipment, instruments and the like are solved to a great extent; and the electrocardiogram diagnosis efficiency is improved.

The invention relates to a quality control system for an electrocardiogram diagnosis report. The system comprises the steps: obtaining electrocardiogram diagnosis terms contained in a plurality of clinical electrocardiogram conclusions, and constructing a training set and a test set of a recurrent neural network model through preprocessing; taking the kth electrocardiogram diagnosis term containedin each electrocardiogram conclusion in the training set and the test set as input, taking the (k+1)th electrocardiogram diagnosis term as output, and training a recurrent neural network; and utilizing the trained recurrent neural network model to predict the (k+1)th electrocardiogram diagnosis term of the electrocardiogram conclusion in the electrocardiogram diagnosis report to be subjected to quality control, and judging whether the corresponding electrocardiogram diagnosis report has a low-level error. According to the invention, some low-level errors when an electrocardiogram doctor givesa conclusion can be corrected, and intelligent prompts can be provided for clinical diagnosis.

The invention relates to an electrocardio diagnosis model, and the model is characterized by comprising the following steps: S1, collecting N pieces of resting 12-lead electrocardiogram data, and enabling the number of atrial fibrillation electrocardiogram data to be equal to that of non-atrial fibrillation electrocardiogram data; S2, performing preprocessing: if the signal sampling frequency is lower than 200Hz, re-sampling is carried out firstly, so that the sampling frequency reaches 200Hz or above, and then filtering is carried out; S3, training a deep learning network: the deep learning network comprises at least three convolutional layers as a feature extraction module, and at least comprises two full connection layers as a classification module; S4, optimizing parameters of the deep learning network through the loss value of the loss function under minimization, and obtaining all weights and offsets in the whole deep learning network. The invention provides a loss function, which not only reduces the error between the input and the tag, but also restrains the error between the outputs of the feature layers of different types of signals.

The invention relates to an automatic diagnosis method and system for an electrocardiogram diagnosis conclusion. An electrocardiosignal is input into a trained deep learning model to obtain an outputvector representing the diagnosis conclusion, an element in the output vector represents the probability that a corresponding electrocardio term is contained in the diagnosis conclusion. According tothe method, the final diagnosis conclusion of the to-be-diagnosed electrocardiosignal is given in combination with the element values in the output vector and the probability that the electrocardio terms can be combined with one another. By means of the method, automatic diagnosis can be achieved, meanwhile, the situation that mutually exclusive electrocardio terms appear in a diagnosis conclusionis avoided, and the accuracy of electrocardio diagnosis is improved.

The invention relates to a quality control method for an electrocardiogram diagnosis report. The method comprises the steps: obtaining electrocardiogram diagnosis terms contained in a plurality of clinical electrocardiogram conclusions, and constructing a training set and a test set of a recurrent neural network model through preprocessing; taking the kth electrocardiogram diagnosis term containedin each electrocardiogram conclusion in the training set and the test set as input, taking the (k+1)th electrocardiogram diagnosis term as output, and training a recurrent neural network; and utilizing the trained recurrent neural network model to predict the (k+1)th electrocardiogram diagnosis term of the electrocardiogram conclusion in the electrocardiogram diagnosis report to be subjected to quality control, and judging whether the corresponding electrocardiogram diagnosis report has a low-level error. According to the invention, some low-level errors when an electrocardiogram doctor givesa conclusion can be corrected, and intelligent prompts can be provided for clinical diagnosis.

The invention provides long-time-history dynamic electrocardiogram data extension equipment and system and an electrocardiogram data extension method. The long-time-history dynamic electrocardiogram data extension equipment comprises a BLE host module, a storage module, a main control module and a communication module, wherein the BLE host module, the storage module and the communication module are all connected with the main control module; the BLE host module is used for being connected with an electrocardiogram recorder; the storage module is used for storing obtained dynamic electrocardiogram original data; the communication module is used for transmitting the analyzed and processed electrocardiogram diagnosis data to a data server; and the main control module is used for driving the BLE host module, the storage module and the communication module. The long-time-history dynamic electrocardiogram data extension equipment provided by the invention is high in function integration level, can be automatically connected with an electrocardiogram recorder, analyzes and processes the electrocardiogram data and uploads diagnosis data, reduces the operation burden of a user, and reduces the use cost.

The invention relates to an arrhythmia detection method which comprises the following steps: collecting an electrocardiosignal, extracting an R peak of the electrocardiosignal, and calculating an RR period value of each heart beat of the electrocardiosignal according to the R peak of the electrocardiosignal; generating an auxiliary waveform according to the RR period value of each heart beat of the electrocardiosignal; and inputting the auxiliary waveform and the electrocardiosignal into an electrocardiosignal detection network for identification, and outputting an identification result of each heart beat in the electrocardiosignal. By converting the RR period value of the electrocardiosignal into the auxiliary waveform and fusing the auxiliary waveform into heart beat classification, the accuracy of arrhythmia detection and the disease detection performance based on the artificial intelligence electrocardiograph diagnosis system can be effectively improved.