32 results about "Electrodiagnosis" patented technology

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.

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 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 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 an electrocardio automatic diagnosis method based on intelligent simulation modeling, and the method comprises the following steps: S1, carrying out the preprocessing of electrocardio signasl, employing a fast planting filter with a window length of 0.5*sr+1 to remove baseline offset, employing the sr as a sampling rate, and employing fast wavelet transform to remove high-frequency noise; S2, waveform positioning: respectively positioning the starting point and the ending point of a QRS, the starting points, the ending points and the peak values of a P wave and a T wave, and the positions of three wave crests of a Q wave, an R wave and an S wave based on a full-automatic electrocardiograph waveform detection algorithm of wavelet transformation, and unifying the number of cardiac beats of each lead according to a lead II; and when the starting point and the end point of the QRS are positioned, carrying out secondary high-frequency noise removal processing on the electrocardio signals; S3, feature extraction: extracting related features required by electrocardio diagnosis; and S4, electrocardio diagnosis: converting diagnosis thinking of a doctor into electrocardiogram feature description by using a rule method so as to diagnose electrocardio diseases; the obtained aluminum alloy base material has good conductivity.

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.

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.