42 results about "T wave" patented technology

The invention discloses an electrocardiosignal processing method and system, and relates to the field of biomedical signal processing.The method comprises the steps that electrocardiosignals are obtained, heart beat cutting is conducted on the electrocardiosignals, multiple heart beats are obtained, and position information of QRS waves, T waves and ST wave bands of each heart beat is obtained through a Pan-Tompkins algorithm; extracting dynamic characteristics of each heart beat based on the position information; and evaluating the importance of the dynamic features by using a random forest algorithm, screening key features, and carrying out series fusion to obtain comprehensive feature information. According to the method, through multi-dimensional feature extraction and fusion, subtle changes in the complex electrocardiosignals are captured more comprehensively, and therefore the accuracy and efficiency of electrocardiosignal analysis are improved.

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.

The invention relates to the technical field of medical equipment, and provides an electrocardiosignal R wave identification method, device and equipment. The method comprises the steps of determining upper and lower limit thresholds of electrocardiosignal state transition, and determining a first state transition point and a second state transition point based on intersection points of the upper and lower limit thresholds and electrocardiosignals and signal trends on two sides of the intersection points; acquiring a plurality of groups of current continuous first and second state transition points, and determining an R wave starting point based on the time difference between the first and second state transition points in the plurality of groups; timing is started from the R wave starting point to the end of the ventricular refractory period, then a first state transition point closest to the R wave starting point is recognized, and the newly recognized first state transition point serves as a new R wave starting point. According to the scheme, the recognition accuracy of the R wave in the electrocardiosignal is improved, the probability that the R wave in the electrocardiosignal is mistakenly removed or the T wave is recognized as the R wave is reduced, and the problem that counterpulsation errors are generated in the IABP technical process is reduced.

A system that enables safe cardiac pulsed field ablation by using multiple types of sensors on a patch to detect and cross-check that the heartbeat is in a safe phase to receive an ablation pulse. The patch may contain for example any or all of ECG electrodes, PPG sensors, accelerometers, and microphones. The patch may also contain one or more pulse return electrodes for unipolar ablation. A signal analyzer coupled to a pulse generator may receive sensor data from the patch and may analyze this data to determine the heartbeat phase. If data from different sensors are inconsistent, then pulse generation may be disabled as a safety feature. Otherwise, pulses may be enabled when the heartbeat is in a safe phase; for example, pulses may be excluded during the T wave.

The invention relates to a method, a device and equipment for measuring a QTc interval in a flat movement test process, a storage medium and a program product. The method comprises the steps of collecting electrocardiogram data according to a preset mode in a flat plate exercise test process; respectively determining a QRS wave group starting point and a T wave end point from the electrocardiogram data; determining a measured QT interval according to the QRS wave group starting point and the T wave end point; the measured QT interval is corrected for the first time through the time limit of the QRS wave group, and the corrected QT interval is obtained; automatically correcting the corrected QT interval for the second time according to the heart rate change of the testee in the tablet exercise test process to obtain a QTc interval; and drawing a real-time change trend chart of the QTc interval according to different stages of the flat plate motion test. Therefore, the QTc interval measurement of each stage in the plate motion test process can be automatically completed, the real-time measurement value of the QTc interval is obtained, the test efficiency is high, and the result is more accurate.

According to one aspect of the present invention, provided is a method for classifying an electrocardiogram signal, the method comprising the steps of: converting an electrocardiogram signal into first graph data including a P node corresponding to a P wave, a QRS node corresponding to a QRS complex, and a T node corresponding to a T wave; and outputting the classification result of the electrocardiogram signal by processing the first graph data by using a graph convolutional network (GCN)-based classification model.

The invention discloses an electrocardiogram ventricular escape real-time judgment method and system, relates to the technical field of electrocardiograms, solves the problem of lack of multi-dimensional analysis and dynamic characteristic verification of electrocardiogram waveforms, and aims at solving the problem that a time period in which waveform abnormity really exists is focused through continuous analysis of fluctuation points of a to-be-recognized section, so that the accuracy of the electrocardiogram ventricular escape real-time judgment is improved. A symmetric space vector range of a normal P wave is used as a reference, whether a to-be-analyzed waveform contains the P wave is visually judged through matching of the form and the position of a to-be-verified vector, missed judgment caused by low amplitude of the P wave or overlapping of the P wave in a T wave is avoided, and the accuracy of the judgment is improved. A complete analysis chain from electrocardiosignals to clinical diagnosis is constructed through a four-layer logic architecture of pacing point positioning, feature waveform matching, abnormal time capturing and escape pacing mechanism verification, high-precision recognition of ventricular escape pacing is achieved, and the accuracy of ventricular escape pacing recognition is improved through deep fusion of a physiological mechanism and an algorithm. And an interpretable and traceable technical path is provided for automatic electrocardio diagnosis.

The invention provides a heart sound segmentation method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out signal preprocessing on an electrocardiosignal and a heart sound signal, and determining the preprocessed electrocardiosignal and the preprocessed heart sound signal; aiming at the condition that the relationship category is a synchronous relationship, performing joint segmentation processing on the preprocessed heart sound signal based on the R wave and the T wave of the preprocessed electrocardiosignal, and determining the segmentation positions of the first heart sound and the second heart sound of the preprocessed heart sound signal; and independently segmenting the preprocessed heart sound signals on the basis of a Markov chain model aiming at the condition that the relationship category is an asynchronous relationship, and determining segmentation positions of the first heart sound and the second heart sound. According to the method, the segmentation position of the heart sound signal can be quickly determined through the waveform of the electrocardiosignal, intelligent compensation can be carried out through the Markov chain model to determine the segmentation position, and the problem of signal missing or wrong segmentation caused when the electrocardiogram cannot be fully acquired is effectively avoided.

The invention discloses a portable blood circulation auxiliary device, a control method and a blood circulation auxiliary system. The portable blood circulation auxiliary device integrates the functions of blood oxygen and electrocardiogram detection and multi-part electrical stimulation, is small in size, supports portable use, gets rid of dependence of a traditional air bag type device on a fixed power source, does not limit activities of a user and meets daily scene requirements. Moreover, by combining double-index dynamic adjustment of the electrocardio waveform and the blood oxygen data, the discharge frequency is determined through the real-time heart rate, the discharge intensity is adjusted based on the blood oxygen data, precise and personalized intervention is achieved, and the defect that a traditional device lacks self-adaptive adjustment is overcome to a certain degree. Besides, electric stimulation is released according to the time sequence that the crus delays K1 milliseconds at the peak of the R wave and the thigh delays K2 milliseconds at the peak of the R wave, the time sequence is synchronously continued to the starting point of the T wave, the physiological characteristics of lower limb muscles and the blood pumping rhythm of the heart are met, the use comfort is improved, meanwhile, blood backflow is efficiently promoted, and the heart burden is relieved.

The application provides an arrhythmia signal detection method and system based on expert knowledge, wherein the method comprises the following steps: performing signal filtering on an acquired electrocardio signal to filter out high-frequency noise and baseline drift in the electrocardio signal; performing P-QRS-T positioning detection on the signal-filtered electrocardio signal to obtain a QRS complex and P / T wave; performing expert-knowledge-based feature mapping on the obtained QRS complex and P / T wave to obtain a category feature of the electrocardio signal; comparing the obtained category feature of the electrocardio signal with a preset threshold to determine an arrhythmia signal, thereby completing detection of the arrhythmia signal. Meanwhile, a corresponding terminal and medium are provided. The application improves the detection accuracy; fully considers the morphology of an abnormal heartbeat and the timing of positioning the P / T wave, can effectively avoid the case that the P-QRS-T positioning is inaccurate due to the abnormal heartbeat, has high robustness, and has low computational complexity.

The present application provides an electrocardiogram analysis method based on a three-dimensional electrocardiogram superposition diagram, which can realize the rapid location of abnormal P wave and T wave sample data in the whole heartbeat for a large amount of dynamic electrocardiogram data, and the method is simple and easy to grasp. It constructs a three-dimensional electrocardiogram superposition diagram, and establishes a graphical correspondence between the waveform superposition diagram, the chromatogram, the characteristic parameter scatter diagram and the sample diagram through the three-dimensional electrocardiogram superposition diagram; the QRS wave with drastic amplitude changes is quickly found through the electrocardiogram superposition diagram, and the P wave or / and T wave with obvious changes is quickly found through the chromatogram, and the data suspected of abnormality is located; the characteristic parameter scatter diagram based on the characteristic parameter and time representation is designed to make up for the difficulty of observation caused by the voltage amplitude of the central electric wave of the chromatogram being converted into the depth of color, and the heartbeat characteristic parameter value of each heartbeat data is confirmed through the characteristic parameter scatter diagram, and each suspected data is confirmed in combination with the sample diagram, and finally the abnormal data is located.

The invention discloses a non-contact electrocardiogram signal reconstruction method for a millimeter-wave radar, which is applied to the cross technical field of millimeter-wave radar and biomedical signal processing, and aims to solve the problems that weak characteristics such as P waves and T waves are lost due to neglect of time-frequency dynamic characteristics of electrocardiogram signals in the conventional electrocardiogram reconstruction technology based on the millimeter-wave radar; and hardware non-ideal noise and motion interference in radar signals are not processed in a targeted manner, and the standardized root-mean-square error of reconstructed signals is generally high. The method comprises the steps of firstly eliminating hardware noise and breathing interference in radar original signals, then capturing time-frequency details of an electrocardiogram through multi-scale short-time Fourier transform, strengthening key feature focusing in combination with an attention-guided U-shaped network, and finally optimizing a model by using a time domain-frequency domain joint loss function, so as to obtain a time domain-frequency domain joint loss function. The core characteristics of P waves, QRS wave groups, T waves and the like are reserved, and meanwhile motion artifacts and multi-source noise are remarkably restrained.

The present invention relates to an electrocardiogram feature signal extraction method and terminal, which obtain an input electrocardiogram signal; filter and preprocess the electrocardiogram signal; amplify the electrocardiogram signal using a square function, and process the electrocardiogram signal using a moving window integration algorithm; sequentially detect the electrocardiogram signal using a feature point detection algorithm, wherein the types of feature point detection algorithms include R wave peak point detection algorithm, P wave peak point detection algorithm, T wave peak point detection algorithm, S wave valley point detection algorithm, and Q wave valley point detection algorithm; during the process of detecting the electrocardiogram signal, an adaptive threshold value corresponding to the type of feature point detection algorithm is obtained using a threshold adaptive algorithm, and whether the electrocardiogram signal meets the preset detection conditions of the feature point detection algorithm is determined. The electrocardiogram signal that meets the preset detection conditions is used as a feature point peak value / feature point valley value and is saved. Therefore, the present invention can accurately and completely detect the feature data of the electrocardiogram signal.

The invention relates to the technical field of integrated circuits, and discloses an anti-aliasing filter, a physiological signal acquisition device and a chip, the anti-aliasing filter comprises a pre-amplification module, an active filtering module and a double-T wave limiting module; the pre-amplification module is used for amplifying the electrocardio differential signal to obtain a first electrocardio signal; the active filtering module is used for filtering the first electrocardiosignal to obtain a second electrocardiosignal; and the double-T wave limiting module is used for processing a power frequency interference signal with a preset frequency in the second electrocardiosignal to obtain an effective electrocardiosignal. The power frequency interference can be effectively suppressed, and the accuracy of the electrocardiosignal is improved.

A system that enables safe cardiac pulsed field ablation by using multiple types of sensors on a patch to detect and cross-check that the heartbeat is in a safe phase to receive an ablation pulse. The patch may contain for example any or all of ECG electrodes, PPG sensors, accelerometers, and microphones. The patch may also contain one or more pulse return electrodes for unipolar ablation. A signal analyzer coupled to a pulse generator may receive sensor data from the patch and may analyze this data to determine the heartbeat phase. If data from different sensors are inconsistent, then pulse generation may be disabled as a safety feature. Otherwise, pulses may be enabled when the heartbeat is in a safe phase; for example, pulses may be excluded during the T wave.

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.

The present disclosure provides an identity recognition method and device, electronic equipment and computer storage medium. The identity information of a user is automatically recognized, and the efficiency of user identity recognition is improved. The method comprises: acquiring electrocardio data of at least two leads of a user, wherein the leads comprise limb leads and / or chest leads; performing contour recognition on each electrocardio data to obtain the contour of each wave of a specified type in the electrocardio data, wherein the each wave of the specified type comprises at least one of a P wave, a T wave and a QRS wave; projecting the contour of each wave corresponding to each electrocardio data in a specified plane to obtain an electrocardio vector ring of the user; respectively matching the electrocardio vector ring of the user and each preset template electrocardio vector ring to obtain each matching value, wherein each preset template electrocardio vector ring corresponds to different user identity information; and determining the identity information of the user according to the each matching value.

The invention relates to a device for monitoring and optimizing the time-triggered stability of extracorporeal circulation support, as well as to an extracorporeal circulation support control unit comprising such a device and to a corresponding method. Accordingly, the present invention proposes a method for monitoring and optimizing time-triggered stability of extracorporeal circulation support, comprising the steps of: receiving an electrocardiogram signal of a supported patient within a predetermined duration; determining a P wave, an R wave and a T wave from the received electrocardiogram signal; a predetermined trigger signal is determined from the electrocardiogram signal of the current cardiac cycle, taking into account the at least one detected P-wave and / or the at least one detected T-wave.

The present application relates to a kind of ECG feature wave segmentation method and FS-Net model, ECG feature wave segmentation method specifically includes the following steps: S1.ECG signal denoising;S2.Using Pan-Tompkins algorithm demarcation ECG signal R peak information, calculate RR interval information, then using sliding window to data normalization processing;S3.ECG signal waveform correction and feature enhancement: first, ECG signal is carried out smooth processing, then the QRS wave of ECG signal, P wave and T wave are corrected, finally feature enhancement is carried out;S4.FS-Net model is constructed;S5.ECG signal after processing in step S1-S3 is input into FS-Net model and is trained, realizes the segmentation to feature wave in ECG signal;S6.post-processing algorithm;S7.ECG signal segmentation: using the FS-Net model after training to the measured ECG signal is segmented.The present application is corrected and enhanced to ECG signal, effectively enhances the performance of P wave and T wave in electrocardiogram, improves the accuracy of feature wave detection.

The invention discloses an electrocardiogram data processing method and device for heart failure assessment, terminal equipment and a storage medium. The electrocardiogram data processing method comprises the following steps: acquiring electrocardiogram data of a patient; performing wave crest screening processing on an electrocardiogram in the electrocardiogram data to obtain a plurality of electrocardiogram waves; determining an R wave, a P wave, a Q wave, an S wave and a T wave in the plurality of electrocardiowaves; based on the generation time, respectively extracting an electrocardio acquisition parameter and an electrocardio waveform parameter corresponding to each electrocardio wave from the electrocardio data; for each electrocardiogram wave, establishing a coordinate system by taking the electrocardiogram waveform parameter type of the electrocardiogram wave as an abscissa and taking the electrocardiogram waveform parameter value as an ordinate, determining the electrocardiogram waveform type of the electrocardiogram wave based on the parameter value range of the historical electrocardiogram waveform type, and summarizing the electrocardiogram waveform type and the electrocardiogram acquisition parameters to obtain an electrocardiogram acquisition parameter set; generating electrocardiogram analysis data of the patient; on the basis of the obtained accurate electrocardio analysis data, heart failure assessment is carried out in combination with the human body impedance of the user, and the accuracy of heart failure assessment can be improved.

The present application proposes a method for calculating an ST offset value, which includes: acquiring an electrocardiogram (ECG) signal, wherein the ECG signal includes an ST segment located between a QRS complex and a T wave; extracting the R wave site of the ECG signal; determining a first Q wave starting point set and a first S wave ending point set in the ECG signal based on the R wave site of the ECG signal and a preset square formula; determining a second Q wave starting point set and a second S wave ending point set in the ECG signal based on the R wave site of the ECG signal and a preset differential formula; determining the isoelectric point position of the ECG signal based on the first Q wave starting point set and the second Q wave starting point set; determining the ST point position of the ECG signal based on the first S wave ending point set and the second S wave ending point set; and determining the ST offset value of the ECG signal based on the isoelectric point position and the ST point position.

The present application relates to a kind of digital electrocardiogram waveform identification and time limit parameter measurement method, the present application selectively according to the signal type of the need to retain one or more of these interference signals and part electrocardio feature signal is decomposed to obtain the feature signal of the layer needed to retain, again by the selective superposition integration of each layer effective signal and feature signal, join the method for identifying J point, QRS wave, P wave, T wave, retain the effective signal data of 3 wave bands finally for doctor auxiliary diagnosis in each layer, then join positioning method is used to realize the accurate positioning of the start point and end point position of PR interval, QT interval, QRS time limit, then the corresponding effective value is determined in conjunction with the amplitude of each wave band, finally join the algorithm for the calculation of heart rate to realize the calculation of heart rate.The present application can shorten the effective time of doctor to view electrocardiogram, improve the efficiency of doctor to read electrocardiogram, reduce the misdiagnosis rate caused by long time reading.

Systems and methods for ambulatory detection of Q wave-to-T wave (QT) interval prolongation are discussed. A medical-device system comprises a controller circuit and a user interface device. The controller circuit includes a long QT syndrome (LQTS) detector that measures a QT interval from a subcutaneous cardiac signal sensed from a patient using implantable electrodes, and detects an indication of QT prolongation using the measured QT time interval and a programmable threshold received as a user input from the user interface. The control circuit can adjust device operation based on the detected indication of QT prolongation. An output unit can generate a programmable alert of the QT prolongation corresponding to the user input of the programmable threshold.

Disclosed is an apparatus for electrocardiogram (ECG) wave classification using machine learning being capable of applying a segmentation technique to an ECG wave, checking a feature for each section of a P wave, a Q wave, an R wave, an S wave, a T wave, and a noise wave included in the ECG wave, quickly classifying heart beats into a normal beat (N), a supraventricular beat(S), a ventricular beat (V), and noise, and removing the noise to make medical decisions quickly and accurately.

The signal analysis device includes an analysis unit that obtains at least a part of the parameters for determining at least any one of the first unimodal distribution, the first cumulative distribution function, the second unimodal distribution, and the second cumulative distribution function when approximating the time waveform of the R wave by using the waveform generated by the difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, or the waveform obtained by adding a level value to this waveform, and obtains at least a part of the parameters for determining at least any one of the third unimodal distribution, the third cumulative distribution function, the fourth unimodal distribution, and the fourth cumulative distribution function when approximating the time-axis reversed waveform of the time waveform of the T wave by using the waveform generated by the difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, or the waveform obtained by adding a level value to this waveform, and obtains the parameters indicating the activity of the myocardium of the heart through the calculation of the obtained parameters.

The invention relates to an arrangement (200) comprising a non-transvenous implantable cardioverter-defibrillator device (210) for stimulating a human or animal heart, the non- transvenous implantable cardioverter-defibrillator device (210) comprising a stimulation unit (270) for emitting stimulation pulses (140) externally to the heart; and a programming device (220) operatively coupled to the non-transvenous implantable cardioverter- defibrillator device (210); wherein the arrangement is capable to predict an expected time of occurrence of a future T wave (Bn) by determining the future T wave (Bn) on the basis of a function using first coefficients (ai), second coefficients (ci) and / or third coefficients (di).

The present invention discloses a method, apparatus, terminal device and storage medium for processing ECG data for heart failure assessment, which obtains ECG data of a patient; performs peak screening processing on an ECG in the ECG data to obtain a plurality of ECG waves; determines R waves, P waves, Q waves, S waves and T waves in the plurality of ECG waves; extracts ECG acquisition parameters and ECG waveform parameters corresponding to each ECG wave in the ECG data based on generation time; establishes a coordinate system for each ECG wave with the ECG waveform parameter type of the ECG wave as the horizontal coordinate and the ECG waveform parameter value as the vertical coordinate, determines the ECG waveform type of the ECG wave based on the parameter value range of historical ECG waveform types, and summarizes the ECG waveform type and the ECG acquisition parameters to generate ECG analysis data of the patient; performs heart failure assessment based on the obtained accurate ECG analysis data in combination with the user's body impedance, which is beneficial to improving the accuracy of heart failure assessment.

A system that enables safe cardiac pulsed field ablation by using multiple types of sensors on a patch to detect and cross-check that the heartbeat is in a safe phase to receive an ablation pulse. The patch may contain for example any or all of ECG electrodes, PPG sensors, accelerometers, and microphones. The patch may also contain one or more pulse return electrodes for unipolar ablation. A signal analyzer coupled to a pulse generator may receive sensor data from the patch and may analyze this data to determine the heartbeat phase. If data from different sensors are inconsistent, then pulse generation may be disabled as a safety feature. Otherwise, pulses may be enabled when the heartbeat is in a safe phase; for example, pulses may be excluded during the T wave.

The invention relates to a method for extracting ST-segment signals in an electrocardiogram, belongs to the technical field of electrocardiosignal processing, and solves the problem that the ST-segment signals in the electrocardiogram cannot be accurately extracted in the prior art. The method comprises the following steps: acquiring an electrocardiosignal, and detecting the electrocardiosignal to obtain an R wave in the electrocardiosignal; calculating the confidence coefficient of each R wave in the electrocardiosignal; removing R-wave fragments with low confidence to obtain preprocessed electrocardiosignals; based on the preprocessed electrocardiosignals, the positions of S waves and T waves are determined through an inverted sequence method, and ST-segment electrocardiosignals are extracted. And rapid and accurate ST segment extraction is realized.

This application proposes a classification model, construction method, and application of myocardial ischemia based on 12-lead ECG. By detecting the ST-T segment beat-by-beat changes using entropy domain, frequency domain, and Lyapunov domain analysis methods, spatiotemporal ECG feature parameters related to myocardial ischemia are obtained. A machine learning model for predicting myocardial ischemia is established, ultimately achieving optimal ECG feature selection related to myocardial ischemia. This addresses the problem that conventional 12-lead ECGs often lack clear ST segment or T wave features related to myocardial ischemia, severely affecting the sensitivity and accuracy of myocardial ischemia diagnosis. The method includes: acquiring a 12-lead ECG; calculating the ST-T segment sample entropy of the 12-lead ECG; converting the 12-lead ECG into a 3-lead ECG vector map; extracting the ST-T segment from the 3-lead ECG vector map and calculating its spatial and temporal feature values; and using a grid search method to select the optimal ECG features reflecting myocardial ischemia. Therefore, the sensitivity and accuracy of myocardial ischemia diagnosis are improved.