53 results about "Electrocardiogram QRS complex" patented technology

The invention discloses an ultra-high heart rate electrocardio acquisition system, which comprises a signal acquisition unit adopting an ADS131E08 chip and supporting 8 channels, a 24-bit delta-sigma ADC (Analog to Digital Converter) and programmable gain amplification; the dynamic sampling rate adjusting unit is used for dynamically adjusting the sampling rate between 1kSPS and 64kSPS according to the heart rate change, and the sampling rate is increased to be greater than or equal to 2kSPS when the heart rate exceeds 200 times per minute; a low-noise design circuit has a common-mode rejection ratio greater than or equal to 110dB, a dynamic range greater than or equal to 118dB and total harmonic distortion less than or equal to-90dB, and power frequency interference is suppressed; the morphological enhancement module is used for enhancing QRS wave group characteristics by using digital morphological filtering; the multi-lead fusion module is used for fusing multi-lead signals through time sequence alignment and R-wave phase difference analysis; and the anti-interference module is integrated with a hardware comparator, and is used for lead falling detection and dynamic digital notch filtering. The system is suitable for researches on myocardial ischemia, arrhythmia and drug cardiotoxicity. Compared with the prior art, the device has the advantages of being simple in structure, low in cost, high in precision, high in reliability and the like, and can effectively solve the problem of animal ultrahigh heart rate electrocardio collection.

The invention discloses a coronary artery lesion risk prediction method, a coronary artery lesion risk processing device, a coronary artery lesion risk prediction system and a computer readable storage medium. Obtaining a point sequence according to high-frequency QRS wave group data obtained by the exercise electrocardiogram data; aiming at a point sequence corresponding to any electrocardiogram lead, taking any sampling point in a plurality of sampling points in the point sequence as a current reference sampling point in a traversal cycle, and traversing sampling points in a preset time interval from the current reference sampling point, determining whether a first sampling point and a second sampling point meeting a preset condition exist in the preset time interval, wherein the first sampling point is earlier than the second sampling point in time sequence; and if the preset condition is met, determining that the high-frequency QRS wave group data has waveform characteristics indicating the coronary artery lesion, thereby determining the possibility of the coronary artery lesion. Compared with a previous scheme, the scheme has the advantage that the qualitative accuracy of coronary artery lesion is improved.

The invention discloses an electrocardio data processing and coronary heart disease risk prediction method, device and system and a medium. The method comprises the steps of obtaining exercise electrocardio data output through at least one electrocardiogram lead; analyzing the exercise electrocardiogram data to obtain a heart rate sequence corresponding to each electrocardiogram lead; extracting a QRS wave group corresponding to each heart beat from the exercise electrocardio data; dividing a plurality of heart rate intervals according to the obtained heart rate sequence; according to the heart rate interval to which the heart rate corresponding to each QRS wave group belongs, classifying the QRS wave group into a group corresponding to the corresponding heart rate interval; determining an average QRS wave group corresponding to each heart rate interval according to all the QRS wave groups in each group; extracting the high-frequency energy of the average QRS wave group corresponding to each heart rate interval; and determining an energy sequence according to the high-frequency energy corresponding to each heart rate interval. According to the scheme, high-frequency components and dynamic load information which are more sensitive to ischemia are utilized, so that the detection rate and the identification capability of coronary heart diseases, particularly early and mild lesions, can be remarkably improved.

The invention belongs to the technical field of electrocardiosignal classification, and discloses an electrocardiosignal quality automatic identification and classification method, which comprises the following steps: carrying out discrete wavelet decomposition on an electrocardiosignal to obtain a low-frequency sub-band, a reconstructed sub-band and a high-frequency sub-band, and carrying out time domain feature extraction on the low-frequency sub-band, the reconstructed sub-band and the high-frequency sub-band; according to the method, wavelet analysis, time domain feature extraction and a fault diagnosis technology are organically combined, and automatic identification and classification of electrocardiogram signal quality are achieved on the premise of not depending on event features such as QRS complex waves and RR intervals.

The invention discloses an electrocardio feature point positioning method based on double U-Net fused long and short range context information, comprising: preprocessing an electrocardio signal, including re-sampling to a unified sampling rate, and removing noise interference; segmenting the electrocardiosignal, wherein the electrocardiosignal is segmented into a single-cardiac-beat segment and a neighbor multi-cardiac-beat segment based on the R peak position; data enhancement operation is applied to the segmented cardiac beat segments; using double U-Net to extract features, including a first U-Net to extract local semantic features of a single cardiac beat segment and a second U-Net to extract global semantic features of a multi-cardiac beat segment; fusing the local semantic features and the global semantic features through a cross-level dual gating module to generate purified fused features; and positioning feature points of the electrocardiosignal based on the fusion features, wherein the feature points comprise the positions of P waves, QRS wave groups and T waves. According to the invention, the precision and robustness of feature point positioning are improved.

A method, device, system and medium for electrocardio data processing and coronary heart disease risk prediction, motion electrocardio data output through at least one electrocardio lead is acquired; the motion electrocardio data is analyzed to obtain a heart rate sequence corresponding to each electrocardio lead; each QRS complex corresponding to each heartbeat is extracted from the motion electrocardio data; a plurality of heart rate intervals are divided according to the obtained heart rate sequence; each QRS complex is classified into a group corresponding to a heart rate interval according to the heart rate interval to which the heart rate corresponding to the QRS complex belongs; the average QRS complex corresponding to each heart rate interval is determined according to all QRS complexes in each group; the high-frequency energy of the average QRS complex corresponding to each heart rate interval is extracted; and an energy sequence is determined according to the high-frequency energy corresponding to each heart rate interval. The present scheme uses high-frequency components and dynamic load information which are more sensitive to ischemia, and can significantly improve the detection rate and discrimination ability for coronary heart disease, especially early and mild lesions.

The application relates to the technical field of electrocardiosignal processing, and discloses a monitoring electrocardio data analysis method based on online monitoring, wherein the method comprises the following steps: performing multistage filtering pretreatment on an original electrocardio signal; detecting a QRS complex and extracting an RR interval sequence; extracting time domain statistical features, frequency domain features and wavelet coefficients to form an electrocardio feature vector; inputting the electrocardio feature vector and an electrocardio signal segment into an atrial fibrillation detection model containing a signal feature learning branch and a sequence feature learning branch, and outputting an atrial fibrillation detection result; and generating an alarm information based on the detection result and pushing the alarm information to a medical terminal. Through atrial fibrillation load statistics and stroke risk grade evaluation, the single abnormality detection result is expanded into a continuous risk quantization index, a coherent analysis link from abnormality identification to risk evaluation is provided for clinical doctors, complete data storage is associated with a patient identifier and a time stamp, all analysis processes can be traced back, and the needs of clinical audit and department review are met.

Disclosed are a myocardial bridge risk prediction method, a processing device, a system and a computer readable storage medium. The method comprises the following steps: determining a point sequence according to candidate high-frequency QRS complex data in which a waveform feature indicating coronary artery lesion does not exist in exercise electrocardiogram data, and traversing the sampling points in the point sequence. If there are two sampling points that meet the preset condition, it is determined that there is a myocardial bridge risk. The scheme can improve the specificity and sensitivity of auxiliary diagnosis from the electrophysiological mechanism in a non-invasive environment, and is convenient and effective.

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 relates to the technical field of sleep-aiding monitoring, in particular to a personalized sleep-aiding system and method based on artificial intelligence. The method comprises the steps that multi-source data such as sleep videos and ECG signals of a user are collected, the advanced graph neural network technology is used for conducting feature extraction and analysis on the sleep videos, the sleep stage of the user is accurately determined, meanwhile, QRS complex wave information in the ECG signals is extracted to obtain a breathing oscillogram of the user, the breathing standard reaching coefficient is calculated, and the sleep stage of the user is determined. The sleep state parameter is combined to calculate the sleep state quality coefficient, finally, the sleep environment parameter of the user is dynamically adjusted according to the respiration standard coefficient and the sleep state quality coefficient, personalized sleep aiding service is provided for the user, and therefore the sleep quality of the user is improved, and the sleep condition of the user is improved.

Embodiments of the present application disclose an atrial fibrillation detection method and device, a storage medium and a computer device. The method comprises: receiving an ECG signal sequence of a preset length and performing a preprocessing operation on the ECG signal; determining a QRS complex position based on the processed ECG signal; extracting a plurality of characteristic parameters according to the QRS complex position; and identifying and classifying the plurality of characteristic parameters based on a classifier. The present application simultaneously utilizes two features of an atrial fibrillation waveform, i.e., irregular changes in an RR interval and a baseline of a heartbeat interval being an irregular continuous high-frequency low-amplitude fibrillation wave, to detect and analyze an atrial fibrillation signal. Specifically, by determining a QRS complex position and extracting a plurality of characteristic parameters based on the position, the change rule of the RR interval and the related plurality of characteristic parameter values of the fibrillation wave are obtained. Further, a support vector machine (SVM) classifier is used to perform binary classification prediction on the above characteristic parameters, thereby achieving detection and identification of the atrial fibrillation signal and greatly improving the accuracy of the analysis result of the atrial fibrillation signal.

Embodiments of a wearable cardioverter defibrillator (WCD) system include a support structure for wearing by an ambulatory patient, a posture detector and at least one processor. When worn, the support structure maintains electrodes on the patient's body, and using the posture detector and the patient's ECG received via the electrodes, the processor determines the patient's posture, formulates posture-based templates of QRS complexes, and the patient's heart rate. The processor can use these determinations to distinguish between VT and SVT and make no-shock, and shock decisions.

The invention relates to the technical field of intelligent wearable equipment, and provides an ECG electrocardio measurement and analysis method of an intelligent ring based on an AI model. The method is applied to the intelligent ring and comprises the following steps: acquiring an original signal and performing multi-band decomposition on the original signal; identifying a characteristic frequency band corresponding to a plurality of noise types in the decomposed original signal; the noise types at least comprise motion artifacts, muscle vibration and electromagnetic interference; extracting ECG key features from the decomposed original signal according to an attention mechanism, wherein the ECG key features at least comprise an RR interval and a QRS wave group; filtering the characteristic frequency band according to the ECG key characteristics, and outputting an ECG characteristic signal; inputting the ECG characteristic signal into a pre-trained AI model, and outputting a signal quality score; and if the ECG characteristic signal is determined to be qualified according to the signal quality score, outputting the ECG characteristic signal for ECG equivalent index calculation.

Disclosed is a method for obtaining a start point and an end point of a QRS complex by using an imaginary number. The method of obtaining a start point and an end point of a QRS complex by using an imaginary number comprises: receiving, by a receiving unit, a measured electrocardiogram signal, performing, by a transform unit, Hilbert transform on the electrocardiogram signal, and obtaining, by a measurement unit, the amplitude of the electrocardiogram signal by creating a phase space map on the basis of the Hilbert transformed electrocardiogram signal, wherein a vertex of an imaginary part representing the start point and the end point of the QRS complex exists in the phase space map, and the vertex exists in a phase space region of the QRS complex.

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 application provides a processing method and device of a QRS complex anomaly classification model and processing equipment. The QRS complex anomaly classification model is constructed by introducing a QRS complex anomaly classification mechanism based on 4 leads or 8 leads. Experimental results show that the detection accuracy of different types of abnormal QRS complexes is significantly improved in the 8-lead format. In addition, due to the simplification of data, especially in the 4-lead format, the application cost in terms of data processing time, data application time and data storage time is obviously improved, and the personnel workload in clinical work is also significantly reduced, so the application value is better.

The invention relates to the technical field of medical instruments, in particular to a heart health state assessment method, a processing device, a system and a computer readable storage medium. Obtaining a first maximum voltage according to first high-frequency QRS wave group data obtained according to the exercise electrocardio data, obtaining a waveform type of a first high-frequency QRS wave group corresponding to each electrocardiogram lead, obtaining a first lead positive index, and obtaining a second maximum voltage according to a second high-frequency QRS wave group number obtained according to the resting electrocardio data, and obtaining a wave crest number corresponding to each electrocardiogram lead; a high-frequency form index, a second lead positive index and a QRS time limit; respectively obtaining a cardiopulmonary function index, a vascular response capability index and a myocardial cell vitality index based on the data; and determining a heart health assessment result according to the cardiopulmonary function index, the vascular response capability index and the myocardial cell vitality index. According to the scheme, the evaluation dimensionality is comprehensive, the defect of single-dimensional evaluation in the prior art is overcome, and the requirement for integrated evaluation of heart health clinically is met.

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 discloses a myocardial bridge risk prediction method, a processing device, a system and a computer readable storage medium, and the method comprises the steps: determining a point sequence according to candidate high-frequency QRS wave group data in motion electrocardio data, and if two sampling points meeting the preset condition exist, determining that the myocardial bridge risk exists. According to the scheme, the auxiliary diagnosis specificity and sensitivity can be improved from the electrophysiological mechanism in a non-invasive environment, and convenience and effectiveness are achieved.

The invention relates to an information detection method and device, equipment, a storage medium and a program product. According to one embodiment of the invention, the method comprises the following steps: transmitting a wireless sensing signal to a detected object based on a wireless sensing assembly, and receiving a reflected signal of the wireless sensing signal, the reflected signal comprising a signal transmitted after the wireless sensing signal is in contact with the detected object; determining a target electrocardiosignal waveform of the detected object based on the reflected signal and the wireless sensing signal; identifying a QRS wave group from the target electrocardiosignal waveform; and in response to the identified at least two QRS wave groups, determining RR interval information of the detected object based on the at least two QRS wave groups. According to the method, the RR interval information of the QRS wave group in the electrocardiosignal can be accurately detected, and the requirement for finer health information detection is met.

Embodiments of a wearable cardioverter defibrillator (WCD) system include a support structure for wearing by an ambulatory patient, a posture detector and at least one processor. When worn, the support structure maintains electrodes on the patient's body, and using the posture detector and the patient's ECG received via the electrodes, the processor determines the patient's posture, formulates posture-based templates of QRS complexes, and the patient's heart rate. The processor can use these determinations to distinguish between VT and SVT and make no-shock, and shock decisions.

Disclosed are a sudden cardiac death risk prediction method, device, system and storage medium. The method comprises: acquiring resting electrocardiogram data output through a plurality of electrocardiogram leads, processing the resting electrocardiogram data to obtain high-frequency QRS complex data corresponding to each electrocardiogram lead, and obtaining high-frequency QRS features corresponding to each electrocardiogram lead according to the high-frequency QRS complex data; determining a lead positive index corresponding to each electrocardiogram lead according to a high-frequency morphology index corresponding to each electrocardiogram lead; determining whether there is a sudden cardiac death risk based on at least one of a plurality of conditions; and in the case of determining that there is a sudden cardiac death risk, predicting a sudden cardiac death risk level according to a sum of the high-frequency morphology indexes corresponding to each electrocardiogram lead. The scheme provided in the embodiments of the present application can quantitatively predict the sudden death risk based on the sum of the high-frequency morphology indexes of a plurality of (greater than or equal to 12) leads reflecting the myocardial injury volume in the case of qualitative determination based on a plurality of risk factors, and can take into account the misdiagnosis rate and accuracy.

This invention belongs to the field of electrocardiogram (ECG) signal classification technology and discloses an automatic identification and classification method for ECG signal quality. The method involves performing discrete wavelet decomposition on the ECG signal to obtain low-frequency sub-bands, reconstructed sub-bands, and high-frequency sub-bands. Time-domain features are then extracted from these sub-bands. Based on the results of the time-domain feature extraction, global noise classification and local noise classification are performed. This invention organically combines wavelet analysis, time-domain feature extraction, and fault diagnosis technology, enabling automatic identification and classification of ECG signal quality without relying on event features such as QRS complexes and RR intervals.

The invention discloses an electrocardio QRS (Quadrature Reference Signal) wave detection method based on an improved Hamilton-Tompkins structure. The method comprises the following specific steps: step 1, carrying out self-adaptive power frequency interference filtering and band-pass filtering on an original electrocardiosignal to suppress noise in the signal; step 2, differential nonlinear combination is performed on the filtered signals, and QRS waveform characteristics in the signals are enhanced; step 3, performing adaptive smoothing on the enhanced signal based on dynamic index moving average; step 4, using dual dynamic thresholds to realize self-adaptive thresholds to perform initial detection on R waves; step 5, correcting missing detection of a detection result through RR interval compensation and a backtracking detection strategy; and 6, eliminating false R peaks by setting amplitude, form and time interval conditions so as to enhance the detection accuracy, thereby realizing accurate positioning of the QRS wave group. According to the method, the QRS waves can be detected, and compared with a classic Hamilton-Tompkins structure, the sensitivity and the prediction rate of QRS detection can be improved, and the robustness is higher.

The present disclosure describes a system for cardiac assessment. Electrical activity from tissue of a patient is monitored (410) using a plurality of external electrodes to generate a plurality of electrical signals over time. The plurality of electrical signals is filtered (420) using a first filter having a first frequency range to generate a plurality of first filtered signals. The plurality of electrical signals is filtered (430) using a second filter having a second frequency range different from the first frequency range to generate a plurality of second filtered signals. At least one QRS complex is detected (440) based on the plurality of first filtered signals. A QRS peak of the at least one QRS complex is detected (450) based on the plurality of second filtered signals and the detected at least one QRS complex.

Computer-implemented method for estimating a respiratory rate of a subject, the method including acquiring a single-lead electrocardiographic signal from the subject, detecting R-peaks of the single-lead electrocardiographic signal, determining R-peak intervals from the R-peaks, extracting QRS complexes corresponding to the R-peaks based on the single-lead electrocardiographic signal, determining a time-domain sequence of root mean square amplitudes of the QRS complexes in a first window, the first window being in the time domain, generating, within a predetermined frequency range, a power spectrum of the sequence of root mean square amplitudes, determining a dominant frequency of the power spectrum, and estimating the respiratory rate of the subject based on the dominant frequency. Respirometry system employing same.

In one aspect, a computer-implemented method includes receiving a signal corresponding to electrical activity of a patient's heart; separating the signal into component signals; detecting fractional phase transitions for each of the component signals; generating, at each of the detected fractional phase transitions for each of the component signals, a data object containing a time value and an amplitude value; for a set of consecutive data objects associated with a first component signal of the component signals, detecting a peak amplitude; for a set of consecutive data objects associated with a second component signal of the component signals, detecting a peak amplitude; determining that the peak amplitudes satisfy a first time; calculating a consolidated peak amplitude and a consolidated peak time; and in response to determining that the consolidated peak amplitude satisfies both an amplitude criterion and a second time criterion, providing an indication of a detected heartbeat.

The application discloses a robust electrocardiogram denoising method based on autoregressive induced residual generation, and belongs to the field of electrocardiogram signal denoising. The method is characterized in that: discrete wavelet transform is performed on a collected noisy electrocardiogram signal segment and filtering is performed to obtain a proxy signal after suppressing part of the noise, and the proxy signal is input into a latent space autoregressive module; on this basis, the original noisy signal is subtracted from the output of the first stage to obtain a noisy residual signal, and the noisy residual signal is input into a conditional generation network based on flow matching after a Gaussian disturbance is injected, so that fine reconstruction of high-frequency details and key waveform forms is realized; finally, the generated target residual signal is fused with the semantic structure of the first stage to obtain a denoised electrocardiogram signal, so that the integrity of diagnostic elements such as P waves, QRS complexes and ST segments is maintained while effectively suppressing multi-source complex noise, and the generalization, interpretability of the electrocardiogram denoising result and the accuracy of downstream tasks are significantly improved.

The invention discloses a coronary microcirculation disturbance prediction method, a processing device, a system and a computer readable storage medium, and the method comprises the steps: respectively obtaining motion electrocardio data and resting electrocardio data output through at least one electrocardiogram lead, respectively obtaining first and second high-frequency QRS wave group data according to the motion electrocardio data and the resting electrocardio data, and obtaining first and second lead positive indexes based on the first and second high-frequency QRS wave group data; whether target first high-frequency QRS wave group data exists in the first high-frequency QRS wave group data corresponding to positive indication of the first lead positive index or not is determined, and the target first high-frequency QRS wave group data does not have a first waveform feature indicating coronary artery lesion and a second waveform feature indicating myocardial bridge; and determining the possibility of coronary microcirculation disturbance. According to the scheme, the coronary artery microcirculation disturbance can be noninvasively, rapidly and specifically recognized.