38 results about "ECG feature" patented technology

The invention discloses a method which is used for the recognition and the classification of an ECG and a device thereof. The method and the device obtain a QRS wave group queue obtained by an ECG diagnostic instrument, thereby being capable of obtaining the starting point, the end point, the slope and the R wave position of each QRS wave, the amplitude and the direction of the Q wave, the R wave and the S wave, the interval of two adjacent R waves, the form model of the QRS wave and the incisure and the slurring of the QRS wave and so on. The invention also discloses a mode classification method which is used for the ECG diagnostic instrument and fully borrows ideas from the thought processes of experts. The invention is the method and the device which realize the effective characteristic recognition and the mode classification of the ECG by fewer operations.

Techniques are provided for detecting stroke within a patient using an implantable medical device in conjunction with an external confirmation system. In one example, a preliminary detection of stroke is performed by a subcutaneous monitor based on an analysis of features of an electrocardiogram (ECG) sensed within the patient. Exemplary ECG features indicative of possible stroke include the onset of prominent U-waves, the onset of notched T-waves, and changes in ST segment duration or QT duration or dynamic trends in these parameters. The monitor transmits a signal indicative of possible stroke to a bedside monitor or other external system, which generates a stroke questionnaire for use in confirming the stroke. Family members or other caregivers input answers to the questionnaire into the external system, which confirms or disconfirms the stroke. Emergency personnel can be automatically notified. Implantable systems that detect stroke based on intracardiac electrogram (IEGM) signals are also described herein.

The present invention provides a method and apparatus for determining the occurrence of apnoeas or hypopneas from ECG signal data alone. The method is carried out by apparatus configured to acquire ECG signals from a sleeping subject, transform the signals to data, and extract ECG features relevant to estimate breathing effort for the determination of respiratory events characteristic of apnoeas and hypopneas. The extracted ECG features are correlates of breathing efforts and are used as surrogate measures of breathing or respiratory events. The method may include calculating an AHI or apnoea / hypopnea index. The method may classify apnoeas into obstructive or central apnoeas.

The invention provides an identification method, which performs identification by using an electrocardio (ECG) signal. The method comprises the following steps of: (a) ECG signal collection; (b) ECG signal pre-treatment, wherein the collected ECG signal is filtered; (c) characteristic extraction, wherein the characteristics of the ECG signal are extracted to build an identification characteristicvectors and the built identification characteristic vectors comprise analysis characteristics, presentative characteristics, transformation-domain characteristics and fusion characteristics; (d) identification process, wherein the identification characteristic vectors of a person to be identified is compared with the identification characteristic vectors which are pre-stored in an ECG characteristic template base; and (e) result output. The invention also provides an identification system using the identification method.

The invention discloses a multimodal-based college student emotional pressure detection system and method. The method comprises the following steps: S01, artificially exciting a subject to be in an emotional excitation state, carrying out an emotional pressure test experiment, and respectively obtaining EEG, ECG and GSR data of the subject in a basic emotional pressure state and a pressure excitation state; s02, performing preprocessing and feature extraction on the EEG, ECG and GSR data collected in the step S01; s03, inputting the EEG features, the ECG features and the GSR features obtained in the step S02 into a pre-trained BP neural network for classification to obtain a multi-modal-based tested basic emotional pressure level and an excitation pressure level; and S04, inputting the basic emotion pressure level, the excitation pressure level and the pressure change difference value in the step S03 into a pre-trained sensor for classification, and judging whether the subject is in a high emotion pressure state or not. According to the invention, the emotion pressure change condition of the subject in a pressure environment can be accurately recognized, and the method plays an important role in recognizing high-risk emotion college students.

A method for analyzing an electrocardiography (ECG) signal is provided. The ECG signal is measured through a sensor. One or more specified waveforms in the ECG signal are detected. Multiple ECG features from each specified waveform are captured. The ECG features are input into multiple candidate models to obtain multiple candidate scores. Each candidate score is compared with a standard value to obtain an ideal score among the candidate scores. One of the candidate models corresponding to the ideal score is selected as a risk prediction model to predict disease risk through the risk prediction model.

A diagnostic electrocardiogram system employing an electrode lead system (40) for generating one or more electrode signals indicative of electrical activity of a subject heart (10). The diagnostic electrocardiogram system further employs a diagnostic electrocardiograph (50) coupled to the electrode lead system (40) for communicating (e.g., listing, displaying and / or printing) a subject electrocardiogram (20) and one or more diagnostic electrocardiograms (30) designated as a morphology match to the subject electrocardiogram (20). The subject electrocardiogram (20) includes one or more interpretations of ECG features derived from the electrical activity of the subject heart (10) as indicated by the electrode signal(s) (e.g., an algorithmic interpretation and / or an electrocardiographer interpretation of the subject electrocardiogram (20)). The diagnostic electrocardiogram(s) includes one or more diagnoses of ECG features derived from recorded electrical activity of diagnosed heart(s) (11)(e.g., an algorithmic diagnosis and / or an electrocardiographer diagnosis of the diagnostic electrocardiograms(s) (30)).

The present invention discloses at least two electrodes disposed at the body of the ECG measuring device of the present invention. When a user normally uses the ECG measuring device, the two electrodes are just adhered to the hands and the face of the user. The ECG measuring method of the present invention is to measure a first ECG signal from the left hand to the left side of face of the user and a second ECG signal from the right hand to the right side of face of the user. Then a plurality of ECG features are obtained from the first ECG signal, the second ECG signal and the result, as related coefficients, of the interactive computing, as a method of plus and subtract, of the first ECG signal and the second ECG signal. Hence, a biometric authorization process is engaged according to the plurality of ECG features.

The technical solution of the present invention includes a method for extracting electrocardiographic features based on PCANet, which is characterized in that it includes the following steps: S10, performing preprocessing on the electrocardiogram to obtain a training set and a set to be classified, S20, using PCANet to separate the training set and the set to be classified The classification set carries out the feature extraction of the heart beat, S30, train the classifier using the heart beat feature extracted from the training set and use it for the classification of the heart beat feature of the set to be classified; the beneficial effect of the present invention is: it is robust to the noise of the electrocardiogram signal, Simplify the steps of noise removal, have a better classification effect on unbalanced heart beats, improve the efficiency and accuracy of ECG feature extraction, reduce the pressure on doctors to identify ECGs, and reduce the probability of doctors' misdiagnosis.

The present invention proposes an electrocardiogram feature recognition method that integrates domain knowledge and pattern recognition. First, pre-process the pre-processed ECG signal, and then use pattern recognition to separate the pre-processed signal into P wave, T wave or The characteristic points of the QRS wave, and then post-processing the results of pattern recognition, the purpose is to find the approximate range of the P wave, T wave or QRS wave, and finally use domain knowledge to determine the range of the P wave, T wave or QRS wave The start and end points and peak points of real P wave, T wave or QRS wave. It can effectively improve the feature extraction accuracy of P wave, T wave or QRS wave. The invention additionally provides an electrocardiogram feature recognition system.

The invention discloses a pre-diabetes detection system and method based on the combination of electrocardiogram and electroencephalogram information. The system includes: a signal acquisition module, which uses a wearable device to obtain the user's ECG signal and EEG signal synchronously in a non-invasive manner; Dimensionality reduction processing is performed on multiple dimensionality reduction sets to obtain multiple dimensionality reduction combination feature sets, and by analyzing the correlation between multiple dimensionality reduction combination feature sets and blood glucose concentration values, ECG features and EEG features that meet the set correlation criteria are selected. , to form an optimized combined feature set; the multi-modal fusion module is used to input the optimized combined feature set to the trained neural network models of various types, and obtain the user's diabetes by fusing the output results of various types of neural networks. Preliminary test results. The invention can provide a non-invasive, painless, convenient, comfortable and low-cost pre-diabetes detection solution.

The electrocardiogram feature selection method provided by the present invention divides the electrocardiogram into four types of NSVF classification systems, and decomposes the four types of NSVF classification systems into six binary classifiers of NvS, NvV, NvF, SvV, SvF, and VvF. In the classifier, each feature is sorted according to the score to form a candidate feature set, and then the optimal feature subset is selected from each binary classifier, and the ECG sample to be tested is predicted based on the optimal feature subset to obtain the ECG to be tested kind of category. The electrocardiogram feature selection method provided by the present invention forms a feature subset after sorting the feature scores from high to low, and selects the optimal feature subset from each binary classifier, and uses the optimal feature subset to predict the ECG sample to be tested, The category of the electrocardiogram to be tested is obtained, and the prediction accuracy is improved.

The invention discloses an electrocardiosignal (ECG) data analysis method suitable for a mobile platform. The electrocardiosignal (ECG) data analysis method includes subjecting collected electrocardiosignal data to original ECG electrocardiosignal median filtering; detecting and positioning an R wave through a self-adaption dual-threshold algorithm; detecting two forward waves before and after the R wave, wherein the two forward waves are a P wave and a T wave; detecting two negative waves on two sides of the R wave, wherein the two negative waves are a Q wave and an S wave; using starting point and terminal point positions of the R wave, the P wave, the T wave, the Q wave, and the S wave to calculate ECG feature values of wave point intervals for arrhythmia diagnosis. According to the ECG data analysis method suitable for the mobile platform, the accuracy for detection of arrhythmia can reach over 92%; since an algorithm structure is concise, processing efficiency is high, only 12s is required for processing one-minute ECG data on a Cortex A8 / Android mobile platform, and the method is suitable for developing a mobile platform based portable admeasuring apparatus.

A device, system, and method is provided for detecting pain in a cardiac-related region of the body and determining whether that pain is cardiac or non-cardiac. The device, system, and method may include calculating or determining a first feature based on a variation in activity level and a variation in the detected heartrate measurement and a second feature based on a variation in the detected ECG features and a first feature and then subjecting at least the first feature and the second feature to a cardiac pain classifier to determine a cardiac classification.

An ECG signal processing method and device (700), used to solve the problem that large interference is introduced in the single-arm measurement process of single-lead ECG acquisition, which seriously affects the accurate calculation of heart rate and the accurate analysis of heart rhythm. The method includes: measuring equipment collects ECG signals (100); extracts the kth effective QRS complex of the ECG signal (110), calculates the kth time difference and the (k+1)th time difference (120), and judges the kth Whether the time difference and the (k+1)th time difference is the target time difference (130a, 130b). Therefore, the accuracy of ECG feature extraction is improved, the interference introduced in the single-arm measurement process is greatly eliminated, and the accuracy of heart rate calculation and heart rhythm analysis can be effectively guaranteed.

The invention discloses a fatigue detection method based on electroencephalogram and electrocardiogram in which the steering wheel is embedded with an electrocardiogram sensor. Fatigue detection is performed by constructing a product fuzzy convolution network, which specifically includes: S1. Obtaining electrocardiogram data through an electrocardiogram detection chip, and using The electroencephalograph obtains EEG time series data; S2, uses a fuzzy neural network with multilayer feedback to process EEG time series data, and obtains EEG features; S3, builds a deep network based on a one-dimensional convolutional neural network framework The feature extraction network extracts the fatigue features of the ECG data and generates the ECG feature sequence; S4. Design the fusion network, input the ECG feature sequence and the EEG feature at the same time, fuse the two signals together, and give the predicted value; S5 , Use the adaptive moment estimation algorithm to optimize and train the network model. It can reduce noise and improve detection accuracy. The introduction of multilayer reduces the limitation of fuzzy neural network on the feature dimension of input data and improves the accuracy of classification results.

The invention provides an ECG (electrocardiograph) feature extraction method integrating Butterworth filtering and wavelet transformation, and belongs to the technical field of medical computers. A core concept is described as follows: performing de-noising processing on power frequency interference of an original ECG signal; meanwhile, removing baseline drift in a curve fitting manner; respectively positioning a wave Q, a wave R and a wave S with a differential threshold method and a wavelet transformation method after a relatively accurate ECG signal is acquired, comparing the waves Q, R andS with information marked by an expert, and respectively regulating threshold, scale and translation amount parameters to obtain an optimal positioning result. Based on the result, the wave Q, the wave R and the wave S are positioned respectively by adopting the differential threshold method or the wavelet transformation method. According to the extraction method provided by the invention, the more accurate de-noising processing can be performed on the original ECG signal; in addition, the waves Q, R and S can be positioned accurately, therefore, a foundation is laid for a digital analysis onan ECG.

The invention relates to an electrocardiographic diagnosis and monitoring method and system for removing motion artifact interference and detecting electrocardiographic features. Motion artifacts in ECG signals. Afterwards, the filtered ECG signal is decomposed by lifting wavelet, and the wavelet at different levels is subjected to threshold denoising and moving average filtering to remove noise such as baseline drift and power frequency interference. According to the filtered wavelet coefficients and the ECG signal after median filtering, the zero-crossing detection method and the extreme point detection method are used to detect the R-wave position respectively, and the correct rate of R-wave detection is improved. Finally, the wavelet coefficients are reconstructed to obtain the filtered ECG signal. The invention can not only effectively remove various noises contained in electrocardiographic signals, but also effectively extract electrocardiographic features. And the calculation amount is small, and it is easy to implement on hardware.