The invention discloses an electrocardiosignal compression and recognition method based on singular value decomposition. The method comprises the following steps: S1, acquiring a position of an R wavein an ECG signal through R wave detection by using the data of an MIT-BIH database, carrying out heart beat interception on the ECG signal by taking the detected R wave as a reference point and taking first 100 sample points and last 150 sample points as heart beats so as to obtain experimental data, establishing an SVM model through a feature extraction and numerical standardization method, anddirectly using the experimental data to establish a CNN model; and S2, for data of the MIT-BIH database, acquiring position of the R wave in the ECG signal through R wave detection, enabling R-R sections of the ECG signal to be equal in length through period standardization, carrying out SVD decomposition and reconstruction again to obtain a compression signal, acquiring compression test data through the R-wave detection and heart beat interception, and applying the compression test data to the established SVM model and the CNN model for accuracy testing.

Electrocardiosignal compression and recognition method based on singular value decomposition

The invention discloses an automatic electrocardiogram classification method, system and device based on deep learning. The method comprises the following steps: dividing acquired labeled original electrocardiogram data into a training set and a verification set according to a preset proportion; constructing a convolutional neural network with residual connection, and substituting the convolutional neural network into the training set and the verification set for training and verification to obtain a trained convolutional neural network; evaluating the trained convolutional neural network by using the labeled test set, and acquiring an automatic electrocardiogram classification model passing the test in combination with evaluation indexes; and inputting an electrocardiogram to be tested into the automatic electrocardiogram classification model to obtain an electrocardiogram classification result. The automatic electrocardiogram classification method based on deep learning can carry outcomprehensive feature extraction, complete judgment task of multi-label classification, comprehensively extract information in the electrocardiogram, and complete classification.

Automatic electrocardiogram classification method, system and device based on deep learning

The invention discloses a risk stratification method for myocardial ischemia based on deterministic learning and deep learning. The method includes the steps that conventional 12-lead electrocardiogram signals are collected, based on the deterministic learning theory, neural network modeling and identification are conducted on intrinsic electrocardiodynamic characteristics of the shallow electrocardiogram signals, and the intrinsic dynamic characteristics of ECG signals are obtained; the convolutional neural network under the framework of deep learning is used for achieving the risk stratification of myocardial ischemia. The method combines the deterministic learning dynamic modeling method and the deep learning classification method for the first time, the method is applied to early riskstratification of myocardial ischemia based on the conventional 12-lead electrocardiogram signals, no additional detection equipment is needed, and the method is easy and convenient to use and easy tooperate. Through the deterministic learning method, the dynamic characteristics more sensitive to the ischemic state are extracted, the deep neural network can learn data features independently without further data characterization, and the complexity of the system is reduced.

Risk stratification method for myocardial ischemia based on deterministic learning and deep learning

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Risk stratification method for myocardial ischemia based on deterministic learning and deep learning