Electrocardiosignal detection device and analysis method based on joint neural network

Abstract

The invention discloses an electrocardiosignal detection device and analysis method based on a joint neural network. The method comprises the following steps: firstly, building a joint neural networkalgorithm on a machine learning server, and training a model; aiming at preprocessed ECG data, enabling the model to extract data spatial features and acquire a spatial classification probability through a residual neural network module; extracting time sequence features of the data on a dimensionality-reduced spatial feature map through a bidirectional long-short-term memory neural network and anattention module, and acquiring a time sequence classification probability; finally, fusing the two classification probabilities to obtain a detection result; acquiring a small amount of ECG data ofa patient from a wearable device, performing manual marking, inputting the ECG data into the machine learning server, performing fine-tuning on the model, and deploying the final model to an intelligent mobile device; and finally, realizing real-time anomaly detection through wireless transmission of the wearable device and the intelligent mobile device. The invention develops the wearable devicefor electrocardiosignal acquisition and the real-time detection, and provides an effective technical means for auxiliary diagnosis of heart diseases.

Description

technical field [0001] The invention belongs to the field of abnormal detection of electrocardiographic signals, in particular to a wearable device for electrocardiographic signals based on a joint neural network algorithm and an abnormal detection and analysis method. Background technique [0002] The ECG signal is drawn by the electric current generated by the heart activity and transmitted to the body surface. It can accurately reflect the activity state of the heart and is one of the important means for doctors to diagnose heart disease. ECG signal is a weak physiological signal with small voltage value and time interval. Therefore, extracting ECG signal features and assisting detection by computer can improve the efficiency of doctors' diagnosis. In recent years, with the continuous development and improvement of artificial intelligence algorithms, algorithms commonly used for ECG signal detection and classification are mainly divided into traditional machine learning m...

AI Technical Summary

Problems solved by technology

Many studies only use one type of neural network when building models, resulting in the inability to make full use of the characteristics of ECG

And although some researchers use CNN and LSTM models at the same time, the superimposed structure will cause the neural network algorithm to allocate most of the error to the LSTM network that is closer to the classification layer when the neural network algorithm updates parameters through backpropagation, resulting in a shallow extraction space. The CNN network cannot be updated to the maximum extent

In addition, many researchers did not consider the issue of patient specificity when training and testing algorithms, that is, the training set and test set data they used may come from the same patient, which will lead to poor generalization ability of the final model. ECG data of new patients cannot be accurately detected

Images