Deep learning-based lightweight algorithm for recognizing electrocardio data

Abstract

The invention provides a deep learning-based lightweight algorithm for recognizing electrocardio data. The algorithm comprises the following steps of: roughly extracting data features of extracted electrocardio data through a standard convolution layer; sending the roughly extracted data features into a core module Lite Module so as to extract deep data features after the roughly extracted data features pass through a pooling layer Max-Pooling; sequentially sending the deep data features into a two-layer full-connection layer dense to purify the deep data features after the deep data featurespass through a pooling layer Max-Pooling; and sending the purified data features into a classifier function to carry out feature classification and output. Compared with other algorithms, the algorithm does not need many calculation parameters while ensuring a certain recognition effect, and is capable of processing electrocardio data on limited network resources or operation memories.

Description

technical field [0001] The invention belongs to the field of machine learning algorithms, and in particular relates to a lightweight algorithm for recognizing electrocardiographic data based on deep learning. Background technique [0002] ECG is currently an extremely useful clinical diagnostic sign for cardiovascular system diseases. It reflects the electrical activity process of heart excitation, and has important reference value for the basic function and pathological research of the heart. The electrocardiogram can analyze and identify various arrhythmias; it can also reflect the degree and development process of myocardial damage and the functional structure of the atrium and ventricle. With the rise of machine learning and deep learning algorithms, many researchers have been working on computer-aided ECG analysis for many years. The research methods are mainly divided into traditional machine learning and deep learning. [0003] Traditional machine learning algorith...

AI Technical Summary

Problems solved by technology

Multi-lead data combined with multi-channel neural network algorithms such as MCNN can achieve good results, but the training parameters of the model will be too large and the training time will be too long, making it difficult to achieve real-time monitoring with existing mobile devices

Compared with single-lead data, for example, using the AlexNet structure can also achieve good recognition results, but the algorithm is too backward

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