A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning

Author:

Ma Linjuan1,Zhang Fuquan2

Affiliation:

1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China

2. College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China

Abstract

In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in our work with a multi-branch structure during training can capture a wide range of features to ensure efficient inference and rich feature extraction. The attention mechanism module utilized in our proposed network can dynamically focus on the most relevant spatial and channel-wise features to improve detection accuracy and computational efficiency. Then, the extracted features can be refined for efficient spatial feature processing and robust feature fusion. Several sets of experiments have been carried out to test the validity of the proposed Mamba-RAYOLO and these indicate that our method has made significant improvements in the detection and classification of ECG images. The research offers a promising framework for more accurate and efficient medical ECG diagnostics.

Funder

Beijing Institute of Technology Project by the Cancer Institute and Hospital, Chinese Academy of Medical

Digital Media Art, Key Laboratory of Sichuan Province, Sichuan Conservatory of Music

Minjiang College 2021 school-level scientific research project funding

the first batch of industry–university cooperation collaborative education project funded by the Ministry of Education of the People’s Republic of China, 2021

Publisher

MDPI AG

Reference32 articles.

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