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. 2021 Oct 15:2021:2230383.
doi: 10.1155/2021/2230383. eCollection 2021.

Intracavitary Electrocardiogram Guidance Aids Excavation of Rhythm Abnormalities in Patients with Occult Heart Disease

Yanli Wei  1 , Ying Zhu  1 , Xin Wen  1 , Qing Rui  2 , Wei Hu  1
Affiliations

Intracavitary Electrocardiogram Guidance Aids Excavation of Rhythm Abnormalities in Patients with Occult Heart Disease

Yanli Wei et al. J Healthc Eng. .

Retraction in

Abstract

In this paper, the analysis of intracavitary electrocardiograms is used to guide the mining of abnormal cardiac rhythms in patients with hidden heart disease, and the algorithm is improved to address the data imbalance problem existing in the abnormal electrocardiogram signals, and a weight-based automatic classification algorithm for deep convolutional neural network electrocardiogram signals is proposed. By preprocessing the electrocardiogram data from the MIT-BIH arrhythmia database, the experimental dataset training algorithm model is obtained, and the algorithm model is migrated into the project. In terms of system design and implementation, by comparing the advantages and disadvantages of the electrocardiogram monitoring system platform, the overall design of the system was carried out in terms of functional and performance requirements according to the system realization goal, and a mobile platform system capable of classifying common abnormal electrocardiogram signals was developed. The system is capable of long-term monitoring and can invoke the automatic classification algorithm model of electrocardiogram signals for analysis. In this paper, the functional logic test and performance test were conducted on the main functional modules of the system. The test results show that the system can run stably and monitor electrocardiogram signals for a long time and can correctly call the deep convolutional neural network-based automatic electrocardiogram signal classification algorithm to analyze the electrocardiogram signals and achieve the requirements of displaying the electrocardiogram signal waveform, analyzing the heartbeat type, and calculating the average heart rate, which achieves the goal of real-time continuous monitoring and analysis of the electrocardiogram signals.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Intracavitary electrocardiogram guidance design framework.
Figure 2
Figure 2
Heartbeat interception diagram.
Figure 3
Figure 3
Evaluation process design.
Figure 4
Figure 4
Schematic diagram of a continuous segment of the electrocardiogram signal.
Figure 5
Figure 5
Simulation experiment results.
Figure 6
Figure 6
Five types of results.
Figure 7
Figure 7
Setting range of weights.
Figure 8
Figure 8
Experimental results of weight setting.
Figure 9
Figure 9
Comparison of the overall accuracy change of the network model before and after improvement.
Figure 10
Figure 10
Identification results.
See this image and copyright information in PMC

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