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A CNN Model for Cardiac Arrhythmias Classification Based on Individual ECG Signals by Yuan Zhang; Sen Liu; Zhihui He; Yuwei Zhang; Changming Wang is a Medicine article available to read on EtoBox.

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## Purpose: Wearable devices in the scenario of connected home healthcare integrated with artificial intelligence have been an effective alternative to the conventional medical devices. despite various benefits of wearable electrocardiogram (ecg) device, several deficiencies remain unsolved such as noise problem caused by user mobility. therefore, an insensitive and robust classification model for cardiac arrhythmias detection system needs to be devised. ## Methods: A one-dimensional seven-layer convolutional neural network (cnn) classification model with dedicated design of structure and parameters is developed to perform automatic feature extraction and classification based on large volume of original noisy signals. record-based ten-fold cross validation scheme is devised for evaluation to ensure the independence of the training set and test set, and further improve the robustness of our method. ## Results: The model can effectively detect cardiac arrhythmias, and can reduce the computational workload to a certain extent. our experimental results outperform most recent literature on the cardiac arrhythmias classification with diagnostic accuracy of 0.9874, sensitivity of 0.9811,

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Author

Yuan Zhang; Sen Liu; Zhihui He; Yuwei Zhang; Changming Wang

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Medicine (Health Sciences)