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Open AccessArticle

An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology

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School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
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School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
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Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin 541004, China
*
Authors to whom correspondence should be addressed.
Electronics 2020, 9(1), 121; https://doi.org/10.3390/electronics9010121
Received: 9 December 2019 / Revised: 30 December 2019 / Accepted: 4 January 2020 / Published: 8 January 2020
(This article belongs to the Section Bioelectronics)
Electrocardiogram (ECG) signal evaluation is routinely used in clinics as a significant diagnostic method for detecting arrhythmia. However, it is very labor intensive to externally evaluate ECG signals, due to their small amplitude. Using automated detection and classification methods in the clinic can assist doctors in making accurate and expeditious diagnoses of diseases. In this study, we developed a classification method for arrhythmia based on the combination of a convolutional neural network and long short-term memory, which was then used to diagnose eight ECG signals, including a normal sinus rhythm. The ECG data of the experiment were derived from the MIT-BIH arrhythmia database. The experimental method mainly consisted of two parts. The input data of the model were two-dimensional grayscale images converted from one-dimensional signals, and detection and classification of the input data was carried out using the combined model. The advantage of this method is that it does not require performing feature extraction or noise filtering on the ECG signal. The experimental results showed that the implemented method demonstrated high classification performance in terms of accuracy, specificity, and sensitivity equal to 99.01%, 99.57%, and 97.67%, respectively. Our proposed model can assist doctors in accurately detecting arrhythmia during routine ECG screening. View Full-Text
Keywords: electrocardiogram; arrhythmia; automation; convolutional neural network; long short-term memory electrocardiogram; arrhythmia; automation; convolutional neural network; long short-term memory
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MDPI and ACS Style

Zheng, Z.; Chen, Z.; Hu, F.; Zhu, J.; Tang, Q.; Liang, Y. An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology. Electronics 2020, 9, 121.

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