LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifyin...

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Bibliographic Details
Published in:Physiological reports 2024-09, Vol.12 (17), p.e16182-n/a
Main Authors: Bayani, Ali, Kargar, Masoud
Format: Article
Language:English
Subjects:
Algorithms
Arrhythmia
arrhythmia detection
Arrhythmias, Cardiac - diagnosis
Arrhythmias, Cardiac - physiopathology
Cardiac muscle
Cardiovascular diseases
cardiovascular health
Classification
convolutional neural network
Coronary artery disease
Deep Learning
EKG
electrocardiogram
Electrocardiography - methods
Heart diseases
Humans
Muscle contraction
Neural networks
Neural Networks, Computer
Original
Regular Manuscript
Signal processing
Signal Processing, Computer-Assisted
Support vector machines
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Summary:The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one‐dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state‐of‐the‐art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT‐BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT‐BIH Arrhythmia dataset.
ISSN:2051-817X
2051-817X
DOI:10.14814/phy2.16182