LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.
Physiol Rep
; 12(17): e16182, 2024 Sep.
Article
en En
| MEDLINE
| ID: mdl-39218586
ABSTRACT
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.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Arritmias Cardíacas
/
Redes Neurales de la Computación
/
Electrocardiografía
Límite:
Humans
Idioma:
En
Revista:
Physiol Rep
Año:
2024
Tipo del documento:
Article
País de afiliación:
Irán