Artificial Neural Network-Based ECG Signal Classification and the Cardiac Arrhythmia Identification

2021 ◽  
pp. 445-461
Author(s):  
M. Ramkumar ◽  
C. Ganesh Babu ◽  
G. S. Priyanka ◽  
B. Maruthi Shankar ◽  
S. Gokul Kumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cardiac Arrhythmia ◽  
Signal Classification ◽  
Ecg Signal ◽  
Artificial Neural

VHDL Modeling of EMG Signal Classification using Artificial Neural Network

2012 ◽  
Vol 12 (3) ◽  
pp. 244-253 ◽  
Author(s):  
M.R. Ahsan ◽  
M.I. Ibrahimy ◽  
O.O. Khalifa ◽  
M.H. Ullah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Signal Classification ◽  
Emg Signal ◽  
Artificial Neural

scholarly journals ECG QRS Enhancement Using Artificial Neural Network

2010 ◽  
pp. 170-175
Author(s):  
Sambita Dalal ◽  
Laxmikanta Sahoo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transmission Lines ◽  
Intelligent Systems ◽  
Side Lobe ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Long Distance ◽  
Potential Applications ◽  
Artificial Neural

Soft computing is a new approach to construct intelligent systems. The complex real world problems require intelligent systems that combine knowledge, techniques and methodologies from various sources. Neural networks recognize patterns and adapt themselves to cope with changing environments. Artificial neural network has potential applications in the field of ECG diagnosis measures. So noise reduced QRS complex of ECG signal is of utmost importance for automatic ECG interpretation and analysis. Noise is an unwanted energy, which interferes with the desired signal. Noise cancellation is mainly used as interference canceling in ECG, echo elimination on long distance telephone transmission lines and antenna side lobe interference canceling. In the study, the ECG signal is trained following various artificial neural network based algorithms to enhance the QRS complex by reducing noise for further analysis.

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