ECG Signal Recognition Based on Lookup Table and Neural Networks

Abstract

Electrocardiograph (ECG) signals are very important part in diagnosis healthcare the heart diseases. The implemented ECG signals recognition system consists hardware devices, software algorithm and network connection. An ECG is a non-invasive way to help diagnose many common heart problems. A health-care provider can use an ECG to recognize irregular heartbeats, blocked or narrowed arteries in the heart, whether you have ever had a heart attack, and the quality of certain heart disease treatments. The main part of the software algorithm including the recognition of ECG signals parameters such as P-QRST. Since the voltages at which handheld ECG equipment operate are shrinking, signal processing has become an important challenge. The implemented ECG signal recognition approach based on both lookup table and neural networks techniques. In this approach, the extracted ECG features are compared with the stored features to recognize the heart diseases of the received ECG features. The introduction of neural network technology added new benefits to the system implementing the learning and training process.