Framework for Arrhythmia Identification using Real Time ECG Signals

Abnormal heartbeats or Cardiac arrhythmia denotes a dangerous condition of irregular electrical activity in the heart. Arrhythmia is the disease affecting 30 million people worldwide. There is need of introducing a method of real time Arrhythmia detection from low cost hardware. This article focuses on for the Arrhythmia identification using electrocardiogram (ECG) signals. The Electrocardiogram (ECG) signals are having small amplitude of about 1mv but carry a lot of noise. This paper is about a framework design to build, evaluate, and create a system that can record a patient's ECG in real time and measure the heartrate and irregular rhythm of the heartbeats. The framework further performs Arrhythmia detection. The process of acquiring the ECG is done using electrodes connected to PC/laptop having Matlab software. Filtering, describing, and evaluating the Electrocardiogram (ECG) data are further steps in the process of classifying heartbeats into Tachycardia, Bradycardia or Arrhythmias. RT interval, RR interval, and QRS complex are the features that were calculated. Tachycardia, Bradycardia, and the "normal state" are examples of various cardiac arrhythmias that cause changes in heart rate. The threshold is chosen in accordance with algorithm, and ECG signal peaks. The patient's anomaly is identified in accordance with the feature extraction algorithm and thresholding.