Classification of Right Bundle Branch Block and Left Bundle Branch Block cardiac arrhythmias based on ECG analysis

Heart is a vital organ of the human body which plays an important role in the circulation of the blood throughout the body and also serves as the power source of the electrical impulses that generate the rhythmicity of the heart thus resulting in the successful circulation of the blood. Now any disturbance in the proper functioning of the heart results in some type of diseases termed as cardiovascular diseases or arrhythmias. These diseases can be diagnosed and consequently treated. The diagnosis is done by an efficient technique known as Electrocardiogram (ECG). This paper focuses on the area of biomedical signal analysis, where a method for detection of two types of cardiac arrhythmias namely Right Bundle Branch Block (RBBB) and Left Bundle Branch Block (LBBB) is discussed. The signal processing and analysis have been carried out on the data collected from MITBIH [1] database. Implementation is done on the MATLAB platform. The data collected from the MITBIH database are initially pre-processed and filtered by Savitzky–Golay filtering technique, smoothened and later subjected to feature extraction process where a number of features are extracted out and some computations are carried out. The results obtained from the feature extraction stage give us the idea of classifying the cardiac arrhythmias into the two types namely Right Bundle Branch Block (RBBB) and Left bundle Branch Block (LBBB). However the project is being extended to a classification stage where a Neural Network based classifier is proposed to be implemented in order to obtain better results.