Automated Diagnosis of Cardiac Health

Electrocardiogram (ECG) is the P, QRS, T wave representing the electrical movement of the heart. The fine changes in amplitude and interval of ECG cannot be interpreted exactly by the naked eye, hence imposing the need for a computer assisted diagnosis tool. Automatically classified five types of ECG beats of MIT-BIH arrhythmia records. The five types of beats are Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). We have match up to the performances of three approaches. The first approach apply principle components of segmented ECG beats, the second approach apply principle components of error signals of linear prediction model, whereas the third approach apply principle components of Discrete Wavelet Transform (DWT) coefficients as features. These features from three approaches were independently classified by feed forward neural network (NN) and Least SquareSupport Vector Machine (LS-SVM). To attain the highest accuracy with the first approach using principle components of segmented ECG beats with average sensitivity of 99.90%, specificity of 99.10%, PPV of 99.61% and classification accuracy of 98.11%. The system built-up is clinically set to arrange for collection screening programs. KEYWORDS—Electrocardiogram, MIT-BIH arrhythmia database, Discrete Wavelet Transform (DWT), Neural network (NN), Support Vector Machine (SVM).