Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation

• Atrial fibrillation and atrial flutter have life threatening complications.
• The time domain features do not provide good discrimination.
• Discrete wavelet and cosine transforms with dimensionality reduction methods used.
• DCT coupled with ICA and KNN give 99.45% of accuracy using 10-fold cross validation.
• The performance is comparable with literature and is a methodological advancement.

Electrocardiogram (ECG) is a P-QRS-T wave, representing the depolarization and repolarization mechanism of the heart. Among different cardiac abnormalities, the atrial fibrillation (AF) and atrial flutter (AFL) are frequently encountered medical emergencies with life threatening complications. The clinical features of ECG, the amplitude and intervals of different peaks depict the functioning of the heart. The changes in the morphological features during various pathological conditions help the physician to diagnose the abnormality. These changes, however, are very subtle and difficult to correlate with the abnormalities and demand a lot of clinical acumen. Hence a computer aided diagnosis (CAD) tool can help physicians significantly. In this paper, a general methodology is presented for automatic detection of the normal, AF and AFL beats of ECG. Four different methods are investigated for feature extraction: (1) the principal components (PCs) of discrete wavelet transform (DWT) coefficients, (2) the independent components (ICs) of DWT coefficients, (3) the PCs of discrete cosine transform (DCT) coefficients, and (4) the ICs of DCT coefficients. Three different classification techniques are explored: (1) K-nearest neighbor (KNN), (2) decision tree (DT), and (3) artificial neural network (ANN). The methodology is tested using data from MIT BIH arrhythmia and atrial fibrillation databases. DCT coupled with ICA and KNN yielded the highest average sensitivity of 99.61%, average specificity of 100%, and classification accuracy of 99.45% using ten fold cross validation. Thus, the proposed automated diagnosis system provides high reliability to be used by clinicians. The method can be extended for detection of other abnormalities of heart and to other physiological signals.