An Effective and Fast Model for Characterization of Cardiac Arrhythmia and Congestive Heart Failure.

Background/

Objectives

Cardiac arrhythmia (ARR) and congestive heart failure (CHF) are heart diseases that can cause dysfunction of other body organs and possibly death. This paper describes a fast and accurate detection system to distinguish between ARR and normal sinus (NS), and between CHF and NS.

Methods

the proposed automatic detection system uses the higher amplitude coefficients (HAC) of the discrete cosine transform (DCT) of the electrocardiogram (ECG) as discriminant features to distinguish ARR and CHF signals from NS. The approach is tested with three statistical classifiers, of which the k-nearest neighbors (k-NN) algorithm.

Results

the DCT provides fast compression of the ECG signal, and statistical tests show that the obtained HACs are different from ARR and NS, and for CHF and NS. The latter achieved highest accuracy under ten-fold cross-validation in comparison to Naïve Bayes (NB) and nonlinear support vector machine (SVM). The kNN yielded 97% accuracy, 99% sensitivity, 90% specificity and 0.63 s processing time when classifying ARR against NS, and it yielded 99% accuracy, 99.7% sensitivity, and 99.2% specificity, and 0.27 seconds processing time when classifying HCF against NS. In addition to a fast response, the DCT-kNN system yields higher accuracy in comparison to recent works.

Conclusions

it is concluded that using the DCT based HACs as biomarkers of ARR and CHF can lead an efficient computer aided diagnosis (CAD) system which is fast, accurate and does not require ECG signal pre-processing and segmentation. The proposed system is promising for applications in clinical milieu.