A PCA-based technique for QRS complex estimation

In this paper, a new method for QRS complex prediction is presented. It is based on Principal Components Analysis (PCA) and polynomial fitting techniques. QRS complexes were extracted from multilead ECG signals and were aligned very perfectly. The covariance matrix was calculated from the QRS complex data matrix of many heartbeats. Afterwards, the corresponding eigenvectors and eigenvalues were computed and the reconstruction parameters vectors were derived by expansion of every beat in terms of the first eigenvectors. Performing the first order poly-fit method on the elements of the reconstruction parameter vectors yielded certain linear functions. Thereafter, the following QRS complexes were estimated by calculating the corresponding reconstruction parameter vectors derived from these functions. The similarity, absolute error and RMS error between the original and predicted QRS complexes were measured.