R-peak detection for improved analysis in health informatics

Improvement in R-peak detection of electrocardiogram (ECG) signal is still not saturated even requires better pre-processing, feature extraction and detection stage. Proper detection of heart diseases using the proposed technique only leads to increase its applications in medical engineering for health informatics. R-peak detection is very important for detecting heart diseases, but involvement of various types of noises makes its detection too much complex. In this work, discrete wavelet transform (DWT) is used as pre-processing tool and Hilbert transform (HT) is used as a feature extraction tool for spectral estimation (in the form of trajectory pattern). Finally, principal component analysis (PCA) is adopted for reducing feature vectors. Detection of R-peaks are accomplished on the basis of variance obtained by first principal component (PC1). For validating this research work Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database has been used. The proposed technique was evaluated in MATLAB environment R2015a. The detection sensitivity (SE), positive predictivity (PP), F-score (F-s) and mean squared error (MSE) are estimated for evaluating the performance of the proposed technique. The proposed technique has resulted into SE of 99.88%, PP of 99.88%, F-s of 99.88%, SNR of 7.60 dB and MSE of 0.8131%.