A unified non-linear approach based on recurrence quantification analysis and approximate entropy: application to the classification of heart rate variability of age-stratified subjects.
Journal:
Medical & biological engineering & computing
Published Date:
Nov 3, 2018
Abstract
This paper presents a unified approach based on the recurrence quantification analysis (RQA) and approximate entropy (ApEn) for the classification of heart rate variability (HRV). In this paper, the optimum tolerance threshold (r) corresponding to ApEn has been used for RQA calculation. The experimental data length (N) of RR interval series (RR) is optimized by taking r as key parameter. r is found to be lying within the recommended range of 0.1 to 0.25 times the standard deviation of the RR, when N ≥ 300. Consequently, RQA is applied to the age stratified RR and indices such as percentage recurrence (%REC), percentage laminarity (%LAM), and percentage determinism (%DET) are calculated along with ApEn, [Formula: see text], [Formula: see text], and an index namely the radius differential (R). Certain standard HRV statistical indices such as mean RR, standard deviation of RR (or NN) interval (SDNN), and the square root of the mean squared differences of successive RR intervals (RMSSD) (Eur Hear J 17:354-381, 1996) are also found for comparison. It is observed that (i) R can discriminate between the elderly and young subjects with a value of 0.1151 ± 0.0236 (mean ± SD) and 0.0533 ± 0.0133 (mean ± SD) respectively for the elderly and young subjects and is found to be statistically significant with p < 0.05. (ii) Similar significant discrimination was obtained using [Formula: see text] with a value of 0.1827 ± 0.0382 (mean ± SD) and 0.2248 ± 0.0320 (mean ± SD) (iii) other significant indices were found to be %REC, %DET, %LAM, SDNN, and RMSSD; however, ApEn was found to be insignificant with p > 0.05. The above features of RR time series were tested for classification using support vector machine (SVM) and multilayer perceptron neural network (MLPNN). Higher classification accuracy was achieved using SVM with a maximum value of 99.71%. Graphical abstract.