Analysis of Photo Plethysmography (PPG) Signals with Artificial Neural Networks Using Curvelet Transform

Shaik Subhani

Photoplethysmography (PPG) is an abnormal optical method that calculates relational blood volume updates in the blood vessels. The cardiovascular parameters contain heart rate, blood pressure, blood oxygen saturation and respiration rate are assessed. In this study, we presented an algorithm for analyzing the Photo-plethysmography signals based curve let transform and artificial neural network (ANN).Curvelet transform is used for extracting the features from PPG signal and the optimal features are selected using ANN classifier. The Experimental results are carried using synthetic dataset and a comparison is made with different classifiers such as SVM, KNN, Naive Bayesian, Multilayer Perception, Random Forest Tree, Bootstrap and logistic models. The statistical results show that ANN classifier was effectively distinguishes between two classes. The correct classification was 99.5% for training dataset and 97.7% for test dataset.

Volume 11 | Issue 1

Pages: 363-370