Expert diagnostic system for detection of hypertension and diabetes mellitus using discrete wavelet decomposition of photoplethysmogram signal and machine learning technique

Abstract

Hypertension is a leading risk factor for cardiovascular disease (CVD) and the overlap of diabetes mellitus (DM) with hypertension can lead to severe complications. There is a need for early diagnosis and risk stratification to implement an overall risk management strategy. Presently, the conventional method is not suitable for large-scale screening. The primary aim of this study is to develop an automated diagnostic system that uses Photoplethysmogram (PPG) signals for the non-invasive diagnosis of hypertension and DM-II. The proposed model uses a statistical feature extracted by decomposing the PPG signal up to level 11 into a sub-band using Discrete wavelet transform (DWT), and a variety of classifiers are used for the classification of hypertension and detection of DM-II patients. Three feature selection techniques used are Spearman correlation, ReliefF and Minimum Redundancy Maximum Relevance (mRMR) to select 20 top features out of 130 features using correlation with systole blood pressure (SBP), diastole blood pressure (DBP) values and D-II. The highest accuracy attained by the Adaptive neural fuzzy system (ANFIS) for classification categories such as normal (NT) vs prehypertension(PHT), NT vs. hypertension type 1 (HT-I), NT vs hypertension type 2 (HT-II) in terms of F1 score are 92.%, 98.5%, 98.3% (SBP) and 83.1%, 95.6%, 86.8% (DBP),respectively. The accuracy achieved by the adaptive-network-based fuzzy inference system (ANFIS) for the classification of normal (non-diabetic) vs. diabetic patients is 99.1%. The hybrid learning algorithm-based classifier achieved higher accuracy for hypertension risk stratification as compared to the hard computing classifier, which requires parameter tuning and DWT decomposition is robust to the noisy signal, overcoming the limitation of the morphological feature-based model.