Novel Photoplethysmographic and Electrocardiographic Features for Enhanced Detection of Hypertensive Individuals

Hypertension is a major risk factor for many cardiovascular diseases, which are the leading cause of death worldwide. Regular monitoring is essential to provide early diagnosis since most patients with elevated blood pressure (BP) have asymptomatic hypertension. This work presents a method for BP classification using simultaneous electrocardiographic (ECG), photoplethysmographic (PPG) and BP signals. 86 recordings were used, being 35 normotensive, 26 prehypertensive and 25 hypertensive. It has been proposed 23 novel features to improve the discrimination between healthy and hypertensive individuals based on pulse arrival times (PAT) and morphological features from PPG, VPG and APG signal. Moreover, alternative classification models as Support Vector Machines (SVM), Naive Bayes or Coarse Trees were trained with the defined features to compare the classification performance. The classifier that provided the highest results comparing normotensive with prehypertensive and hypertensive subjects were Coarse Tree, providing an F1 score of 85.44% (Se of 86.27% and Sp of 77.14%). The use of new PPG and ECG features has successfully improved the discrimination between healthy and hypertensive individuals, around 7% of F1 score, so this machine learning methodology would be of high interest to detect HT introducing these techniques in wearable devices.