Non-Invasive Risk Stratification of Hypertension: A Systematic Comparison of Machine Learning Algorithms

Giovanna Sannino,Giuseppe De Pietro,Ivanoe De Falco

doi:10.3390/jsan9030034

Abstract

One of the most important physiological parameters of the cardiovascular circulatory system is Blood Pressure. Several diseases are related to long-term abnormal blood pressure, i.e., hypertension; therefore, the early detection and assessment of this condition are crucial. The identification of hypertension, and, even more the evaluation of its risk stratification, by using wearable monitoring devices are now more realistic thanks to the advancements in Internet of Things, the improvements of digital sensors that are becoming more and more miniaturized, and the development of new signal processing and machine learning algorithms. In this scenario, a suitable biomedical signal is represented by the PhotoPlethysmoGraphy (PPG) signal. It can be acquired by using a simple, cheap, and wearable device, and can be used to evaluate several aspects of the cardiovascular system, e.g., the detection of abnormal heart rate, respiration rate, blood pressure, oxygen saturation, and so on. In this paper, we take into account the Cuff-Less Blood Pressure Estimation Data Set that contains, among others, PPG signals coming from a set of subjects, as well as the Blood Pressure values of the latter that is the hypertension level. Our aim is to investigate whether or not machine learning methods applied to these PPG signals can provide better results for the non-invasive classification and evaluation of subjects’ hypertension levels. To this aim, we have availed ourselves of a wide set of machine learning algorithms, based on different learning mechanisms, and have compared their results in terms of the effectiveness of the classification obtained.

Highlights

Cardiovascular diseases (CVDs) have been increasing worldwide, and their related mortality rate has overtaken that of cancer, making CVDs the leading cause of death in humans
We have investigated whether or not machine learning methods applied to PhotoPlethysmography (PPG) signals can provide good results for the non-invasive classification and evaluation of subjects’ hypertension levels
The use of Machine Learning techniques to estimate blood pressure values through the PPG signal is well known in the scientific literature

Summary

Introduction

Cardiovascular diseases (CVDs) have been increasing worldwide, and their related mortality rate has overtaken that of cancer, making CVDs the leading cause of death in humans. The sphygmomanometer [3] is the most common instrument to measure BP, even if it is considered an invasive sensor. This technique of measurement has been widely recognized and popularized over the last century of development, and it has played a major role in the control of CVDs. This technique of measurement has been widely recognized and popularized over the last century of development, and it has played a major role in the control of CVDs This sensor is considered invasive because it requires a cuff and a pressure to the forearm when BP is measured. The measurement can be affected by the operation and use conditions, such as the operation of the cuff, and the sitting posture

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Conclusion