Key result
Machine learning algorithms applied to photoplethysmography signals were systematically compared for the non-invasive classification of hypertension levels.
Why the study?
Early detection and risk stratification of hypertension via wearable devices are crucial, but whether machine learning applied to photoplethysmography signals can improve non-invasive classification remains to be investigated.
Do machine learning algorithms applied to PPG signals accurately classify hypertension levels in subjects from the Cuff-Less Blood Pressure Estimation Data Set?
Do machine learning algorithms applied to PPG signals accurately classify hypertension levels in subjects from the Cuff-Less Blood Pressure Estimation Data Set?
This study investigates the use of various machine learning algorithms on PPG signals for non-invasive hypertension risk stratification.
Supports PPG-ML exploration for hypertension classification; leaves open prospective validation before clinical use.
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.
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Sannino et al. (2020) studied Hypertension. Machine learning algorithms applied to photoplethysmography (PPG) signals was evaluated on Classification and evaluation of subjects' hypertension levels. Machine learning algorithms applied to photoplethysmography signals were systematically compared for the non-invasive classification of hypertension levels.
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