Advances in Photopletysmography Signal Analysis for Biomedical Applications

Abstract

Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost–benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique.

Photoplethysmography based atrial fibrillation detection: a review

“…Heart rate, blood pressure, and respiratory rate could be closely monitored for fitness or health assessment. 19 Advanced diagnostic applications of PPG were also envisaged. Cardiac function, arterial stiffness, autonomic nervous system (ANS) responses, and apnea are among conditions that could potentially be detected or evaluated using PPG.…”

EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

“…Formulae for measuring these terms are given below: [27]. More information is available in [28][29][30][31][32][33][34].…”

Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach

“…The PWV can be estimated from so called pulse transit time (PTT), which is the time difference between pulse leaving from the heart (aortic valve opening) and arrival of the pulse to a distal arterial site. The arrival of pulse to the distal location can be measured, for example, using noninvasive optical photoplethysmographic (PPG) sensor . The measurement of aortic valve opening is, however, much more challenging and accurate detection is only possible with clinical methods such as ultrasound or MRI.…”

“…The arrival of pulse to the distal location can be measured, for example, using noninvasive optical photoplethysmographic (PPG) sensor. 6 The measurement of aortic valve opening is, however, much more challenging and accurate detection is only possible with clinical methods such as ultrasound or MRI.…”