Wearable technology has increased in popularity since the advent of smartwatches and fitness trackers. We are more comfortable than ever with continuous data collection, especially as it pertains to our health. Among the different categories in the wearable technology market, pervasive health monitoring applications are ranked the fastest-growing segment due to the overwhelming need to monitor chronic diseases and aging populations (1). Currently, modern wearable devices are no longer only focused on simple fitness tracking measurements such as a daily step count, they also monitor important physiological measures such as Heart Rate Variability (HRV), glucose measures, blood pressure readings, and other health-related information.
The heart rate (HR) calculation has been one of the most valuable parameters (1). Currently, the Electrocardiogram (ECG) has been used as a dominant cardiac monitoring technique to detect irregularities in heart rhythms. As the conductive pathway of the heart depolarises in sequence, the cardiac muscle contracts, creating a heartbeat. The ECG records the heart’s electrical activity down the conduction pathway; it shows the variations in the amplitude of the ECG signal over time (Bolanos). Even though, since its advent, ECG technology has undergone continuous improvements in measurement accuracy and is still widely used (3, 4), it is still lacking in the domains of user flexibility, portability, and convenience. For the ECG to effectively measure the heart’s electrical activity, several bioelectrodes must be placed on certain spots on the body, which greatly limits the movement flexibility and mobility of the users (5).
However, photoplethysmography (PPG) technology is proving to be an accurate and effective heart rate monitoring technique. In fact, when a research group compared the HRV signals extracted from PPG and ECG signals, they concluded that the PPG signal offers an excellent potential to replace ECG recordings for the extraction of HRV signals, especially in monitoring healthy individuals. Therefore, to overcome the ECG limitations, an alternative solution based on PPG technology can be used (6). Photoplethysmography, or PPG, utilises infrared light to measure the volume variations in blood circulation, as an indicator of the cardiovascular system’s health (7). A device employing PPG technology will contain a light source and a photodetector, which can be positioned differently to measure different variables. In transmission mode, the light source and photodetector are separated by the tissue, so the detector only detects wavelengths of light that penetrate the tissue. In reflectance mode, the photodetector is positioned along the light source on the same side of the tissue to measure the reflected light (5). With either mode, the light detected is proportional to blood volume variations; as the tissue perfusion changes through the cardiac cycle, the photodetector measures the minute changes in light compared with the original light source and converts this information into heart rate, blood pressure and many other physiological parameters (8).
The PPG signal itself comprises pulsatile (AC) and superimposed (DC) components. The AC component is provided by the variations in blood volume that arise from heartbeats, while the DC component is shaped by several factors, including respiration, sympathetic nervous system activity, and temperature regulation (9). The AC component depicts the changes in blood volume occurring with phasic cardiac activity, known as the systolic and diastolic phases. The systolic phase (also called, “rise time”) begins with a valley and ends with the pulse wave systolic peak. The pulse wave end is marked by another valley at the end of the diastolic phase (10). The subsequent waveform graphed from this data and its features such as rise time, amplitude, and shape can predict vascular changes in the bloodstream (11, 12). This data can also be used to measure HRV or the variations between heartbeat time intervals. A myriad of factors can contribute to this variation, such as age, heart conditions, thermoregulation, and physical fitness (13). It is also possible to calculate the second derivative of the original PPG wave, called the acceleration photoplethysmogram (APG). This is more commonly used than the first derivative wave, as it’s used to calculate the acceleration of blood. There are a number of critical points that can be extracted from the second derivative wave of a PPG signal, as Figure 1 demonstrates (14). From the APG, the large artery stiffness index can be calculated (15), and this data is also correlated to numerous other metrics such as the distensibility of the carotid artery, age, blood pressure, risk of coronary heart disease, and the presence of the atherosclerotic disorders (16-18).
Figure 1: A) PPG signal B) PPG first derivative C) PPG second derivative (5).
Most commonly, PPG sensors use an infrared light emitting diode (IR-LED) or a green LED as the main light source. IR-LEDs are more sensitive for measuring the flow of blood that is more deeply concentrated in certain parts of the body such as the muscles, while green light is typically used for calculating the absorption of oxygen in oxyhemoglobin (oxygenated blood) and deoxyhemoglobin (blood without oxygen present) (19). Green LED light is the preferred and most commonly used light despite alternatives, as it penetrates more deeply into tissue and therefore provides more accurate measurements.
PPG technology can be utilised in many different devices, such as clips or cuffs, in many places on the body, such as fingers, wrists, forearms and torsos. The measurement site is determined by the desired application (20). In healthcare settings, PPG technology is employed in the form of a pulse oximeter, a device sitting over a patient’s finger to measure oxygen saturation. One limitation of PPG technology use in practice, however, is the susceptibility of PPG signals to motion artefacts caused by hand movement, which limits the accuracy of the data (5). The Canaria V device circumvents this issue by utilising PPG technology in a device that hooks over the ear and clips to the earlobe, an area far less prone to motion artefacts. The earlobe is also an ideal site for PPE measurement as it contains no cartilage and is therefore supplied by larger volumes of blood (5).
As mentioned previously, several personal and environmental factors can influence the accuracy of PPG recordings, such as body movement. It is expected that individual differences in anatomy will result in variable light passage through tissue; thicker earlobes or fingers will transmit or reflect less light (21). Movement and pressure on the device to the skin can alter the magnitude of the signal, both issues of which are minimised with the Canaria V device’s placement on the earlobe.
The popularity of PPG technology as an alternative physiological monitoring technique has recently increased, mainly due to the simplicity of its operation, the wearing comfortability for its users, and its cost-effectiveness. These factors are not available in traditional ECG-based biometric systems. Calculating the first and second derivatives of the PPG signal allows valuable health-related data about the patient’s heart and cardiovascular system to be gleaned. The Canaria V device employs an innovative use of PPG technology in its predictive biometric system to protect the safety of workers in a variety of industrial contexts.
Park S, Jayaraman S. Enhancing the Quality of Life Through Wearable Technology. IEEE Eng Med Biol Mag 2003;22(3):41–48.
Bolanos M, Nazeran H, Haltiwanger E, et al. Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals. Engineering in Medicine and Biology Society. 2006; 1:4289–4294.
Mc Laughlin NB, Campbell RW, Murray A. Accuracy of four automatic QT measurement techniques in cardiac patients and healthy subjects. Heart. 1996;76(5):422–6.
Postema PG, De Jong JS, Van der Bilt IA, et al. Accurate electrocardiographic assessment of the QT interval: Teach the tangent. Hear Rhythm, 2008;5(7): 1015–1018.
Castaneda, D., Esparza, A., Ghamari, M., Soltanpur, C., & Nazeran, H. (2018). A review on wearable photoplethysmography sensors and their potential future applications in health care. International journal of biosensors & bioelectronics, 4(4), 195–202. A review on wearable photoplethysmography sensors and their potential future applications in health care
Bolanos M, Nazeran H, Haltiwanger E, et al. Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals. Engineering in Medicine and Biology Society. 2006; 1:4289–4294.
Tamura T, Maeda Y, Sekine M, et al. Wearable photoplethysmographic sensors—past and present. Electronics. 2014;3:282–302.
Wang C, Li Z, Wei X. Monitoring heart and respiratory rates at radial artery based on PPG. Opt Int J Light Electron Opt 2013;124(4):3954–3956.
Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 2007;28(3):R1–R39.
Weissler AM, Harris WS, Schoenfeld CD. Systolic time intervals in heart failure in man. Circulation. 1968;37(2): 149–159.
Oliva I, Roztoĉil K. Toe pulse wave analysis in obliterating atherosclerosis. Angiology. 1983;34(9):610–619.
Allen J, Overbeck K, Nath AF, et al. A prospective comparison of bilateral photoplethysmography versus the ankle-brachial pressure index for detecting and quantifying lower limb peripheral arterial disease. J Vase Surg2008;47(4):794–802.
Lu G, Yang F, Taylor JA, et al. A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects. J Med Eng Technol. 2009;33(8):634–41.
Ghamari M, Soltanpur C, Cabrera S, et al. Design and prototyping of a wristband - type wireless photoplethysmographic device for heart rate variability signal analysis. Conf Proc IEEE Eng Med Biol Soc 2016;2016:4967–4970.
Ahn JM. New Aging Index Using Signal Features of Both Photoplethysmograms and Acceleration Plethysmograms. Healthc Inform Res 2017;23(1):53–59.
Takazawa K, Tanaka N, Fujita M, et al. Assessment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform. Hypertension. 1998;32(2):365–370.
Imanaga I, Hara H, Koyanagi S, et al. Correlation between wave components of the second derivative of Plethysmogram and arterial distensibility. Jpn Heart J 1998;39(6):775–784.
Bortolotto LA, Blacher J, Kondo T, et al. Assessment of vascular aging and atherosclerosis in hypertensive subjects: second derivative of photoplethysmogram versus pulse wave velocity. Am J Hypertens 2000;13(2): 165–171.
Kavsaoğlu AR, Polat K, Hariharan M. Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signal’s characteristics features. Appl Soft Comput2015;37:983–991.
Nilsson L, Johansson A, Kalman S, et al. Respiration can be monitored by Photoplethysmography with high sensitivity and specificity regardless of anaesthesia and ventilatory mode. Acta Anaesthesiol Scand2005;49(8): 1157–1162.
Sazonov E, Neuman MR. Wearable sensors: fundamentals, implementation and applications. San Diego: Academic Press, 2014.
Comments