Non-invasive discrimination between diabetic states (HBA1C<8% and HBA1C>10%) using photoplethysmography

Diabetes mellitus is a group of metabolic diseases associated with the production and/or reaction of insulin leading to hyperglycemia. Glycated hemoglobin (HbA1c) level is generally measured for hyperglycemia. The risk of developing complications depends on both the duration of diabetes and hyperglycemia. A trend of increasing arterial stiffness has been identified in type 2 diabetes. Photoplethysmographic (PPG) pulse wave provides a ‘window’ into the properties of small arteries whereas stiffening of these arteries will alter the PPG waveform. In this research, the potential of PPG in discriminating between type 2 diabetic patients at risk of having HbA1c level > 10% has been investigated. To this end, PPG signals recorded from diabetic patients with different levels of HbA1c (HbA1c level 10%) were acquired from the index finger of the right arm of 101 subjects (53 subjects with HbA1c level 10%) at a sampling rate of 275 Hz. The area under the curve of PPG (auc-PPG) was proposed in analyzing the PPG pulse contour. Results of t-test analysis show that auc-PPG is significantly larger in diabetic patients with HbA1c level 10% (p-value 10% (total 56 subjects) show that there is no significant difference in the mean value of auc-PPG between the first measurement and repeated measurement for both groups. Finally, a logistic regression model for estimating the risk of having HbA1c level > 10% among diabetic patients was estimated using data from 51 female diabetic patients. The model shows that the auc-PPG is an independent predictor for estimating the risk of having HbA1c level > 10% (p-value = 0.005) among female diabetic patients

Quantifying the Relationship of Bilateral Blood Flow in Glabrous Skin at Rest and During Sympathetic Perturbations

Sympathetic nervous system regulation of blood flow within glabrous skin occurs through control of vasoconstrictor tone, with vasodilation being a passive process. As bursts of sympathetic vasoconstrictor activity occur simultaneously at separate sites of the body, blood flow patterns should also be closely matched due to the direct connection between sympathetic nerves and peripheral microvessels. With sympathetic activity difficult and invasive to measure directly, the possibility of using blood conductance as an indirect measure seems promising. We investigated the relationship of bilateral blood conductance recordings of both middle fingers in ten (7M, 3F) healthy participants, while at rest and in response to perturbations known to elicit sympathetic activity. Cutaneous vascular conductance was measured from both middle fingers via laser Doppler flowmetry, while at rest in a thermoneutral room for 20 minutes and in response to 4 randomized sympathetic perturbations (2 breath holds and 2 cold stimuli) while centrally vasodilated via heating of the back. Correlation coefficients while at thermoneutral rest were high (0.80 ± 0.22) demonstrating a strong temporal relationship for blood conductance in both fingers. During the sympathetic perturbations, blood conductance in both fingers were more related during (0.93 ± 0.11) and post (0.87 ± 0.11) administration of the sympathetic perturbation than prior (0.67 ± 0.25) to the administration (p = 0.002). Taken together, these findings indicate that blood conductance patterns at separate sites of the body are significantly more related during vasoconstrictor activity and that blood conductance may have potential as a non-invasive measure of sympathetic activity

NONINVASIVE ASSESSMENT AND MODELING OF DIABETIC CARDIOVASCULAR AUTONOMIC NEUROPATHY

Noninvasive assessment of diabetic cardiovascular autonomic neuropathy (AN): Cardiac and vascular dysfunctions resulting from AN are complications of diabetes, often undiagnosed. Our objectives were to: 1) determine sympathetic and parasympathetic components of compromised blood pressure regulation in patients with polyneuropathy, and 2) rank noninvasive indexes for their sensitivity in diagnosing AN. Continuous 12-lead electrocardiography (ECG), blood pressure (BP), respiration, regional blood flow and bio-impedance were recorded from 12 able-bodied subjects (AB), 7 diabetics without (D0), 7 with possible (D1) and 8 with definite polyneuropathy (D2), during 10 minutes supine control, 30 minutes 70-degree head-up tilt and 5 minutes supine recovery. During the first 3 minutes of tilt, systolic BP decreased in D2 while increased in AB. Parasympathetic control of heart rate, baroreflex sensitivity, and baroreflex effectiveness and sympathetic control of heart rate and vasomotion were reduced in D2, compared with AB. Baroreflex effectiveness index was identified as the most sensitive index to discriminate diabetic AN. Four-dimensional multiscale modeling of ECG indexes of diabetic autonomic neuropathy: QT interval prolongation which predicts long-term mortality in diabetics with AN, is well known. The mechanism of QT interval prolongation is still unknown, but correlation of regional sympathetic denervation of the heart (revealed by cardiac imaging) with QT interval in 12-lead ECG has been proposed. The goal of this study is to 1) reproduce QT interval prolongation seen in diabetics, and 2) develop a computer model to link QT interval prolongation to regional cardiac sympathetic denervation at the cellular level. From the 12-lead ECG acquired in the study above, heart rate-corrected QT interval (QTc) was computed and a reduced ionic whole heart mathematical model was constructed. Twelve-lead ECG was produced as a forward solution from an equivalent cardiac source. Different patterns of regional denervation in cardiac images of diabetic patients guided the simulation of pathological changes. Minimum QTc interval of lateral leads tended to be longer in D2 than in AB. Prolonging action potential duration in the basal septal region in the model produced ECG and QT interval similar to that of D2 subjects, suggesting sympathetic denervation in this region in patients with definite neuropathy

Photoplethysmography for the evaluation of diabetic autonomic neuropathy

The aim of this study was to determine if photoplethysmography (PPG) could be used to analyse the foot microvascular changes caused by diabetic autonomic neuropathy. The digital PPG signals were collected from 37 healthy volunteers (Group I), 35 diabetic patients (Group II), and 38 diabetic patients with sensory neuropathy (Group III) and analysed using MAT LAB. Prominent spectral peaks with sidebands were obtained at both the high frequency (HF) and the low frequency (LF) end of the Fourier spectrum of these PPG signals. Previous studies of microcirculation have shown that both are sympathetically and parasympathetically mediated and hence are a good measure of the autonomic activity. In the HF analysis, the heart rate (HR) response from 13 participants in Group III was severely reduced and significantly different from the responses obtained from the other two groups. However the responses from remaining 25 participants had similar characteristics to those of Group II. Hence the HF analyses failed to both statistically and objectively differentiate between the diabetics with and without neuropathy. The spectral density for the frequency bandwidth of 3-20 cpm was significantly reduced in the neuropathic group, compared to the other two groups. A Statistically significant difference was observed in the spectral densities calculated from Group II and III, though no difference could be established between Groups I and III. The LF analysis of this bandwidth differentiated between Groups II and III with a sensitivity of 84% and specificity of 61%. Activities at the LF end of the spectrum mostly represent the sympathetic control as opposed to the HR variability that is mostly a measure of the parasympathetic control. These results suggest that sympathetic dysfunction possibly precedes parasympathetic dysfunction and that PPG can assess the changes in the skin microcirculation due to sympathetic damage with moderate success

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Multi-sensor Framework for Heart Rate and Blood Oxygen Saturation Monitoring of Human Body

Cardiovascular diseases have been the cause of death for millions of people. Some of these deaths could be avoided if there was a signi cant increase of diagnosis for the detection of such diseases. This diagnosis, in turn, could be realized with the increased availability of robust and low-cost medical diagnostic devices. Integrated technology sensors available on wearable devices have been commonly used to read physiological data in users (patients). Particularly the pulse oximetry sensors, o ers a unique, non-invasive method that can be used to detect the severity of such diseases. This evaluation of the physical condition of the patient for certain diseases is possible due to non-invasive measurement through photoplethysmography, which allows the extraction of heart rate and oxygen saturation in the blood. Since some diseases diagnoses require simultaneous monitoring of blood oxygen saturation values at various sites in the body, a project has been developed to perform such reading of physiological data. This thesis presents the development of a systems platform based on the use of multiple pulse oximetry sensors connected to an application developed for a mobile device though a wireless connection. The purpose of this platform is to provide an easy-to-read experience of health data that can be analyzed to diagnose cardiovascular disease symptoms, aiding in an early diagnosis. The complete structure as well as the aspects of the analysis and implementation of the systems related to the proposed architecture are described in this dissertation

Analysis and refinement of pulse rate variability

Heart rate variability (HRV), calculated from the cardiac intervals of electrocardiogram (ECG), is a promising marker of the cardiovascular system status and fitness. However, ECG signal is not always available and photoplethysmogram (PPG) is easier to obtain, and more widely used in clinical is running HRV analysis on pulse-to-pulse intervals of PPG signal, which is usually referred to as pulse rate variability (PRV). Thus, whether PRV can be used as a substitution of HRV is of substantial interest to researchers. In this thesis, two issues about PRV are discussed. The first issue is the selection of characteristic point, which determines the length and location of the pulse-to-pulse interval and will affect the agreement between PRV and HRV. Six characteristic points of PPG pulse are extracted and the agreement between HRV and corresponding PRV is calculated and compared, in two situations, subjects with cardiovascular diseases (CVD) and subjects without cardiovascular diseases (non-CVD). The result indicates that pulse peak is most suitable for CVD subjects, and 50% max amplitude point and 75% max amplitude point on pulse slope are most suitable for non-CVD subjects. The second issue studied in this thesis is the PRV refinement using arterial blood pressure (ABP) information. The relationship between systolic blood pressure extracted from ABP signal and pulse transit time (PTT) is modeled using linear kernel support vector regression (SVR) and RBF kernel SVR, respectively. Estimated PTT is used to adjust the location of PPG pulse-to-pulse intervals. PRV after adjustment is calculated, and its agreement to HRV is compared with the original PRV. For CVD subjects, the improvement to the agreement is limited, and only the agreement for variables representing long-term variability is improved. For non-CVD subjects, there is a relatively large improvement for approximately all variables after refinement and linear kernel outperforms RBF kernel in this situation