Assessment of Subtle Changes in Diabetes-Associated Arteriosclerosis using Photoplethysmographic Pulse Wave from Index Finger

Predicting Arterial Stiffness From Single-Channel Photoplethysmography Signal: A Feature Interaction-Based Approach

Arterial stiffness (AS) serves as a crucial indicator of arterial elasticity and function, typically requiring expensive equipment for detection. Given the strong correlation between AS and various photoplethysmography (PPG) features, PPG emerges as a convenient method for assessing AS. However, the limitations of independent PPG features hinder detection accuracy. This study introduces a feature selection method leveraging the interactive relationships between features to enhance the accuracy of predicting AS from a single-channel PPG signal. Initially, an adaptive signal interception method was employed to capture high-quality signal fragments from PPG sequences. 58 PPG features, deemed to have potential contributions to AS estimation, were extracted and analyzed. Subsequently, the interaction factor (IF) was introduced to redefine the interaction and redundancy between features. A feature selection algorithm (IFFS) based on the IF was then proposed, resulting in a combination of interactive features. Finally, the Xgboost model is utilized to estimate AS from the selected features set. The proposed approach is evaluated on datasets of 268 male and 124 female subjects, respectively. The results of AS estimation indicate that IFFS yields interacting features from numerous sources, rejects redundant ones, and enhances the association. The interaction features combined with the Xgboost model resulted in an MAE of 122.42 and 142.12 cm/sec, an SDE of 88.16 and 102.56 cm/sec, and a PCC of 0.88 and 0.85 for the male and female groups, respectively. The findings of this study suggest that the stated method improves the accuracy of predicting AS from single-channel PPG, which can be used as a non-invasive and cost-effective screening tool for atherosclerosis.

Non-invasive detection of early microvascular changes in juveniles with type 1 diabetes

AIMS/HYPOTHESIS

The study aimed to assess the usefulness of capillaroscopy and photoplethysmography in the search for early vascular anomalies in children with type 1 diabetes.

METHODS

One hundred sixty children and adolescents aged 6-18, 125 patients with type 1 diabetes, and 35 healthy volunteers were enrolled in the study. We performed a detailed clinical evaluation, anthropometric measurements, nailfold capillaroscopy, and photoplethysmography.

RESULTS

Patients with diabetes had more often abnormal morphology in capillaroscopy (68.60%, p = 0.019), enlarged capillaries (32.6%, p = 0.006), and more often more over five meandering capillaries (20.90%, p = 0.026) compared to healthy controls. Meandering capillaries correlated with higher parameters of nutritional status. In a photoplethysmography, patients with diagnosed neuropathy had a higher percentage of flow disturbance curves (p < 0.001) with a reduced frequency of normal curves (p = 0.050).

CONCLUSIONS

Capillaroscopic and photoplethysmographic examinations are non-invasive, painless, fast, and inexpensive. They are devoid of side effects, and there are no limitations in the frequency of their use and repetition. The usefulness of capillaroscopy and photoplethysmography in the study of microcirculation in diabetic patients indicates the vast application possibilities of these methods in clinical practice.

Biomimetic Flexible Sensors and Their Applications in Human Health Detection

Bionic flexible sensors are a new type of biosensor with high sensitivity, selectivity, stability, and reliability to achieve detection in complex natural and physiological environments. They provide efficient, energy-saving and convenient applications in medical monitoring and diagnosis, environmental monitoring, and detection and identification. Combining sensor devices with flexible substrates to imitate flexible structures in living organisms, thus enabling the detection of various physiological signals, has become a hot topic of interest. In the field of human health detection, the application of bionic flexible sensors is flourishing and will evolve into patient-centric diagnosis and treatment in the future of healthcare. In this review, we provide an up-to-date overview of bionic flexible devices for human health detection applications and a comprehensive summary of the research progress and potential of flexible sensors. First, we evaluate the working mechanisms of different classes of bionic flexible sensors, describing the selection and fabrication of bionic flexible materials and their excellent electrochemical properties; then, we introduce some interesting applications for monitoring physical, electrophysiological, chemical, and biological signals according to more segmented health fields (e.g., medical diagnosis, rehabilitation assistance, and sports monitoring). We conclude with a summary of the advantages of current results and the challenges and possible future developments.

Calculation of an Improved Stiffness Index Using Decomposed Radial Pulse and Digital Volume Pulse Signals

The stiffness index (SI) is used to estimate cardiovascular risk in humans. In this study, we developed a refined SI for determining arterial stiffness based on the decomposed radial pulse and digital volume pulse (DVP) waveforms. In total, 40 mature asymptomatic subjects (20 male and 20 female, 42 to 76 years of age) and 40 subjects with type 2 diabetes mellitus (T2DM) (23 male and 17 female, 35 to 78 years of age) were enrolled in this study. We measured subjects' radial pulse at the wrist and their DVP at the fingertip, and then implemented ensemble empirical mode decomposition (EEMD) to derive the orthogonal intrinsic mode functions (IMFs). An improved SI (SI_new) was calculated by dividing the body height by the mean transit time between the first IMF5 peak and the IMF6 trough. Another traditional index, pulse wave velocity (PWV_finger), was also included for comparison. For the PWV_finger index, the subjects with T2DM presented significantly higher SI_new values measured according to the radial pulse (SI_new-RP) and DVP signals (SI_new-DVP). Using a one-way analysis of variance, we found no statistically significant difference between SI_new-RP and PWV_finger when applied to the same test subjects. Binary logistic regression analysis showed that a high SI_new-RP value was the most significant risk factor for developing T2DM (SI_new-RP odds ratio 3.17, 95% CI 1.53-6.57; SI_new-DVP odds ratio 2.85, 95% CI 1.27-6.40). Our refined stiffness index could provide significant information regarding the decomposed radial pulse and digital volume pulse signals in assessments of arterial stiffness.

Kirigami-Inspired Pressure Sensors for Wearable Dynamic Cardiovascular Monitoring

Continuously and accurately monitoring pulse-wave signals is critical to prevent and diagnose cardiovascular diseases. However, existing wearable pulse sensors are vulnerable to motion artifacts due to the lack of proper adhesion and conformal interface with human skin during body movement. Here, a highly sensitive and conformal pressure sensor inspired by the kirigami structure is developed to measure the human pulse wave on different body artery sites under various prestressing pressure conditions and even with body movement. COMSOL multiphysical field coupling simulation and experimental testing are used to verify the unique advantages of the kirigami structure. The device shows a superior sensitivity (35.2 mV Pa^-1 ) and remarkable stability (>84 000 cycles). Toward practical applications, a wireless cardiovascular monitoring system is developed for wirelessly transmitting the pulse signals to a mobile phone in real-time, which successfully distinguished the pulse waveforms from different participants. The pulse waveforms measured by the kirigami inspired pressure sensor are as accurate as those provided by the commercial medical device. Given the compelling features, the sensor provides an ascendant way for wearable electronics to overcome motion artifacts when monitoring pulse signals, thus representing a solid advancement toward personalized healthcare in the era of the Internet of Things.

Analysis on Four Derivative Waveforms of Photoplethysmogram (PPG) for Fiducial Point Detection

Fiducial points of photoplethysmogram (PPG), first derivative PPG (VPG), and second derivative PPG (APG) are essential in extracting numerous parameters to diagnose cardiovascular disease. However, the fiducial points were usually detected using complex mathematical algorithms. Inflection points from derivatives waveforms are not thoroughly studied, whereas they can significantly assist in peak detection. This study is performed to investigate the derivative waveforms of PPG and use them to detect the important peaks of PPG, VPG, and APG. PPGs with different morphologies from 43 ischemic heart disease subjects are analyzed. Inflection points of the derivative waveforms up to the fourth level are observed, and consistent information (derivative markers) is used to detect the fiducial points of PPG, VPG, and APG with proper sequence. Moving average filter and simple thresholding techniques are applied to detect the primary points in VPG and the third derivative waveform. A total of twelve out of twenty derivative markers are found reliable in detecting fiducial points of two common types of PPG. Systolic peaks are accurately detected with 99.64% sensitivity and 99.38% positive predictivity using the 43 IHD dataset and Complex System Laboratory (CSL) Pulse Oximetry Artifact Labels database. The study has introduced the fourth derivative PPG waveform with four main points, which are significantly valuable for detecting the fiducial points of PPG, VPG, and APG.

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

(1) Background: Diabetes mellitus (DM) is a chronic, metabolic disease characterized by elevated levels of blood glucose. Recently, some studies approached the diabetes care domain through the analysis of the modifications of cardiovascular system parameters. In fact, cardiovascular diseases are the first leading cause of death in diabetic subjects. Thanks to their cost effectiveness and their ease of use, electrocardiographic (ECG) and photoplethysmographic (PPG) signals have recently been used in diabetes detection, blood glucose estimation and diabetes-related complication detection. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (2) Method: We performed a systematic review based on articles that focus on the use of ECG and PPG signals in diabetes care. The search was focused on keywords related to the topic, such as "Diabetes", "ECG", "PPG", "Machine Learning", etc. This was performed using databases, such as PubMed, Google Scholar, Semantic Scholar and IEEE Xplore. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (3) Results: A total of 78 studies were included. The majority of the selected studies focused on blood glucose estimation (41) and diabetes detection (31). Only 7 studies focused on diabetes complications detection. We present these studies by approach: traditional, machine learning and deep learning approaches. (4) Conclusions: ECG and PPG analysis in diabetes care showed to be very promising. Clinical validation and data processing standardization need to be improved in order to employ these techniques in a clinical environment.

Assessment of arteriosclerosis based on multiscale cross approximate entropy of human finger pulse wave

BACKGROUND: Arteriosclerosis is one of the diseases that endanger human health. There is a large amount of information in pulse wave signals to reflect the degree of arteriosclerosis. OBJECTIVE: The degree of arteriosclerosis is assessed by analyzing pulse wave signal and calculating multi-scale entropy values. METHODS: A method based on the multiscale cross-approximate entropy of the pulse wave of the human finger is proposed to assess the degree of arteriosclerosis. A total of 86 subjects were divided into three groups. The data of 1000 pulse cycles were selected in the experiment, and the multiscale cross-approximate entropy was calculated for the climb time and pulse wave peak interval. Independent sample t-test analysis gives the small-scale cross-approximate entropy of the two time series of climb time and pulse wave peak interval as p< 0.001 in Groups 1 and 2. The large-scale cross-approximate entropy of the two time series of climb time and pulse wave peak interval is p< 0.017 in Groups 2 and 3. RESULTS: Using the proposed algorithm, the results showed that the small-scale cross-approximate entropy of climb time and pulse wave peak interval could reflect the degree of arteriosclerosis in the human body from the perspective of autonomic nerve function. The large-scale cross-approximate entropy of climb time and pulse wave peak interval confirmed the effect of diabetes on the degree of arteriosclerosis. CONCLUSIONS: The results demonstrate the multiscale cross-approximate entropy is a comprehensive index to evaluate the degree of human arteriosclerosis.

Wearable Pressure Sensors for Pulse Wave Monitoring

Cardiovascular diseases remain the leading cause of death worldwide. The rapid development of flexible sensing technologies and wearable pressure sensors have attracted keen research interest and have been widely used for long-term and real-time cardiovascular status monitoring. Owing to compelling characteristics, including light weight, wearing comfort, and high sensitivity to pulse pressures, physiological pulse waveforms can be precisely and continuously monitored by flexible pressure sensors for wearable health monitoring. Herein, an overview of wearable pressure sensors for human pulse wave monitoring is presented, with a focus on the transduction mechanism, microengineering structures, and related applications in pulse wave monitoring and cardiovascular condition assessment. The conceptualizations and methods for the acquisition of physiological and pathological information related to the cardiovascular system are outlined. The biomechanics of arterial pulse waves and the working mechanism of various wearable pressure sensors, including triboelectric, piezoelectric, magnetoelastic, piezoresistive, capacitive, and optical sensors, are also subject to systematic debate. Exemple applications of pulse wave measurement based on microengineering structured devices are then summarized. Finally, a discussion of the opportunities and challenges that wearable pressure sensors face, as well as their potential as a wearable intelligent system for personalized healthcare is given in conclusion.

Functional imaging of human retina using integrated multispectral and laser speckle contrast imaging

A novel integration of retinal multispectral imaging (MSI), retinal oximetry and laser speckle contrast imaging (LSCI) is presented for functional imaging of retinal blood vessels that could potentially allow early detection or monitoring of functional changes. We designed and built a cost-effective, scalable, retinal imaging instrument that integrates structural and functional retinal imaging techniques, including MSI, retinal oximetry and LSCI. Color fundus imaging was performed with 470 nm, 550 nm and 600 nm wavelength light emitting diode (LED) illumination. Retinal oximetry was performed using 550 nm and 600 nm LED illumination. LSCI of blood flow was performed using 850 nm laser diode illumination at 82 frames per second. LSCI can visualize retinal and choroidal vasculature without requiring exogenous contrast agents and can provide time-resolved information on blood flow, generating a cardiac pulse waveform from retinal vasculature. The technology can rapidly acquire structural MSI images, retinal oximetry and LSCI blood flow information in a simplified clinical workflow without requiring patients to move between instruments. Results from multiple modalities can be combined and registered to provide structural as well as functional information on the retina. These advances can reduce barriers for clinical adoption, accelerating research using MSI, retinal oximetry and LSCI of blood flow for diagnosis, monitoring and elucidating disease pathogenesis.

A First Step towards a Comprehensive Approach to Harmonic Analysis of Synchronous Peripheral Volume Pulses: A Proof-of-Concept Study

The harmonic analysis (HA) of arterial radial pulses in humans has been widely investigated in recent years for clinical applications of traditional Chinese medicine. This study aimed at establishing the validity of carrying out HA on synchronous peripheral volume pulses for predicting diabetes-induced subtle changes in heart energy. In this study, 141 subjects (Group 1: 63 healthy elderly subjects; Group 2: 78 diabetic subjects) were enrolled at the same hospital. After routine blood sampling, all synchronous electrocardiogram (ECG) and photoplethysmography (PPG) measurements (i.e., at the six locations) were acquired in the morning. HA of synchronous peripheral volume pulses and radial pulse waves was performed and analyzed after a short period of an ensemble averaging process based on the R-wave peak location. This study utilized HA for the peripheral volume pulses and found that the averaged total pulse energy (i.e., the C₀ of the DTFS) was identical in the same subject. A logistic regression model with C₀ and a waist circumference variable showed a graded association with the risk of developing type 2 diabetes. The adjusted odds ratio for C₀ and the waist circumference were 0.986 (95% confidence interval: 0.977, 0.994) and 1.130 (95% confidence interval: 1.045, 1.222), respectively. C₀ also showed significant negative correlations with risk factors for type 2 diabetes mellitus, including glycosylated hemoglobin and fasting plasma glucose (r = −0.438, p < 0.001; r = −0.358, p < 0.001, respectively). This study established a new application of harmonic analysis in synchronous peripheral volume pulses for clinical applications. The findings showed that the C₀ could be used as a prognostic indicator of a protective factor for predicting type 2 diabetes.

Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring

One in three adults worldwide has hypertension, which is associated with significant morbidity and mortality. Consequently, there is a global demand for continuous and non-invasive blood pressure (BP) measurements that are convenient, easy to use, and more accurate than the currently available methods for detecting hypertension. This could easily be achieved through the integration of single-site photoplethysmography (PPG) readings into wearable devices, although improved reliability and an understanding of BP estimation accuracy are essential. This review paper focuses on understanding the features of PPG associated with BP and examines the development of this technology over the 2010-2019 period in terms of validation, sample size, diversity of subjects, and datasets used. Challenges and opportunities to move single-site PPG forward are also discussed.

Electrocardiogram-Based R Wave Pulse Wave Index for Assessment of Carotid Atherosclerosis

BACKGROUND Carotid atherosclerosis (CA) is a common disease in middle-aged and elderly people, which is closely related to cardiovascular and cerebrovascular disease. In this study, we investigated the benefits of the electrocardiogram (ECG)-based R wave pulse wave index (ERWVI) for the diagnosis of CA. MATERIAL AND METHODS According to CA examinations by color Doppler ultrasound, patients were assigned to positive and negative groups. The ECG R wave-Pulse wave transit time (ERWPTT) was obtained by synchronously collecting ECG signals (R wave in ECG) and the time variations in maximum finger pulse oxygen (DOP) on the ECG monitor. RESULTS ERPWI was positively correlated with sex, age, BMI, diastolic/systolic blood pressure, fasting blood glucose, uric acid, cholesterol and triglyceride levels, LDL-cholesterol, non-alcoholic fatty liver disease (NAFLD), creatinine, and homocysteine, and was negatively correlated with HDL-cholesterol (P<0.05). With the increase of ERPWI, the incidence of CA significantly increased to various degrees among the subgroups (P<0.05). The binary logistic regression model showed that ERPWI was an independent risk factor for atherosclerosis. The ROC curve showed that when ERPWI was above 0.505, the incidence of CA increased significantly. CONCLUSIONS There is a close relationship between ERPWI and CA. ERPWI is an independent risk factor for CA. ERPWI ≥0.505 can be used as a diagnostic threshold for CA and a reference index for the diagnosis of CA.

Influence of mental stress on the pulse wave features of photoplethysmograms

Mental stress is a major burden for our society. Invasive and non-invasive methods have been proposed to monitor and quantify it using various sensors on and off body. In this Letter, the authors investigated the use of the arm photoplethysmogram (PPG) to assess mental stress in laboratory conditions. Results were in correspondence with their previous in-silico study which guided the present study. Three wave shape parameters were identified for stress assessment from the PPG signal: (i) the time from dicrotic notch to end diastole; (ii) the time from pulse onset to systolic peak; and (iii) the ratio of diastolic to systolic area. The proposed in-vivo results showed that the two first parameters responded significantly to increased mental stress and to a breathing relaxation procedure, complementing heart rate, heart rate variability, and pulse transit time as indices of stress.

Instantaneous frequency from Hilbert-Huang transformation of digital volume pulse as indicator of diabetes and arterial stiffness in upper-middle-aged subjects

To investigate the value of decomposed short-time digital volume pulse (DVP) signals in discerning systemic vascular anomaly in diabetic patients, demographic and anthropometric parameters, serum lipid profile, fasting blood glucose and glycated hemoglobin (HbA1c) levels were obtained from 29 healthy adults (Group 1) and 29 age-matched type 2 diabetes mellitus patients (Group 2). Six-second DVP signals from right index finger acquired through photoplethysmography were decomposed using ensemble empirical mode decomposition. Using one intrinsic mode function (IMF5), stiffness index (SI) and instantaneous energy of maximal energy (f_Emax) were obtained. Other indicators of arterial stiffness, including electrocardiogram-pulse wave velocity of foot (ECG-PWV_foot), crest time (CT) and crest time ratio (CTR), were obtained from the testing subjects for comparison. The mean body weight, body mass index, waist circumference, HbA1c and fasting blood sugar levels were higher in Group 2 than those in Group 1, whereas values of systolic and diastolic blood pressure were lower in Group 2 than those in Group 1. SI and f_Emax were significantly higher in Group 2 than those in Group 1. Moreover, f_Emax was positively associated with HbA1c concentration, CT and SI in Group 2 (p < 0.05) but not in Group 1. When all subjects were considered, f_Emax was highly significantly associated with HbA1c and fasting blood sugar levels, and SI (all p < 0.001). After Hilbert-Huang transformation, short-time DVP signals could give significant information on arterial stiffness and vascular anomaly in diabetic patients.

Can Photoplethysmography Replace Arterial Blood Pressure in the Assessment of Blood Pressure?

Arterial Blood Pressure (ABP) and photoplethysmography (PPG) are both useful techniques to monitor cardiovascular status. Though ABP monitoring is more widely employed, this procedure of signal acquisition whether done invasively or non-invasively may cause inconvenience and discomfort to the patients. PPG, however, is simple, noninvasive, and can be used for continuous measurement. This paper focuses on analyzing the similarities in time and frequency domains between ABP and PPG signals for normotensive, prehypertensive and hypertensive subjects and the feasibility of the classification of subjects considering the results of the analysis performed. From a database with 120 records of ABP and PPG, each 120 s in length, the records where separated into epochs taking into account 10 heartbeats, and the following statistical measures were performed: Correlation (r), Coherence (COH), Partial Coherence (pCOH), Partial Directed Coherence (PDC), Directed Transfer Function (DTF), Full Frequency Directed Transfer Function (ffDTF) and Direct Directed Transfer Function (dDTF). The correlation coefficient was r > 0.9 on average for all groups, indicating a strong morphology similarity. For COH and pCOH, coherence (linear correlation in frequency domain) was found with significance (p < 0.01) in differentiating between normotensive and hypertensive subjects using PPG signals. For the dataset at hand, only two synchrony measures are able to convincingly distinguish hypertensive subjects from normotensive control subjects, i.e., ffDTF and dDTF. From PDC, DTF, ffDTF, and dDTF, a consistent, a strong significant causality from ABP→PPG was found. When all synchrony measures were combined, an 87.5 % accuracy was achieved to detect hypertension using a Neural Network classifier, suggesting that PPG holds most informative features that exist in ABP.

Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects

The present study aimed at testing the hypothesis that application of multiscale cross-approximate entropy (MCAE) analysis in the study of nonlinear coupling behavior of two synchronized time series of different natures [i.e., R-R interval (RRI) and crest time (CT, the time interval from foot to peakof a pulse wave)] could yield information on complexity related to diabetes-associated vascular changes. Signals of a single waveform parameter (i.e., CT) from photoplethysmography and RRI from electrocardiogram were simultaneously acquired within a period of one thousand cardiac cycles for the computation of different multiscale entropy indices from healthy young adults (n = 22) (Group 1), upper-middle aged non-diabetic subjects (n = 34) (Group 2) and diabetic patients (n = 34) (Group 3). The demographic (i.e., age), anthropometric (i.e., body height, body weight, waist circumference, body-mass index), hemodynamic (i.e., systolic and diastolic blood pressures), and serum biochemical (i.e., high- and low-density lipoprotein cholesterol, total cholesterol, and triglyceride) parameters were compared with different multiscale entropy indices including small- and large-scale multiscale entropy indices for CT and RRI [MEI_SS(CT), MEI_LS(CT), MEI_SS(RRI), MEI_LS(RRI), respectively] as well as small- and large-scale multiscale cross-approximate entropy indices [MCEI_SS, MCEI_LS, respectively]. The results demonstrated that both MEI_LS(RRI) and MCEI_LS significantly differentiated between Group 2 and Group 3 (all p < 0.017). Multivariate linear regression analysis showed significant associations of MEI_LS(RRI) and MCEI_LS(RRI,CT) with age and glycated hemoglobin level (all p < 0.017). The findings highlight the successful application of a novel multiscale cross-approximate entropy index in non-invasively identifying diabetes-associated subtle changes in vascular functional integrity, which is of clinical importance in preventive medicine.