Photoplethysmogram Waveform Research Articles

Retracted: Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling.

[This retracts the article DOI: 10.1155/2020/3764653.].

Decomposing photoplethysmogram waveforms into systolic and diastolic waves, with application to hyperbaric environments

Objective:A new algorithm to decompose the photoplethysmogram (PPG) pulse in two waves related with the systolic and diastolic parts, was applied to identify alterations in the morphology of the PPG pulse due to the pressure. Approach:Each pulse was decomposed into two waves: the first one a Gaussian related with the systolic peak, and the second one was modelled as a lognormal curve associated with the diastolic part. From these two waves, 13 parameters related with the width (W1, W2 and W2/W1), the time instant (T1, T2, T21, TBB), the amplitude (A1, A2, A2/A1) and the areas (D1, D2, D2/D1) were estimated. These parameters were computed from subjects inside a hyperbaric chamber, involving five stages at different pressure: 1 atm, 3 atm, 5 atm, 3 atm and 1 atm. Main results:There was a significant increase in the values of A1, A2, W1, T1, and D1, and a decrease in the ratios when the pressure increased, that suggest a vasoconstriction when the pressure increased. There is also an increase in the values of TBB, T2, T12, and W2 along the protocol, that implies a dependency of some parameters with the pulse-to-pulse interval. Significance:This methodology allows extracting a large set of parameters related with the PPG morphology that are affected by the change of pressure inside hyperbaric environments.

PS-C25-5: ASSOCIATIONS BETWEEN CARDIORESPIRATORY FITNESS AND CARDIOVASCULAR PARAMETERS DERIVED AT REST FROM FINGER PHOTOPLETHYSMOGRAM

Objective: Cardiorespiratory fitness has been shown to be correlated with cardiovascular complications and comparable to other conventional health parameters. Consequently, it can be a useful preventative and a practical first line of information for cardiovascular diseases, such as hypertension. Wearable devices have the capacity to provide a convenient assessment of individuals cardiorespiratory fitness. Despite its clinical relevance measures of cardiorespiratory fitness remain limited to commercial devices for exercise training purposes. This study investigates whether there is potential for cardiorespiratory fitness to be derived from indices measured at rest from a finger photoplethysmogram waveform. Design and method: Participants (n = 8, 4 females, 39 ± 17 years) first completed two questionnaires to provide two separate estimates of cardiorespiratory fitness. The first questionnaire by Jackson et al. (1990) provides an estimated maximum rate of oxygen (VO2max, gold standard measure of cardiorespiratory fitness) based on height, weight, age, and a subjective physical activity rating score. The second questionnaire by Radim et al. (2005) provides a metabolic equivalent parameter (MET) that has previously been shown to correlate with measured VO2max. Following 10 minutes of seated rest, 30 seconds of finger photoplethysmography was recorded. A 10-second snippet with high quality signal was extracted, and a generalised transfer function was applied to the signal to generate an uncalibrated central aortic waveform. Pulse pressure amplification (PPAmp), ejection duration (ED), subendocardial viability ratio (SEVR), heart rate (HR) and augmentation index corrected to a HR of 75 bpm (AIx75bpm) were then calculated for each cardiac cycle and averaged over 10 seconds. Correlation plots and stepwise linear regression were then performed on the estimated VO2max and MET with the derived parameters. Results: Average baseline estimated VO2max was 44 ± 10 ml/kg/min and estimated MET was 11 ± 2 (1 MET = VO2/3.5). Stepwise linear regression revealed AIx75bpm, SEVR and HR were the strongest predictors of estimated VO2max (model R^2 = 0.929, F = 17.44, p = 0.009). Only AIx75bpm and SEVR were strongly associated with estimated MET (model R^2 = 0.942, F = 40.5, p < 0.001). For both estimated VO2max and estimated MET, individual correlation plots for PPAmp and ED showed poor correlation. Conclusion: A selection of non-exercise derived central aortic parameters from finger photoplethysmography were correlated with cardiorespiratory fitness. This demonstrates that a finger photoplethysmogram signal obtained at rest, such as can be easily measured in wearable devices, may be useful in management and monitoring of cardiorespiratory fitness, and by association health, without the burden of performing rigorous exercise tests.

A computationally efficient CNN-LSTM neural network for estimation of blood pressure from features of electrocardiogram and photoplethysmogram waveforms

Continuous blood pressure (BP) monitoring would significantly improve diagnosis and treatment of hypertension. Current at-home monitoring relies on uncomfortable and unreliable cuff-based devices, which are incapable of continuous measurement. In this work, we present a new hybrid neural network (NN) that combines convolutional layers with long short-term memory (LSTM) layers to classify systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP), using 12 straightforward features extracted from electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms. Our proposed network achieves mean absolute errors (MAEs) of 4.53 mmHg, 3.37 mmHg and 3.36 mmHg for SBP, DBP and MAP respectively. Additionally, our scheme passes the criteria outlined by the Association for the Advancement of Medical Instrumentation (AAMI) and achieves an A grade in accordance with the British Hypertension Society (BHS) protocol. These results provide a deep learning approach to BP estimation that could be implemented in low-power wearable devices.

Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study.

BackgroundFor the noninvasive assessment of arterial stiffness, a well-known indicator of arterial aging, various features based on the photoplethysmogram and regression methods have been proposed. However, whether because of the existing characteristics not accurately reflecting the characteristics of the incident and reflected waveforms of the photoplethysmogram or because of the lack of expressive power of the regression model, a reliable arterial stiffness assessment technique based on a single photoplethysmogram has not yet been proposed.ObjectiveThe purpose of this study is to discover highly correlated features from the incident and reflected waves decomposed from a photoplethysmogram waveform and to develop an artificial neural network-based regression model for the assessment of vascular aging using newly derived features.MethodsWe obtained photoplethysmograms from 757 participants. All recorded photoplethysmograms were segmented for each beat, and each waveform was decomposed into incident and reflected waves by the Gaussian mixture model. The 26 basic features and 52 combined features were defined from the morphological characteristics of the incident and reflected waves. The regression model of the artificial neural network was developed using the defined features.ResultsIn correlation analysis, the features from the amplitude of the reflected wave and the skewness of the photoplethysmogram showed a relatively strong correlation with the participant’s real age. In the estimation of real age, the artificial neural network model showed 10.0 years of root mean square error. Its estimated age and real age had a strong correlation of 0.63 (P<.001).ConclusionsThis study proved that the features defined from the reflected wave and skewness of the photoplethysmogram are useful to assess vascular aging. Moreover, the regression model of artificial neural network using these features shows the feasibility for the estimation of vascular aging.

Pulse arrival time as a surrogate of blood pressure

Various models have been proposed for the estimation of blood pressure (BP) from pulse transit time (PTT). PTT is defined as the time delay of the pressure wave, produced by left ventricular contraction, measured between a proximal and a distal site along the arterial tree. Most researchers, when they measure the time difference between the peak of the R-wave in the electrocardiogram signal (corresponding to left ventricular depolarisation) and a fiducial point in the photoplethysmogram waveform (as measured by a pulse oximeter attached to the fingertip), describe this erroneously as the PTT. In fact, this is the pulse arrival time (PAT), which includes not only PTT, but also the time delay between the electrical depolarisation of the heart’s left ventricle and the opening of the aortic valve, known as pre-ejection period (PEP). PEP has been suggested to present a significant limitation to BP estimation using PAT. This work investigates the impact of PEP on PAT, leading to a discussion on the best models for BP estimation using PAT or PTT. We conducted a clinical study involving 30 healthy volunteers (53.3% female, 30.9 ± 9.35 years old, with a body mass index of 22.7 ± 3.2 kg/m^{2}). Each session lasted on average 27.9 ± 0.6 min and BP was varied by an infusion of phenylephrine (a medication that causes venous and arterial vasoconstriction). We introduced new processing steps for the analysis of PAT and PEP signals. Various population-based models (Poon, Gesche and Fung) and a posteriori models (inverse linear, inverse squared and logarithm) for estimation of BP from PTT or PAT were evaluated. Across the cohort, PEP was found to increase by 5.5 ms ± 4.5 ms from its baseline value. Variations in PTT were significantly larger in amplitude, − 16.8 ms ± 7.5 ms. We suggest, therefore, that for infusions of phenylephrine, the contribution of PEP on PAT can be neglected. All population-based models produced large BP estimation errors, suggesting that they are insufficient for modelling the complex pathways relating changes in PTT or PAT to changes in BP. Although PAT is inversely correlated with systolic blood pressure (SBP), the gradient of this relationship varies significantly from individual to individual, from − 2946 to − 470.64 mmHg/s in our dataset. For the a posteriori inverse squared model, the root mean squared errors (RMSE) for systolic and diastolic blood pressure (DBP) estimation from PAT were 5.49 mmHg and 3.82 mmHg, respectively. The RMSEs for SBP and DBP estimation by PTT were 4.51 mmHg and 3.53 mmHg, respectively. These models take into account individual calibration curves required for accurate blood pressure estimation. The best performing population-based model (Poon) reported error values around double that of the a posteriori inverse squared model, and so the use of population-based models is not justified.

Estimation of Fetal Blood Oxygen Saturation from Transabdominally Acquired Photoplethysmogram Waveforms.

Transabdominal Fetal Pulse Oximetry (TFO) faces several challenges, including the acquisition of noisy Photoplethysmogram (PPG) signals that contain a mixture of maternal and weak fetal information and scarcity of the data points on which an estimation model can be calibrated. This paper presents a novel algorithm that addresses these problems and contributes to the estimation of fetal blood oxygen saturation from PPG signals sensed through the maternal abdomen in a non-invasive manner. Our approach is composed of two critical steps. First, we develop methods to approximate the contribution of pulsating and non-pulsating fetal tissue from the sensed mixed signal. Furthermore, we leverage prior information about the system under observation, such as the physiological plausibility of fetal SpO2 estimates, to mitigate measurement noise and infer additional data samples, enabling improvements in the inferred SpO2 estimation model. We have validated our approach in-vivo, using a pregnant sheep model with a hypoxic fetal lamb. Compared with gold standard SaO2 obtained from blood gas analysis, our fetal SpO2 estimation algorithm yields the cross-validation mean absolute error (MAE) of 6.29% and correlation factor of r=0.82.

Cuffless blood pressure estimation from PPG signals and its derivatives using deep learning models

Blood pressure (BP) is a direct indicator for hypertension, therefore, continuous and non-invasive BP monitoring is essential for reducing future health complications. Most non-invasive blood pressure monitors use the oscillometric technique, which can be cumbersome and impractical. To address this problem, we explore several features extracted from the Photoplethysmogram (PPG) waveform and its first and second derivatives and employ deep learning recurrent models for non-invasive cuffless estimation for systolic and diastolic BP. In this research, three techniques have been considered including statistical and machine learning techniques for reducing the collinearity and redundancy in the input feature vector. The estimation models consist of a one bidirectional recurrent layer, followed by a series of stacked conventional recurrent layers and an attention layer. All models were evaluated on 942 subjects collected from the MIMIC II dataset. The best performing model (consists of one bidirectional layer followed by several Long Short-Term Memory layers and an attention layer) achieved a mean absolute error, and standard deviation of 4.51 ± 7.81 mmHg for systolic BP (SBP), and 2.6 ± 4.41 mmHg for diastolic BP (DBP). The results show that the deep learning model trained on features extracted from one PPG sensor yield good performance for continuous and cuffless BP monitoring. Additionally, the results fulfil the international standard for cuffless BP estimation.

A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms

Background and objectives: Continuous and non-invasive blood pressure monitoring would revolutionize healthcare. Currently, blood pressure (BP) can only be accurately monitored using obtrusive cuff-based devices or invasive intra-arterial monitoring. In this work, we propose a novel hybrid neural network for the accurate estimation of blood pressure (BP) using only non-invasive electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms as inputs.Methods: This work proposes a hybrid neural network combines the feature detection abilities of temporal convolutional layers with the strong performance on sequential data offered by long short-term memory layers. Raw electrocardiogram and photoplethysmogram waveforms are concatenated and used as network inputs. The network was developed using the TensorFlow framework. Our scheme is analysed and compared to the literature in terms of well known standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI).Results: Our scheme achieves extremely low mean absolute errors (MAEs) of 4.41 mmHg for SBP, 2.91 mmHg for DBP, and 2.77 mmHg for MAP. A strong level of agreement between our scheme and the gold-standard intra-arterial monitoring is shown through Bland Altman and regression plots. Additionally, the standard for BP devices established by AAMI is met by our scheme. We also achieve a grade of ‘A’ based on the criteria outlined by the BHS protocol for BP devices.Conclusions: Our CNN-LSTM network outperforms current state-of-the-art schemes for non-invasive BP measurement from PPG and ECG waveforms. These results provide an effective machine learning approach that could readily be implemented into non-invasive wearable devices for use in continuous clinical and at-home monitoring.

The Machine Learnings Leading the Cuffless PPG Blood Pressure Sensors Into the Next Stage

Recent works on the machine learnings designed for cuffless blood pressure (BP) estimation based on measured photoplethysmogram (PPG) waveforms are reviewed by this study, with future trends of the related technology developments distilled. This review starts with those based on the conventional pulse wave velocity (PWV) theory, by which few equations are derived to calculate BPs based on measured pulse arrival times (PATs) and/or pulse transit time (PTT). Due to the inadequacy of PATs and PPTs to characterize BP, some works were reported to employ more features in PPG waveforms to achieve better accuracy. In these works, varied machine learnings were adopted, such as support vector machine (SVM), regression tree (RT), adaptive boosting (AdaBoost), and artificial neural network (ANN), etc., resulting in satisfactory accuracies based on a large number of data in the databases of Queensland and/or MIMIC II. Most recently, a few studies reported to utilize the deep learning machines like convolution neural network (CNN), recursive neural network (RNN), and long short-term memory (LSTM), etc., to handle feature extraction and establish models integrally, with the aim to cope with the inadequacy of pre-determined (hand-crafted) features to characterize BP and the difficulty of extracting pre-determined features by a designed algorithm. Therefore, the deep learning opens an opportunity of achieving much better BP accuracy by using a single PPG sensor. Favorable accuracies have been resulted by these few studies in comparison with prior works. Finally, future research efforts needed towards successful commercialization of the cuffless BP sensor are distilled.

Recommendation for Measuring Digital Volume Pulse in Mobile Application: For Healthy Normal Subject

The aim of this study was to identify changes in photoplethysmogram waveform with decreasing sampling frequency and quantization bit depth and suggest appropriate criteria for measurements. We performed down-sampling and re-quantization of phoplethysmogram measured at high resolution. Changes of waveform were quantitatively observed. Photoplethysmograms recorded at a 1-kHz sampling frequency and 16-bit quantization bit depth were converted to signals with sampling frequencies of 500-, 250-, 100-, 50-, 25-, and 10-Hz by means of down-sampling. We then performed re-quantization to convert the quantization bit depth into 16, 14, 12, 10, 8, and 6 bits for each down-sampled signal. Degradation of signal was quantified in terms of morphological change and feature-point deviation. Morphological analysis revealed that the correlation coefficient was decreased while normalized root mean square error was increased with decreasing sampling frequency and quantization bit depth. Feature-point analysis revealed that the mean absolute time error and amplitude error of feature points tended to increase with decreasing sampling frequency and quantization bit depth. Although there were differences according to pulse onset and systolic peak, sampling frequency ≥ 250-Hz and 16-bit quantization bits were required in order to have ≤ 1 ms of timing error and a normalized amplitude error ≤1%. In addition, sampling frequency ≥ 100-Hz and 12-bit quantization bits are recommended to have feature-point time errors and amplitude errors < 10 ms and a normalized amplitude error < 10%.

Vital sign monitoring during out-of-hospital pediatric advanced airway management.

ObjectiveTo evaluate physiologic monitoring in pediatric patients undergoing out‐of‐hospital advanced airway management.MethodsRetrospective case series of pediatric patients (<18 years) with advanced airways placed in the out‐of‐hospital setting. Patients given cardiopulmonary resuscitation (CPR) or defibrillation before the first advanced airway attempt were excluded. Reviewers abstracted physiologic data from the patient monitor files and patient care reports. The primary outcome was the proportion of time pulse oximetry was in place during airway management. Other outcomes included the proportion of time ECG monitoring and waveform end‐tidal capnography were in place as well as the incidence of oxygen desaturation events.ResultsWe evaluated 23 pediatric patients with a mean age of 10.7 years (SD 6.5). Eleven of 18 (61%) children with medication‐facilitated intubation had pulse oximetry in place when the first medication was documented as given. Eight of 18 (44%) had ECG monitoring, 12 of 18 (66%) had waveform capnography, and 5 of 18 (28%) had a blood pressure check within the 3 minutes before receiving the first medication. In the 3‐minute preoxygenation phase, pulse oximetry was in place for an average of 1.4 minutes (47%, SD 0.37) and a visible photoplethysmogram (PPG) waveform obtained from the pulse oximeter was present for 0.6 minutes (20%, SD 0.34). During airway device placement, pulse oximetry was in place 73% (SD 0.39) of the time and 30% (SD 0.41) of the time there was a visible PPG waveform.ConclusionsPediatric patients had critical deficits in physiologic monitoring during advanced airway management.

A Pilot Study using the Compensatory Reserve Index to evaluate individuals with Postural Orthostatic Tachycardia syndrome.

The diagnosis of Postural Orthostatic Tachycardia syndrome traditionally involves orthostatic vitals evaluation. The Compensatory Reserve Index is a non-invasive, FDA-cleared algorithm that analyses photoplethysmogram waveforms in real time to trend subtle waveform features associated with varying degrees of central volume loss, from normovolemia to decompensation. We hypothesised that patients who met physiologic criteria for Postural Orthostatic Tachycardia syndrome would have greater changes in Compensatory Reserve Index with orthostatic vitals. Orthostatic vitals and Compensatory Reserve Index values were assessed in individuals previously diagnosed with Postural Orthostatic Tachycardia syndrome and healthy controls aged 12-21 years. Adolescents were grouped for comparison based on whether they met heart rate criteria for Postural Orthostatic Tachycardia syndrome (physiologic Postural Orthostatic Tachycardia syndrome). Sixty-one patients were included. Eighteen percent of patients with an existing Postural Orthostatic Tachycardia syndrome diagnosis met heart rate criteria, and these patients had significantly greater supine to standing change in Compensatory Reserve Index (0.67 vs. 0.51; p<0.001). The optimal change in Compensatory Reserve Index for physiologic Postural Orthostatic Tachycardia syndrome was 0.60. Patients with physiologic Postural Orthostatic Tachycardia syndrome were more likely to report previous diagnoses of anxiety or depression (p = 0.054, 0.042). An accurate diagnosis of Postural Orthostatic Tachycardia syndrome may be confounded by related comorbidities. Only 18% (8/44) of previously diagnosed Postural Orthostatic Tachycardia syndrome patients met heart rate criteria. Findings support the utility of objective physiologic measures, such as the Compensatory Reserve Index, to more accurately identify patients with true autonomic dysfunction.

Methods of Extracting Feature from Photoplethysmogram Waveform for Non-Invasive Diagnostic Applications

&lt;p&gt;This paper presents a bibliographical survey of recently-published research on different techniques to extract feature from photoplethysmogram (PPG). These techniques and approaches have been implemented for better accuracy in detecting diseases. Moreover, several aspects in analyzing PPG waveform are discussed on the techniques in feature extraction, parameters involved and performance comparisons. This review will serve as a comparative study and reference for researches working on PPG waveform in health care applications.&lt;/p&gt;

Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling.

In this research, the photoplethysmogram (PPG) waveform analysis is utilized to develop a logistic regression-based predictive model for the classification of diabetes. The classifier has three predictors age, b/a, and SP indices in which they achieved an overall accuracy of 92.3% in the prediction of diabetes. In this study, a total of 587 subjects were enrolled. A total of 459 subjects were used for model training and development, while the rest of the 128 subjects were used for model testing and validation. The classifier was able to diagnose 63 patients correctly as diabetes while 27 subjects were wrongly classified as nondiabetes with an accuracy of 70%. Again, the model classified 479 subjects as nondiabetes correctly while it incorrectly classified 18 subjects as diabetes with an accuracy of 96.4%. Finally, the proposed model revealed an overall predictive accuracy of 92.3% which makes it a reliable surrogate measure for diabetes classification and prediction in clinical settings.

Reflectance-Based Organic Pulse Meter Sensor for Wireless Monitoring of Photoplethysmogram Signal.

This paper compares the structural design of two organic biosensors that minimize power consumption in wireless photoplethysmogram (PPG) waveform monitoring. Both devices were fabricated on the same substrate with a red organic light-emitting diode (OLED) and an organic photodiode (OPD). Both were designed with a circular OLED at the center of the device surrounded by OPD. One device had an OLED area of 0.06 cm2, while the other device had half the area. The gap distance between the OLED and OPD was 1.65 mm for the first device and 2 mm for the second. Both devices had an OPD area of 0.16 cm2. We compared the power consumption and signal-to-noise ratio (SNR) of both devices and evaluated the PPG signal, which was successfully collected from a fingertip. The reflectance-based organic pulse meter operated successfully and at a low power consumption of 8 µW at 18 dB SNR. The device sent the PPG waveforms, via Bluetooth low energy (BLE), to a PC host at a maximum rate of 256 kbps data throughput. In the end, the proposed reflectance-based organic pulse meter reduced power consumption and improved long-term PPG wireless monitoring.

Determination of vascular responses to manual PC6 acupuncture in subjects healthy and diabetics by the second derivative of the finger photoplethysmogram waveform

Determination of vascular responses to manual PC6 acupuncture in subjects healthy and diabetics by the second derivative of the finger photoplethysmogram waveform

Evaluation of the Possible Use of PPG Waveform Features Measured at Low Sampling Rate

To date, the applications of photoplethysmograms in the estimation of cuff-less blood pressure, arteriosclerosis, and so on have been studied. Photoplethysmogram waveform features have often been used to estimate the target volumes. For these estimations, it is necessary to acquire photoplethysmogram waveforms and changes in their derivative waveform details, for which the photoplethysmograms measured at comparatively high sampling rates have been used. The performance of smartphone cameras and wearable photoplethysmogram sensors has improved; with regard to mobile health technology, photoplethysmograms measured at lower sampling rates offer considerable advantages. These include lower computational resources, compression of accumulated data, and lower sensor power consumption. However, compared to photoplethysmograms measured at a high sampling rate, photoplethysmogram measurement at a low sampling rate will result in waveform signal degradation. This paper investigates the possibility of using photoplethysmograms measured at a low sampling rate. To this end, we statistically compared photoplethysmogram waveform features obtained from 63 male subjects free of circulatory diseases, at a sampling rate of 240 Hz, with waveform features obtained at low sampling rates (120, 60, 30, 20, and 10 Hz) through downsampling, and evaluated possible commercial use.

Neural Network-based Diabetic Type II High-Risk Prediction using Photoplethysmogram Waveform Analysis

This work aims to predict and classify patients into diabetic and nondiabetic subjects based on age and four independent variables extracted from the analysis of photoplethysmogram (PPG) morphology in time domain. The study has two main stages, the first one was the analysis of PPG waveform to extract b/a, RI, DiP, and SPt indices. These parameters contribute by some means to the prediction of diabetes. They were statistically significant and correlated with the HbA1C test. The second stage was building a neural network based classifier to predict diabetes. The model showed an accuracy of 90.2% in training phase and an accuracy of 85.5% in testing phase. The findings of this research work may contribute towards the prediction of diabetes in early stages. Also, the proposed classifier showed a high accuracy in predicting the existence of diabetes in Saudi people population.

Application of multiscale Poincaré short-time computation versus multiscale entropy in analyzing fingertip photoplethysmogram amplitudes to differentiate diabetic from non-diabetic subjects

Background and objectivesMultiscale Poincaré (MSP) plots have recently been introduced to facilitate the visualization of time series of physiological signals. This study aimed at investigating the feasibility of MSP application in distinguishing subjects with and without diabetes. MethodsUsing photoplethysmogram (PPG) waveform amplitudes acquired from unilateral fingertip of non-diabetic (n = 34) and diabetic (n = 30) subjects, MSP indices (MSPI) of the two groups were compared using 1000, 500, 250, 100 data points. Data from Poincaré index (short-term variability/long-term variability [i.e. SD1/SD2] ratio, SSR) and multiscale entropy (MSE) were also obtained with the four corresponding data points for comparison. ResultsSSR and MSPI were both negatively related to glycated hemoglobin (HbA1c) and fasting blood sugar levels. Significant negative correlation was also noted between MSPI and pulse pressure. When only 500 and 250 data points were included, significant elevations in the non-diabetic group were only noted in MSPI (both p < 0.01). Furthermore, MSPI was significantly higher in non-diabetic than that in diabetic subjects on all scales (i.e., 1–10) but not using MSE when utilizing 1000 data points. ConclusionsThe results demonstrated enhanced sensitivity of MSP in differentiating between non-diabetic and diabetic subjects compared to SSR and MSE, highlighting the feasibility of MSP application in biomedical data analysis to reduce computational time and enhance sensitivity.