Posture-Dependent Variability in Wrist Ballistocardiogram-Photoplethysmogram Pulse Transit Time: Implication to Cuff-Less Blood Pressure Tracking.

OBJECTIVE: Toward the ultimate goal of robust cuff-less blood pressure (BP) tracking with wrist wearables against postural changes, the goal of this work was to investigate posture-dependent variability in pulse transit time (PTT) measured with ballistocardiogram (BCG) and photoplethysmogram (PPG) signal pair at the wrist. METHODS: BCG and PPG signals were acquired from 25 subjects under the combination of 3 body (standing, sitting, and supine) and 3 arm (vertical in head-to-foot direction, placed on the chest, and holding a shoulder) postures. PTT was computed as the time interval between the BCG J wave and the PPG foot, and the impact of the 9 postures on PTT was analyzed by invoking an array of possible physical mechanisms. RESULTS: Our work suggests that (i) wrist BCG-PPG PTT is consistent under standing and sitting postures with vertically held arms; and (ii) changes in wrist orientation and height as well as restrictions in body and arm movement may alter wrist BCG-PPG PTT via distortions in the wrist BCG and PPG waveforms. The results indicate that wrist BCG-PPG PTT varies with respect to postures even when BP remains constant. CONCLUSION: The potential of cuff-less BP tracking via wrist BCG-PPG PTT demonstrated under standing posture with arms vertically down in the head-to-foot direction may not generalize to other body and arm postures. SIGNIFICANCE: Understanding the physical mechanisms responsible for posture-induced BCG-PPG PTT variability may increase the versatility of the wrist BCG for cuff-less BP tracking.

A Unified Approach to Wearable Ballistocardiogram Gating and Wave Localization.

OBJECTIVE: Toward the ultimate goal of cuff-less blood pressure (BP) trend tracking via pulse transit time (PTT) using wearable ballistocardiogram (BCG) signals, we present a unified approach to the gating of wearable BCG and the localization of wearable BCG waves. METHODS: We present a unified approach to localize wearable BCG waves suited to various gating and localization reference signals. Our approach gates individual wearable BCG beats and identifies candidate waves in each wearable BCG beat using a fiducial point in a reference signal, and exploits a pre-specified probability distribution of the time interval between the BCG wave and the fiducial point in the reference signal to accurately localize the wave in each wearable BCG beat. We tested the validity of our approach using experimental data collected from 17 healthy volunteers. RESULTS: We showed that our approach could localize the J wave in the wearable wrist BCG accurately with both the electrocardiogram (ECG) and the wearable wrist photoplethysmogram (PPG) signals as reference, and that the wrist BCG-PPG PTT thus derived exhibited high correlation to BP. CONCLUSION: We demonstrated the proof-of-concept of a unified approach to localize wearable BCG waves suited to various gating and localization reference signals compatible with wearable measurement. SIGNIFICANCE: Prior work using the BCG itself or the ECG to gate the BCG beats and localize the waves to compute PTT are not ideally suited to the wearable BCG. Our approach may foster the development of cuff-less BP monitoring technologies based on the wearable BCG.

Low Muscle Mass Is Associated with Lower 25-Hydroxyvitamin D Level in All Age Groups of South Korean Adults: The 2009-2010 Korea National Health and Nutrition Examination Surveys (KNHANES).

Vitamin D plays pivotal role in bone mineral homeostasis. But the association of vitamin D with muscle mass remains obscure, especially among young adults. Therefore, we assessed the association between muscle mass and 25-hydroxyvitamin D (25[OH]D) in South Korean adults using data from the 2009-2010 Korean National Health and Nutrition Examination Survey (KNHANES). This study involved 12,324 (5,375 males and 6,949 females) participants in the 2009-2010 KNHANES aged 20 y or older. Appendicular skeletal muscle mass (ASM) was measured by dual X-ray absorptiometry. Low muscle mass was defined as an ASM divided by body mass index (BMI) (ASM [kg]÷BMI [kg/m2]) value of <0.789 in males and <0.512 in females. The vitamin D status was evaluated by assaying the serum 25(OH)D level. After adjustment for covariates, low muscle mass was significantly associated with lower 25(OH)D level (odds ratio [OR], 0.55; 95% confidence interval [CI], 0.40-0.75 for 10.0-19.9 ng/mL vs. <10.0 ng/mL; OR, 0.47; 95% CI, 0.33-0.68 for 20.0-29.9 ng/mL vs. <10.0 ng/mL; and OR, 0.39; 95% CI, 0.24-0.64 for ≥30.0 ng/mL vs. <10.0 ng/mL). Moreover, low muscle mass was significantly associated with lower 25(OH)D level in all age groups. In conclusion, low muscle mass was significantly associated with lower 25(OH)D level in South Korean adults in all age groups.

The Potential of Wearable Limb Ballistocardiogram in Blood Pressure Monitoring via Pulse Transit Time.

The goal of this study was to investigate the potential of wearable limb ballistocardiography (BCG) to enable cuff-less blood pressure (BP) monitoring, by investigating the association between wearable limb BCG-based pulse transit time (PTT) and BP. A wearable BCG-based PTT was calculated using the BCG and photoplethysmogram (PPG) signals acquired by a wristband as proximal and distal timing reference (called the wrist PTT). Its efficacy as surrogate of BP was examined in comparison with PTT calculated using the whole-body BCG acquired by a customized weighing scale (scale PTT) as well as pulse arrival time (PAT) using the experimental data collected from 22 young healthy participants under multiple BP-perturbing interventions. The wrist PTT exhibited close association with both diastolic (group average r = 0.79; mean absolute error (MAE) = 5.1 mmHg) and systolic (group average r = 0.81; MAE = 7.6 mmHg) BP. The efficacy of the wrist PTT was superior to scale PTT and PAT for both diastolic and systolic BP. The association was consistent and robust against diverse BP-perturbing interventions. The wrist PTT showed superior association with BP when calculated with green PPG rather than infrared PPG. In sum, wearable limb BCG has the potential to realize convenient cuff-less BP monitoring via PTT.

Physiological Association between Limb Ballistocardiogram and Arterial Blood Pressure Waveforms: A Mathematical Model-Based Analysis.

By virtue of its direct association with the cardiovascular (CV) functions and compatibility to unobtrusive measurement during daily activities, the limb ballistocardiogram (BCG) is receiving an increasing interest as a viable means for ultra-convenient CV health and disease monitoring. However, limited insights on its physical implications have hampered disciplined interpretation of the BCG and systematic development of the BCG-based approaches for CV health monitoring. In this study, a mathematical model that can predict the limb BCG in responses to the arterial blood pressure (BP) waves in the aorta was developed and experimentally validated. The validated mathematical model suggests that (i) the limb BCG waveform reveals the timings and amplitudes associated with the aortic BP waves; (ii) mechanical filtering exerted by the musculoskeletal properties of the body can obscure the manifestation of the arterial BP waves in the limb BCG; and (iii) the limb BCG exhibits meaningful morphological changes in response to the alterations in the CV risk predictors. The physical insights garnered by the analysis of the mathematical model may open up new opportunities toward next generation of the BCG-based CV healthcare techniques embedded with transparency, interpretability, and robustness against the external variability.

A Simple and Robust Method for Determining the Quality of Cardiovascular Signals Using the Signal Similarity.

This paper proposes a novel signal quality assessment method for quasi-periodic cardiovascular signals, chiefly focus on the photoplethysmogram (PPG). The proposed method utilizes the fact that most cardiovascular signals are slowly time varying and thus morphological aspects of the two adjacent beats are almost identical. In order to implement this idea, the method first identifies pulse onset to divide the signal into several segments each of which contains one period of the signal. The segmented pulse signals having different pulse durations are then temporarily normalized by resampling them at a specific rate. Finally, the quality of the signals is evaluated as the signal similarity between the two adjacent segments. Optimal thresholds for the classification between high-and low-quality PPG signals are determined using the equal training sensitivity and specificity criterion. The proposed method is evaluated using a database where PPG signals are collected during a variety of activities such as cycling exercise. It attains a sensitivity of 97.9%, a specificity of 85.3%, and an accuracy of 93.8%, compared to manually annotated results. The promising results indicate that the proposed method is affordable to simply determine the quality of quasi-periodic cardiovascular signals, particularly PPG signals. In addition, based on the quasi-periodic characteristics of cardiovascular signals, the proposed method can also be used to indicate the reliability and the availability of the collected signals.

Data mining investigation of the association between a limb ballistocardiogram and blood pressure.

OBJECTIVE: To investigate the association between a limb ballistocardiogram (BCG) and blood pressure (BP) based on data mining. APPROACH: During four BP-perturbing interventions, the BCG and reference BP were measured from 23 young, healthy volunteers using a custom-manufactured wristband equipped with a MEMS accelerometer and a commercial continuous BP measurement device. Both timing and amplitude features in the wrist BCG waveform were extracted, and significant features predictive of diastolic (DP) and systolic (SP) BP were selected using stepwise linear regression analysis. The selected features were further compressed using principal component analysis to yield a small set of DP and SP predictors. The association between the predictors thus obtained and BP was investigated by multivariate linear regression analysis. MAIN RESULTS: The predictors exhibited a meaningful association with BP. When three most significant predictors were used for DP and SP, a correlation coefficient of r = 0.75 ± 0.03 (DP) and r = 0.75 ± 0.03 (SP), a root-mean-squared error (RMSE) of 7.4 ± 0.6 mmHg (DP) and 10.3 ± 0.8 mmHg (SP), and a mean absolute error (MAE) of 6.0 ± 0.5 mmHg (DP) and 8.3 ± 0.7 mmHg (SP) were obtained across all interventions (mean ± SE). The association was consistent in all the individual interventions (r ⩾ 0.68, RMSE ⩽ 5.7 mmHg, and MAE ⩽ 4.5 mmHg for DP as well as r ⩾ 0.61, RMSE ⩽ 7.9 mmHg, and MAE ⩽ 6.4 mmHg for SP on the average). The minimum number of requisite predictors for robust yet practically realistic BP monitoring appeared to be three. The association between predictors and BP was maintained even under regularized calibration (r = 0.63 ± 0.05, RMSE = 9.3 ± 0.8 mmHg, and MAE = 7.6 ± 0.7 mmHg for DP as well as r = 0.60 ± 0.05, RMSE = 14.7 ± 1.4 mmHg, and MAE = 11.9 ± 1.1 mmHg for SP (mean ± SE)). The requisite predictors for DP and SP were distinct from each other. SIGNIFICANCE: The results of this study may provide a viable basis for ultra-convenient BP monitoring based on a limb BCG alone.

A Gaussian Model-Based Probabilistic Approach for Pulse Transit Time Estimation.

In this paper, we propose a new probabilistic approach to pulse transit time (PTT) estimation using a Gaussian distribution model. It is motivated basically by the hypothesis that PTTs normalized by RR intervals follow the Gaussian distribution. To verify the hypothesis, we demonstrate the effects of arterial compliance on the normalized PTTs using the Moens-Korteweg equation. Furthermore, we observe a Gaussian distribution of the normalized PTTs on real data. In order to estimate the PTT using the hypothesis, we first assumed that R-waves in the electrocardiogram (ECG) can be correctly identified. The R-waves limit searching ranges to detect pulse peaks in the photoplethysmogram (PPG) and to synchronize the results with cardiac beats--i.e., the peaks of the PPG are extracted within the corresponding RR interval of the ECG as pulse peak candidates. Their probabilities of being the actual pulse peak are then calculated using a Gaussian probability function. The parameters of the Gaussian function are automatically updated when a new pulse peak is identified. This update makes the probability function adaptive to variations of cardiac cycles. Finally, the pulse peak is identified as the candidate with the highest probability. The proposed approach is tested on a database where ECG and PPG waveforms are collected simultaneously during the submaximal bicycle ergometer exercise test. The results are promising, suggesting that the method provides a simple but more accurate PTT estimation in real applications.

Wearable Sensing of In-Ear Pressure for Heart Rate Monitoring with a Piezoelectric Sensor.

In this study, we developed a novel heart rate (HR) monitoring approach in which we measure the pressure variance of the surface of the ear canal. A scissor-shaped apparatus equipped with a piezoelectric film sensor and a hardware circuit module was designed for high wearability and to obtain stable measurement. In the proposed device, the film sensor converts in-ear pulse waves (EPW) into electrical current, and the circuit module enhances the EPW and suppresses noise. A real-time algorithm embedded in the circuit module performs morphological conversions to make the EPW more distinct and knowledge-based rules are used to detect EPW peaks. In a clinical experiment conducted using a reference electrocardiogram (ECG) device, EPW and ECG were concurrently recorded from 58 healthy subjects. The EPW intervals between successive peaks and their corresponding ECG intervals were then compared to each other. Promising results were obtained from the samples, specifically, a sensitivity of 97.25%, positive predictive value of 97.17%, and mean absolute difference of 0.62. Thus, highly accurate HR was obtained from in-ear pressure variance. Consequently, we believe that our proposed approach could be used to monitor vital signs and also utilized in diverse applications in the near future.

Enhancing the pulse contour analysis-based arterial stiffness estimation using a novel photoplethysmographic parameter.

In this paper, we propose a novel method for enhancing pulse contour analysis-based arterial stiffness estimation using a simple and low-complexity photoplethysmographic parameter (P2Ocd). The method first eliminates baseline wanders in the digital volume pulse (DVP) by applying a simple morphological filter. The filtered DVP signal is then transformed into a slope sum function signal to simplify the pulse peak detection process by enhancing the upslope of the DVP signal while suppressing its downslope. An adaptive thresholding scheme is applied to detect pulse peaks from the transformed signal. Pulse onsets are then identified as the minimum values between consecutive pulse peaks. The P2Ocd is finally calculated by dividing the time interval between the pulse peak and the pulse onset by the pulse length. In order to assess the agreement of the P2Ocd with an established technique, brachial-ankle pulse wave velocity, we performed Bland-Altman and correlation analyses. Furthermore, we evaluated the P2Ocd-based arterial stiffness estimation in terms of prediction accuracy (% error rate) and repeatability (coefficient of variation). The results show that the proposed measurement agrees well with the established technique and shows a high repeatability; it also has a better predictive accuracy than that of conventional methods. In addition, we show that the proposed parameter further improves the predictive accuracy by combining it with age. The proposed method is therefore highly applicable to small ubiquitous healthcare applications.

A robust method for pulse peak determination in a digital volume pulse waveform with a wandering baseline.

This paper presents a robust method for pulse peak determination in a digital volume pulse (DVP) waveform with a wandering baseline. A proposed new method uses a modified morphological filter (MMF) to eliminate a wandering baseline signal of the DVP signal with minimum distortion and a slope sum function (SSF) with an adaptive thresholding scheme to detect pulse peaks from the baseline-removed DVP signal. Further in order to cope with over-detected and missed pulse peaks, knowledge based rules are applied as a postprocessor. The algorithm automatically adjusts detection parameters periodically to adapt to varying beat morphologies and fluctuations. Compared with conventional methods (highpass filtering, linear interpolation, cubic spline interpolation, and wavelet adaptive filtering), our method performs better in terms of the signal-to-error ratio, the computational burden (0.125 seconds for one minute of DVP signal analysis with the Intel Core 2 Quad processor @ 2.40 GHz PC), the true detection rate (97.32% with an acceptance level of 4 ms ) as well as the normalized error rate (0.18%). In addition, the proposed method can detect true positions of pulse peaks more accurately and becomes very useful for pulse transit time (PTT) and pulse rate variability (PRV) analyses.

A knowledge-based approach to arterial stiffness estimation using the digital volume pulse.

We have developed a knowledge based approach for arterial stiffness estimation. The proposed new approach reliably estimates arterial stiffness based on the analysis of age and heart rate normalized reflected wave arrival time. The proposed new approach reduces cost, space, technical expertise, specialized equipment, complexity, and increases the usability compared to recently researched noninvasive arterial stiffness estimators. The proposed method consists of two main stages: pulse feature extraction and linear regression analysis. The new approach extracts the pulse features and establishes a linear prediction equation. On evaluating proposed methodology with pulse wave velocity (PWV) based arterial stiffness estimators, the proposed methodology offered the error rate of 8.36% for men and 9.52% for women, respectively. With such low error rates and increased benefits, the proposed approach could be usefully applied as low cost and effective solution for ubiquitous and home healthcare environments.

Mental health promotion system.

Mental activity promotion system is presented that analyzes, quantifies, trains and prescribes based on analysis of logical, memorizing, concentrative, decisive, in conditions where time and space is involved, possibility of dementia, and on evaluation of lifestyle of subjects. Special consideration has been made to make the system motivational, persuasive, attractive and fun to use. The system has been successfully deployed in Bitgeoul Senior health town, Gwangju, South Korea.

PPG delineator for real-time ubiquitous applications.

This paper presents real-time signal processing algorithm for detection of onsets and peaks in Photoplethysmogram (PPG) waveform. Algorithm relies on the analysis of amplitude, slope and inter-beat intervals. The presented algorithm consists of four stages for characterizing PPG waveform. Preprocessing stage involves transformation of PPG since the original waveform is less impulsive and robust. In second stage, algorithm seeks for valid pulse detection in transformed signal complying with the amplitude threshold and inter-beat interval. On detection of valid pulses, algorithm then searches backward and forward in transformed signal for the detection of peaks and onsets. Further the detection parameters are made adaptive to comply with varying beat morphologies and fluctuations in baseline. All signal processing steps and decision logics are implemented with low computational complexity to make it applicable for compact ubiquitous health monitoring devices. On evaluation with our database, the algorithm achieved sensitivity of 96.89% and positive predictivity of 94.55% within an acceptance level of 12 ms.