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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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.

Wearable devices for body-status monitoring require various signal processing such as ambient noise filtering and signal quality evaluation. While wearable devices are very limited in resource, conventional noise filtering and signal quality evaluating methods consume considerable amount of processing power. Moreover, these conventional methods are not suitable for processing bio-signals in terms of its performance. In this paper, we propose a novel method of preprocessing bio-signals. This preprocessing method includes distortionless noise filtering and signal quality estimation, where both parts are basically based on a simple combination of multiple low pass IIR filters.