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We introduce a novel paradigm to unobtrusively and optically measure blood pressure (BP) without calibration. The algorithm combines photoplethysmography (PPG) waveform analysis and biometrics to estimate BP, and was evaluated in subjects with various age, height, weight and BP levels (n = 1249). In the young population (50 years old) with the same categories, the fitting errors are 12.8 ± 9.0, 0.5 ± 8.2 and −14.6 ± 11.5 mmHg for SBP respectively. A simple personalized calibration reduces fitting errors significantly (n = 147), and good peripheral perfusion helps to improve the fitting accuracy. In conclusion, PPG may be used to calculate BP without calibration in certain populations. When calibrated, it shows great potential to serially monitor BP fluctuation, which can bring tremendous economic and health benefits.

Objective. In this study, we aimed to estimate blood pressure (BP) from in-ear photoplethysmography (PPG). This novel implementation provided an unobtrusive and steady way of recording PPG, whereas previous PPG measurements were mostly performed at the wrist, finger, or earlobe.Methods. The time between forward and reflected PPG waves was very short at the ear site. To minimize errors introduced by feature extraction, a multi-Gaussian decomposition of in-ear PPG was performed. Both hand-crafted and whole-based features were extracted and the best combination of features was selected using a backward-search wrapper method and evaluated by the Akaike information criteria. Hemodynamic parameters such as compliance and inertance were estimated from a four-element Windkessel (WK4) model, which was used to pre-classify PPG signals and generate different BP estimation algorithms. Calibration was done by using previous measurements from the same class. To validate this novel approach, 53 subjects were recruited for a one-month follow-up study, and 17 subjects were recruited for a two-month follow-up study. Calibrated systolic BP estimation accuracy was significantly improved with inertance-based pre-classification, while diastolic BP showed less improvement.Results. With proper feature selection, pre-classification and calibration, we have achieved a mean absolute error of 5.35 mmHg for SBP estimation, compared to 6.16 mmHg if no pre-classification was carried out. The performance did not deteriorate in two months, showing a decent BP trend-tracking ability.Conclusion. The study demonstrated the feasibility of in-ear PPG to reliably measure BP, which represents an important technological advancement in terms of unobtrusiveness and steadiness.

Cadmium zinc telluride array detectors are known to have large pixel-to-pixel heterogeneity, which is undesirable in spectral computed tomography, and is hard to be theoretically modeled. To get better image quality and reduce the ring artifact, spectral calibration has to be done for each pixel to get uniform energy responses. Traditionally, multiple radionuclides with known characteristic emission peaks are used to calibrate the detectors. Heterogeneity of pixel responses can be compensated by electronically adjusting the gain and offset. This method is straightforward and simple, but radionuclides may not be readily available to many laboratories. In this paper, we present a method to calibrate the spectral response of each pixel with the broad spectrum features of commercial X-ray generators. For our system, the spectral response was empirically assumed to be Gaussian. A simple recursive fitting method was used to identify the intrinsic amplification and offset properties of each pixel. Gain and offset calibration were done accordingly. This method is robust, accurate, and fast, which is potentially applicable to other detectors with adaption.

Objective This work aims to develop an efficient and robust age-dependent multiple linear regression (MLR) model to estimate blood pressure (BP) from a single-source photoplethysmography (PPG) and biometrics, which could be embedded in the microcontroller of pulse oximeters. Approach Hemodynamic features were extracted from the PPG signal using its waveform, derivatives, and biometrics. Whole-based, feature-based, and fusion models were evaluated and compared for different age groups. Their performance was tested using 1086 subjects with a leave-one-subject-out cross-validation. The improvement by adding biometrics and the long-term calibration effect were investigated in detail. The relative importance of each feature was compared between different age groups and the implication was discussed. Main results The fusion model achieved the best performance in subjects with well-defined PPG features, whereas the feature-based method was better suited for subjects with damped signals. Adding age significantly improved both systolic BP (SBP) and diastolic BP (DBP) estimation accuracy for older subjects (> 50 years old) with well-defined features, while it only improved diastolic BP accuracy for older subjects with damped signals. For younger subjects (≤ 50 years old), the contribution of age was very small. A simple subtraction of subject-specific calibration factors significantly reduced biometric-related errors, which also improved the linearity of BP estimation. The relative importance analysis of input features suggests that separate models are indeed necessary for different age groups with different signal qualities, especially for DBP estimation in older subjects. Significance This study shows a reasonable BP estimation accuracy with age-dependent MLR models, which may help to equip current pulse oximeters with additional functionalities.