Smart watches in future will have smart wristband. This work analyses properties of new developed capacitive wristband sensor that measures ballistocardiogram (BCG) from single point on the wrist. In addition, it considers applications of this sensor to monitoring heart rate variability. Another application is in estimating changes (trend) in systolic blood pressure continuously when combined with lead one electrocardiogram (ECG). BP is one of the vital signs that indicates the health condition. It is commonly measured by cuff-based monitor using either auscultatory or oscillometric method. Cuff-based BP monitor is not portable and unable to measure BP continuously which means it is difficult to attach BP monitoring function on a wearable device. Significant research is conducted in estimating BP from pulse transit time (PTT) mathematically which would enable the cuffless BP measurement. In this work, a new time reference, RJ interval, which is the time delay between ECG and BCG signal peaks was tested whether it can be used as a surrogate of PTT in cuffless BP estimation. Based on the study done on 10 healthy people, it was shown that RJ intervals can be useful in evaluating trends of systolic blood pressure.

Blood pressure (BP) monitoring is an important tool for management of hypertension, which is a significant risk for cardiovascular disease and premature death. Since cuff-based BP measurement can be uncomfortable and does not provide continuous readings, several cuffless methods that are typically based on within-beat information or on the pulse transit time (PTT) have recently been investigated. This work proposes a novel cuffless BP estimation approach that mainly uses the information from cardiovascular dynamics of photoplethysmogram (PPG) waveforms. This work is divided into three parts. The first part proposes a calibration-free approach that uses dynamic changes in the pulse waveform. Results from 200 patients showed that the method achieved grade B, in terms of accuracy, for diastolic blood pressure (DBP) based on the British Hypertension Society (BHS) standard and complied with the accuracy requirements of the Association for Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) standard. The second part presents a method based on calibrated cardiovascular dynamics, achieved through a mathematical model that relates reflective PTT (R-PTT) to BP. Results from 30 patients showed a mean error (ME) of 0.58 mmHg, standard deviation of the error (SDE) of 8.13 mmHg, and a mean absolute error (MAE) of 4.93 mmHg for DBP and an ME of 2.52 mmHg, SDE of 12.28 mmHg, and an MAE of 8.82 mmHg for systolic blood pressure (SBP). The third part proposes a calibration-free method that combines morphology features and dynamic changes of the pulse waveform over short intervals. In this method a neural network was trained on 200 patients and tested on never-seen data from 25 other patients and provided an ME of -0.31 mmHg, SDE of 4.89 mmHg, and MAE of 3.32 mmHg for DBP and an ME of -4.02 mmHg, SDE of 10.40 mmHg, and MAE of 7.41 mmHg for SBP. Overall, the results show that cardiovascular dynamics may contribute useful information for cuffless estimation of BP.

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Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly.