A PPG-Based Calibration-Free Cuffless Blood Pressure Estimation Method Using Cardiovascular Dynamics

Traditional cuff-based sphygmomanometers for measuring blood pressure can be uncomfortable and particularly unsuitable to use during sleep. A proposed alternative method uses dynamic changes in the pulse waveform over short intervals and replaces calibration with information from photoplethysmogram (PPG) morphology to provide a calibration-free approach using a single sensor. Results from 30 patients show a high correlation of 73.64% for systolic blood pressure (SBP) and 77.72% for diastolic blood pressure (DBP) between blood pressure estimated with the PPG morphology features and the calibration method. This suggests that the PPG morphology features could replace the calibration stage for a calibration-free method with similar accuracy. Applying the proposed methodology on 200 patients and testing on 25 new patients resulted in a mean error (ME) of -0.31 mmHg, a standard deviation of error (SDE) of 4.89 mmHg, a mean absolute error (MAE) of 3.32 mmHg for DBP and an ME of -4.02 mmHg, an SDE of 10.40 mmHg, and an MAE of 7.41 mmHg for SBP. These results support the potential for using a PPG signal for calibration-free cuffless blood pressure estimation and improving accuracy by adding information from cardiovascular dynamics to different methods in the cuffless blood pressure monitoring field.

Combining Gaussian Process with Hybrid Optimal Feature Decision in Cuffless Blood Pressure Estimation

Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodology that combines the Gaussian process with hybrid optimal feature decision (HOFD) in cuffless blood pressure estimation. First, we can choose one of the feature selection methods: robust neighbor component analysis (RNCA), minimum redundancy, maximum relevance (MRMR), and F-test, based on the proposed hybrid optimal feature decision. After that, a filter-based RNCA algorithm uses the training dataset to obtain weighted functions by minimizing the loss function. Next, we combine the Gaussian process (GP) algorithm as the evaluation criteria, which is used to determine the best feature subset. Hence, combining GP with HOFD leads to an effective feature selection process. The proposed combining Gaussian process with the RNCA algorithm shows that the root mean square errors (RMSEs) for the SBP (10.75 mmHg) and DBP (8.02 mmHg) are lower than those of the conventional algorithms. The experimental results represent that the proposed algorithm is very effective.

Blood pressure estimation system using human body communication-based electrocardiograph and photoplethysmography

In order to realise low-load cuffless and continuous blood pressure measurement in daily life, the authors developed a blood pressure estimation system combining human body communication-based wearable electrocardiograph and reflectance photoplethysmography. The principle is based on a relationship between the pulse arrive time and the systolic blood pressure. The pulse arrive time is the time period between the R-wave in electrocardiograph and peak of pulse wave. The greatest feature is the use of a human body communication-based electrocardiograph which can provide automatic synchronisation in time between the measured electrocardiograph and pulse wave signals to obtain the pulse arrive time so that no additional synchronisation circuit is required. Using this system, the authors measured the pulse arrive time from the electrocardiograph and pulse wave signals in real time, estimated the systolic blood pressure and compared the result with that measured by a cuff sphygmomanometer. The authors found that the root mean square error of the estimated blood pressure and the actual value measured using the cuff sphygmomanometer was 4.5 mmHg or less, and the correlation coefficient was >0.6 with a P value much <0.05. These results show the validity of the developed system for cuffless and continuous blood pressure estimation.