  This study explores extended feature space that is indicative of blood
pressure (BP) changes for better estimation of continuous BP in an unobtrusive
way. A total of 222 features were extracted from noninvasively acquired
electrocardiogram (ECG) and photoplethysmogram (PPG) signals with the subject
undergoing coronary angiography and/or percutaneous coronary intervention,
during which intra-arterial BP was recorded simultaneously with the subject at
rest and while administering drugs to induce BP variations. The association
between the extracted features and the BP components, i.e. systolic BP (SBP),
diastolic BP (DBP), mean BP (MBP), and pulse pressure (PP) were analyzed and
evaluated in terms of correlation coefficient, cross sample entropy, and mutual
information, respectively. Results show that the most relevant indicator for
both SBP and MBP is the pulse full width at half maximum, and for DBP and PP,
the amplitude between the peak of the first derivative of PPG (dPPG) to the
valley of the second derivative of PPG (sdPPG) and the time interval between
the peak of R wave and the sdPPG, respectively. As potential inputs to either
the knowledge-guided model or data-driven method for cuffless BP calibration,
the proposed expanded features are expected to improve the estimation accuracy
of cuffless BP.
