An important means for preventing and managing cardiovascular disease is the non-invasive estimation of blood pressure. There is particular interest in developing approaches that provide accurate cuffless and continuous estimation of this important vital sign. This paper proposes a method that uses dynamic changes of the pulse waveform over short time intervals and calibrates the system based on a mathematical model that relates reflective PTT (R-PTT) to blood pressure. An advantage of the method is that it only requires collecting the photoplethysmogram (PPG) using one optical sensor, in addition to initial non-invasive measurements of blood pressure that are used for calibration. This method was applied to data from 30 patients, resulting in a mean error (ME) of 0.59 mmHg, a standard deviation of error (SDE) of 7.07 mmHg, and a mean absolute error (MAE) of 4.92 mmHg for diastolic blood pressure (DBP) and an ME of 2.52 mmHg, an SDE of 12.15 mmHg, and an MAE of 8.89 mmHg for systolic blood pressure (SBP). These results demonstrate the possibility of using the PPG signal for the cuffless continuous estimation of blood pressure based on the analysis of calibrated changes in cardiovascular dynamics, possibly in conjunction with other methods that are currently being researched.

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.

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.

The objective of this study is to investigate automatic recognition of speech-evoked auditory brainstem responses (speech-evoked ABR) to the five English vowels (/a/, /ae/, /ao (ɔ)/, /i/ and /u/). We used different automatic speech recognition methods to discriminate between the responses to the vowels. The best recognition result was obtained by applying principal component analysis (PCA) on the amplitudes of the first ten harmonic components of the envelope following response (based on spectral components at fundamental frequency and its harmonics) and of the frequency following response (based on spectral components in first formant region) and combining these two feature sets. With this combined feature set used as input to an artificial neural network, a recognition accuracy of 83.8% was achieved. This study could be extended to more complex stimuli to improve assessment of the auditory system for speech communication in hearing impaired individuals, and potentially help in the objective fitting of hearing aids.iii