Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table

Optimising the assessment of cerebral autoregulation from black box models

Cerebral autoregulation (CA) mechanisms maintain blood flow approximately stable despite changes in arterial blood pressure. Mathematical models that characterise this system have been used extensively in the quantitative assessment of function/impairment of CA. Using spontaneous fluctuations in arterial blood pressure (ABP) as input and cerebral blood flow velocity (CBFV) as output, the autoregulatory mechanism can be modelled using linear and non-linear approaches, from which indexes can be extracted to provide an overall assessment of CA. Previous studies have considered a single – or at most a couple of measures, making it difficult to compare the performance of different CA parameters. We compare the performance of established autoregulatory parameters and propose novel measures. The key objective is to identify which model and index can best distinguish between normal and impaired CA. To this end 26 recordings of ABP and CBFV from normocapnia and hypercapnia (which temporarily impairs CA) in 13 healthy adults were analysed. In the absence of a ‘gold’ standard for the study of dynamic CA, lower inter- and intra-subject variability of the parameters in relation to the difference between normo- and hypercapnia were considered as criteria for identifying improved measures of CA. Significantly improved performance compared to some conventional approaches was achieved, with the simplest method emerging as probably the most promising for future studies

Emotional modulation of visual cortex activity: A functional nearinfrared spectroscopy study

Functional neuroimaging and electroencephalography reveal emotional effects in early visual cortex. Here, we used fNIRS to examine haemodynamic responses evoked by neutral, positive and negative emotional pictures, matched for brightness, contrast, hue, saturation, spatial frequency and entropy. Emotion content modulated amplitude and latency of oxy-, deoxy- and total haemoglobin response peaks, and induced peripheral autonomic reactions. The processing of positive and negative pictures enhanced haemodynamic response amplitude, and this effect was paralleled by blood pressure changes. The processing of positive pictures was reflected in reduced haemodynamic response peak latency. Together these data suggest early visual cortex holds amplitude-dependent representation of stimulus salience and latency-dependent information regarding stimulus valence, providing new insight into affective interaction with sensory processing

Characterization of the microvascular cerebral blood flow response to obstructive apneic events during night sleep

Peer ReviewedPostprint (author's final draft

Modelling arterial pressure waveforms using Gaussian functions and two-stage particle swarm optimizer

Changes of arterial pressure waveform characteristics have been accepted as risk indicators of cardiovascular diseases. Waveform modelling using Gaussian functions has been used to decompose arterial pressure pulses into different numbers of subwaves and hence quantify waveform characteristics. However, the fitting accuracy and computation efficiency of current modelling approaches need to be improved. This study aimed to develop a novel two-stage particle swarm optimizer (TSPSO) to determine optimal parameters of Gaussian functions. The evaluation was performed on carotid and radial artery pressure waveforms (CAPW and RAPW) which were simultaneously recorded from twenty normal volunteers. The fitting accuracy and calculation efficiency of our TSPSO were compared with three published optimization methods: the Nelder-Mead, the modified PSO (MPSO), and the dynamic multiswarm particle swarm optimizer (DMS-PSO). The results showed that TSPSO achieved the best fitting accuracy with a mean absolute error (MAE) of 1.1% for CAPW and 1.0% for RAPW, in comparison with 4.2% and 4.1% for Nelder-Mead, 2.0% and 1.9% for MPSO, and 1.2% and 1.1% for DMS-PSO. In addition, to achieve target MAE of 2.0%, the computation time of TSPSO was only 1.5 s, which was only 20% and 30% of that for MPSO and DMS-PSO, respectively

Adaptive feedback analysis and control of programmable stimuli for assessment of cerebrovascular function

The assessment of cerebrovascular regulatory mechanisms often requires flexibly controlled and precisely timed changes in arterial blood pressure (ABP) and/or inspired CO2. In this study, a new system for inducing variations in mean ABP was designed, implemented and tested using programmable sequences and programmable controls to induce pressure changes through bilateral thigh cuffs. The system is also integrated with a computer-controlled switch to select air or a CO2/air mixture to be provided via a face mask. Adaptive feedback control of a pressure generator was required to meet stringent specifications for fast changes, and accuracy in timing and pressure levels applied by the thigh cuffs. The implemented system consists of a PC-based signal analysis/control unit, a pressure control unit and a CO2/air control unit. Initial evaluations were carried out to compare the cuff pressure control performances between adaptive and non-adaptive control configurations. Results show that the adaptive control method can reduce the mean error in sustaining target pressure by 99.57 % and reduce the transient time in pressure increases by 45.21 %. The system has proven a highly effective tool in ongoing research on brain blood flow control

Effects of novel muscarinic M3 receptor ligand C1213 in pulmonary arterial hypertension models.

Pulmonary hypertension (PH) is a complex disease comprising a pathologic remodeling and thickening of the pulmonary vessels causing an after load on the right heart ventricle that can result in ventricular failure. Triggered by oxidative stress, episodes of hypoxia, and other undetermined causes, PH is associated with poor outcomes and a high rate of morbidity. In the neonate, this disease has a similar etiology but is further complicated by the transition to breathing after birth, which requires a reduction in vascular resistance. Persistent pulmonary hypertension of the newborn (PPHN) is one form of PH that is frequently unresponsive to current therapies including inhaled nitric oxide (due to lack of proper absorption and diffusion), and other therapeutics targeting signaling mediators in vascular endothelium and smooth muscle. The need for novel agents, which target distinct pathways in pulmonary hypertension, remains. Herein, we investigated the therapeutic effects of novel muscarinic receptor ligand C1213 in models of PH We demonstrated that via M3 muscarinic receptors, C1213 induced activating- eNOS phosphorylation (serine-1177), which is known to lead to nitric oxide (NO) production in endothelial cells. Using signaling pathway inhibitors, we discovered that AKT and calcium signaling contributed to eNOS phosphorylation induced by C1213. As expected for an eNOS-stimulating agent, in ex vivo and in vivo models, C1213 triggered pulmonary vasodilation and induced both pulmonary artery and systemic blood pressure reductions demonstrating its potential value in PH and PPHN In brief, this proof-of-concept study provides evidence that an M3 muscarinic receptor functionally selective ligand stimulates downstream pathways leading to antihypertensive effects using in vitro, ex vivo, and in vivo models of PH

Dissociation of Cerebral Blood Flow and Femoral Artery Blood Pressure Pulsatility After Cardiac Arrest and Resuscitation in a Rodent Model: Implications for Neurological Recovery.

Background Impaired neurological function affects 85% to 90% of cardiac arrest (CA) survivors. Pulsatile blood flow may play an important role in neurological recovery after CA. Cerebral blood flow (CBF) pulsatility immediately, during, and after CA and resuscitation has not been investigated. We characterized the effects of asphyxial CA on short-term (<2 hours after CA) CBF and femoral arterial blood pressure (ABP) pulsatility and studied their relationship to cerebrovascular resistance (CVR) and short-term neuroelectrical recovery. Methods and Results Male rats underwent asphyxial CA followed by cardiopulmonary resuscitation. A multimodal platform combining laser speckle imaging, ABP, and electroencephalography to monitor CBF, peripheral blood pressure, and brain electrophysiology, respectively, was used. CBF and ABP pulsatility and CVR were assessed during baseline, CA, and multiple time points after resuscitation. Neuroelectrical recovery, a surrogate for neurological outcome, was assessed using quantitative electroencephalography 90 minutes after resuscitation. We found that CBF pulsatility differs significantly from baseline at all experimental time points with sustained deficits during the 2 hours of postresuscitation monitoring, whereas ABP pulsatility was relatively unaffected. Alterations in CBF pulsatility were inversely correlated with changes in CVR, but ABP pulsatility had no association to CVR. Interestingly, despite small changes in ABP pulsatility, higher ABP pulsatility was associated with worse neuroelectrical recovery, whereas CBF pulsatility had no association. Conclusions Our results reveal, for the first time, that CBF pulsatility and CVR are significantly altered in the short-term postresuscitation period after CA. Nevertheless, higher ABP pulsatility appears to be inversely associated with neuroelectrical recovery, possibly caused by impaired cerebral autoregulation and/or more severe global cerebral ischemia