Quantification of Nocturnal Blood Pressure Oscillations Induced by Sleep Disordered Breathing.

We present an approach to quantifying nocturnal blood pressure (BP) variations that are elicited by sleep disordered breathing (SDB). A sample-by-sample aggregation of the dynamic BP variations during normal breathing and BP oscillations prompted by apnea episodes is performed. This approach facilitates visualization and analysis of BP oscillations. Preliminary results from analysis of a full night study of 7 SDB subjects (5 Male 2 Female, 52±5.6 yrs., Body Mass Index 36.4±7.4 kg/m2, Apnea-Hypopnea Index 69.1±26.8) are presented. Aggregate trajectory and quantitative values for changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP) concomitant with obstructive apnea episodes are presented. The results show 19.4 mmHg (15.3%) surge in SBP and 9.4 mmHg (13.6%) surge in DBP compared to their respective values during normal breathing (p<0.05). Further, the peak of the surge in SBP and DBP occurred about 9s and 7s, respectively, post the end of apnea events. The return of SBP and DBP to baseline values displays a decaying oscillatory pattern.

Dynamic Estimation of Cerebral Blood Flow Using Blood Pressure Signal in sleep Apnea Patients.

Monitoring apnea-induced cerebral blood flow (CBF) oscillations is of importance for assessing apnea patient brain health. Blood pressure (BP) oscillations during apnea can induce oscillations in CBF. Preliminary results of testing an Auto Regressive Moving Average model relating nocturnal CBP oscillations to nocturnal BP variations in 8 obstructive sleep apnea subjects (3 F, 55±8 yrs., BMI 34.2±7.85 kg/m2) showed that largest mean and standard deviation of the CBF estimation errors was 4.49±7.57 cm/s and maximum root mean squared of the errors was 8.80 cm/s. Hence, reasonable accuracy in estimating CBF from BP during sleep apnea events was observed.

Dynamic Estimation of Cerebral Blood Flow Using Photoplethysmography Signal during Simulated Apnea.

Monitoring apnea-induced cerebral blood flow oscillations is of importance for assessing apnea patient brain health. Using an autoregressive moving average model, peak and trough values of cerebral blood flow were estimated from a concurrently recorded forehead photoplethysmography signal. Preliminary testing of the method in 7 subjects (4 F, 32±4 yrs., BMI 24.57±3.87 kg/m2) using a breath hold paradigm for simulating apnea shows that maximum mean and standard deviation of the prediction error is -1.10±8.49 cm/s and the maximum root mean squared of the error is 8.92 cm/s.

Arterial blood pressure feature estimation using photoplethysmography.

Continuous and noninvasive monitoring of blood pressure has numerous clinical and fitness applications. Current methods of continuous measurement of blood pressure are either invasive and/or require expensive equipment. Therefore, we investigated a new method for the continuous estimation of two main features of blood pressure waveform: systolic and diastolic pressures. The estimates were obtained from a photoplethysmography signal as input to the fifth order autoregressive moving average models. The performance of the method was evaluated using beat-to-beat full-wave blood pressure measurements from 15 young subjects, with no known cardiovascular disorder, in supine position as they breathed normally and also while they performed a breath-hold maneuver. The level of error in the modeling and prediction estimates during normal breathing and breath-hold maneuvers, as measured by the root mean square of the residuals, were less than 5 mmHg and 11 mm Hg, respectively. The mean of model residuals both during normal breathing and breath-hold maneuvers was considered to be less than 3.2 mmHg. The dependency of the accuracy of the estimates on the subject data was assessed by comparing the modeling errors for the 15 subjects. Less than 1% of the models showed significant differences (p < 0.05) from the other models, which indicates a high level of consistency among the models.