Non-invasive estimation of static and pulsatile intracranial pressure from transcranial acoustic signals.

The aim of the present study was to examine whether a method for estimation of non-invasive ICP (nICP) from transcranial acoustic (TCA) signals mixed with head-generated sounds estimate the static and pulsatile invasive ICP (iICP). For that purpose, simultaneous iICP and mixed TCA signals were obtained from patients undergoing continuous iICP monitoring as part of clinical management. The ear probe placed in the right outer ear channel sent a TCA signal with fixed frequency (621 Hz) that was picked up by the left ear probe along with acoustic signals generated by the intracranial compartment. Based on a mathematical model of the association between mixed TCA and iICP, the static and pulsatile nICP values were determined. Total 39 patients were included in the study; the total number of observations for prediction of static and pulsatile iICP were 5789 and 6791, respectively. The results demonstrated a good agreement between iICP/nICP observations, with mean difference of 0.39 mmHg and 0.53 mmHg for static and pulsatile ICP, respectively. In summary, in this cohort of patients, mixed TCA signals estimated the static and pulsatile iICP with rather good accuracy. Further studies are required to validate whether mixed TCA signals may become useful for measurement of nICP.

Estimation of left ventricular function using a novel acoustic-based device.

BACKGROUND: We examined the feasibility of estimating left ventricular ejection fraction (LVEF) by a novel acoustic-based device [vibration response imaging (VRI); Deep Breeze]. METHODS: One hundred and forty-one subjects (117 patients and 24 healthy volunteers; age 55 ± 15 years, 82% men) were examined by both VRI and echocardiography. LVEF was determined by echocardiography (echo-LVEF) using the biplane Simpson's method. Low-frequency acoustic signals (10-70 Hz) were recorded by VRI from the left posterior thorax by a matrix of 36 microphones during 8 s of breath holding, and an electrocardiogram was recorded simultaneously. The acoustic signals were processed digitally, and an algorithm designed to estimate LVEF was developed (VRI-LVEF), based on a combination of multiple acoustic (systolic and diastolic acoustic signals, beat-to-beat variability of acoustic signals and propagation of acoustic signals throughout the matrix), electrocardiographic and clinical parameters. RESULTS: Mean echo-LVEF was 51 ± 15% (range, 11-76%). Echo-LVEF was reduced (< 50%) in 55 subjects (39%) and severely reduced (< 35%) in 28 subjects (20%). VRI-LVEF calculated by a multivariate algorithm correlated significantly with echo-LVEF (R(2) = 0·59; P < 0·001). VRI-LVEF accurately predicted the presence of reduced (< 50%) or severely reduced (< 35%) echo-LVEF, with sensitivities of 84% and 82%, specificities of 86% and 91%, positive predictive values of 79% and 70% and negative predictive values of 89% and 95%, respectively. CONCLUSIONS: LVEF can be estimated using a novel acoustic-based device. This device may assist in triage of patients according to LVEF prior to definitive assessment of LVEF by echocardiography.

Detecting REM sleep from the finger: an automatic REM sleep algorithm based on peripheral arterial tone (PAT) and actigraphy.

Scoring of REM sleep based on polysomnographic recordings is a laborious and time-consuming process. The growing number of ambulatory devices designed for cost-effective home-based diagnostic sleep recordings necessitates the development of a reliable automatic REM sleep detection algorithm that is not based on the traditional electroencephalographic, electrooccolographic and electromyographic recordings trio. This paper presents an automatic REM detection algorithm based on the peripheral arterial tone (PAT) signal and actigraphy which are recorded with an ambulatory wrist-worn device (Watch-PAT100). The PAT signal is a measure of the pulsatile volume changes at the finger tip reflecting sympathetic tone variations. The algorithm was developed using a training set of 30 patients recorded simultaneously with polysomnography and Watch-PAT100. Sleep records were divided into 5 min intervals and two time series were constructed from the PAT amplitudes and PAT-derived inter-pulse periods in each interval. A prediction function based on 16 features extracted from the above time series that determines the likelihood of detecting a REM epoch was developed. The coefficients of the prediction function were determined using a genetic algorithm (GA) optimizing process tuned to maximize a price function depending on the sensitivity, specificity and agreement of the algorithm in comparison with the gold standard of polysomnographic manual scoring. Based on a separate validation set of 30 patients overall sensitivity, specificity and agreement of the automatic algorithm to identify standard 30 s epochs of REM sleep were 78%, 92%, 89%, respectively. Deploying this REM detection algorithm in a wrist worn device could be very useful for unattended ambulatory sleep monitoring. The innovative method of optimization using a genetic algorithm has been proven to yield robust results in the validation set.