Early heart rate variability evaluation enables to predict ICU patients' outcome.

Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has been proposed in various types of patients. We attempted to evaluate the best determinants of such variation in survival prediction using a physiological data-warehousing program. Plethysmogram tracings (PPG) were recorded at 75 Hz from the standard monitoring system, for a 2 h period, during the 24 h following ICU admission.

Photoplethysmographic determination of the respiratory rate in acutely ill patients: validation of a new algorithm and implementation into a biomedical device.

Background: Respiratory rate is among the first vital signs to change in deteriorating patients. The aims of this study were to evaluate the accuracy of respiratory rate measurements using a specifically dedicated reflection-mode photoplethysmographic signal analysis in a pathological condition (PPG-RR) and to validate its implementation within medical devices.

Methods: This study is derived from a data mining project, including all consecutive patients admitted to our ICU (ReaSTOC study, ClinicalTrials.

Detection of artifacts on photoplethysmography signals using random distortion testing.

In this work, we describe a novel method based on waveform morphology for detecting artifacts in photoplethysmography (PPG) signals and, thus, improve reliability of PPG. By considering inter-individual and measure condition variability, specific parameters are estimated for each record. We introduce a novel metric for comparing pulses, which is the derivative of the correlation coefficient.

Mechanical ventilation system monitoring: automatic detection of dynamic hyperinflation and asynchrony.

Automatic monitoring of mechanical ventilation system becomes more and more important with respect to the number of patients per clinician. In this paper, the automatic detections of dynamic hyperinflation (PEEPi) and asynchrony in a monitoring framework are considered. The proposed detection methods are based on a robust non-parametric hypothesis testing, namely Random Distortion Testing (RDT), that requires no prior information on the signal distribution.

Automatic detection of AutoPEEP during controlled mechanical ventilation.

Background: Dynamic hyperinflation, hereafter called AutoPEEP (auto-positive end expiratory pressure) with some slight language abuse, is a frequent deleterious phenomenon in patients undergoing mechanical ventilation. Although not readily quantifiable, AutoPEEP can be recognized on the expiratory portion of the flow waveform. If expiratory flow does not return to zero before the next inspiration, AutoPEEP is present.

Protein interaction hotspot identification using sequence-based frequency-derived features.

Finding good descriptors, capable of discriminating hotspot residues from others, is still a challenge in many attempts to understand protein interaction. In this paper, descriptors issued from the analysis of amino acid sequences using digital signal processing (DSP) techniques are shown to be as good as those derived from protein tertiary structure and/or information on the complex. The simulation results show that our descriptors can be used separately to predict hotspots, via a random forest classifier, with an accuracy of 79% and a precision of 75%.