Monitoring the nociception level: a multi-parameter approach.

The aim of the present study was to develop and validate an objective index for nociception level (NoL) of patients under general anesthesia, based on a combination of multiple physiological parameters. Twenty-five patients scheduled for elective surgery were enrolled. For clinical reference of NoL, the combined index of stimulus and analgesia was defined as a composite of the surgical stimulus level and a scaled effect-site concentration of opioid. The physiological parameters heart rate, heart rate variability (0.15-0.4 Hz band power), plethysmograph wave amplitude, skin conductance level, number of skin conductance fluctuations, and their time derivatives, were extracted. Two techniques to incorporate these parameters into a single index representing the NoL have been proposed: NoLlinear, based on an ordinary linear regression, and NoLnon-linear, based on a non-linear Random Forest regression. NoLlinear and NoLnon-linear significantly increased after moderate to severe noxious stimuli (Wilcoxon rank test, p < 0.01), while the individual parameters only partially responded. Receiver operating curve analysis showed that NoL index based on both techniques better discriminated noxious and non-noxious surgical events [area under curve (AUC) = 0.97] compared with individual parameters (AUC = 0.56-0.74). NoLnon-linear better ranked the level of nociception compared with NoLlinear (R = 0.88 vs. 0.77, p < 0.01). These results demonstrate the superiority of multi-parametric approach over any individual parameter in the evaluation of nociceptive response. In addition, advanced non-linear technique may have an advantage over ordinary linear regression for computing NoL index. Further research will define the usability of the NoL index as a clinical tool to assess the level of nociception during general anesthesia.

Obstructive apnea hypopnea index estimation by analysis of nocturnal snoring signals in adults.

STUDY OBJECTIVE: To develop a whole-night snore sounds analysis algorithm enabling estimation of obstructive apnea hypopnea index (AHI(EST)) among adult subjects. DESIGN: Snore sounds were recorded using a directional condenser microphone placed 1 m above the bed. Acoustic features exploring intra-(mel- cepstability, pitch density) and inter-(running variance, apnea phase ratio, inter-event silence) snore properties were extracted and integrated to assess AHI(EST). SETTING: University-affiliated sleep-wake disorder center and biomedical signal processing laboratory. PATIENTS: Ninety subjects (age 53 ± 13 years, BMI 31 ± 5 kg/m(2)) referred for polysomnography (PSG) diagnosis of OSA were prospectively and consecutively recruited. The system was trained and tested on 60 subjects. Validation was blindly performed on the additional 30 consecutive subjects. MEASUREMENTS AND RESULTS: AHI(EST) correlated with AHI (AHI(PSG); r(2) = 0.81, P < 0.001). Area under the receiver operating characteristic curve of 85% and 92% for thresholds of 10 and 20 events/h, respectively, were obtained for OSA detection. Both Altman-Bland analysis and diagnostic agreement criteria revealed 80% and 83% agreements of AHI(EST) with AHI(PSG), respectively. CONCLUSIONS: Acoustic analysis based on intra- and inter-snore properties can differentiate subjects according to AHI. An acoustic-based screening system may address the growing needs for reliable OSA screening tool. Further studies are needed to support these findings.

Nocturnal sound analysis for the diagnosis of obstructive sleep apnea.

A novel method for screening obstructive sleep apnea syndrome (OSAs) based on nocturnal acoustic signal is proposed. Full-night audio signals from sixty subjects were segmented into snore, noise and silence events using semi-automatic algorithm based on Gaussian mixture models which achieves more than 90% (92%) sensitivity (specificity) and produces an average of 2,000 snores per subject. A classification into 3 groups is proposed for the diagnosis: comparison group - non-OSA subjects (apnea hypopnea index, AHI < 10), mild to moderate OSA (10 < AHI < 30) and severe OSA (AHI>30). A Bayes classifier was implemented, fed with five acoustic features, all correlated with the severity of the syndrome: (1) Inter Event Silence, which quantifies segments suspicious as apnea; (2) Mel Cepstability, measures the entire night stability of the spectrum, expressed using mel-frequency cepstrum; (3) Energy Running Variance, a criterion for the variation of the nocturnal acoustic pattern; (4) Apneic Phase Ratio, exploiting the finding that snores around apnea events expressing larger acoustic variation; and (5) Pitch Density. Correct classification of 92% for resubstitution method and 80% for 5-fold cross validation method was achieved. Moreover, in a case of two groups with a threshold of AHI=10, a sensitivity (specificity) of 96.5% (90.6%) and 87.5% (82.1%) for resubstitution and cross-validation respectively were obtained.