h i g h l i g h t sAn automated model-based spindle detection algorithm is proposed. It models the amplitude-frequency spindle distribution with a bivariate normal distribution. It automatically adapts to each individual subject and derivation. It was tested in seven healthy children and six adult patients suffering from different pathologies, and performs similarly or better than sleep experts. Normal modelling enhances spindle detection quality compared to fixed amplitude and frequency thresholds. t r a c tManual scoring of sleep spindles can be very time-consuming, and achieving accurate manual scoring on a long-term recording requires high and sustained levels of vigilance, which makes it a highly demanding task with the associated risk of decreased diagnosis accuracy. Although automatic spindle detection would be attractive, most available algorithms are sensitive to variations in spindle amplitude and frequency that occur between both subjects and derivations, reducing their effectiveness.We propose here an algorithm that models the amplitude-frequency spindle distribution with a bivariate normal distribution (one normal model per derivation). Subsequently, spindles are detected when their amplitude-frequency characteristics are included within a given tolerance interval of the corresponding model. As a consequence, spindle detection is not directly based on amplitude and frequency thresholds, but instead on a spindle distribution model that is automatically adapted to each individual subject and derivation.The algorithm was first assessed against the scoring of one sleep scoring expert on EEG samples from seven healthy children. Afterward, a second study compared performance of two additional experts versus the algorithm on a dataset of six EEG samples from adult patients suffering from different pathologies, to submit the method to more challenging and clinically realistic conditions. Smaller and shorter spindles were more difficult to evaluate, as false positives and false negatives showed lower amplitude and smaller length than true positives. In both studies, normal modelling enhanced performance compared to fixed amplitude and frequency thresholds. Normal modelling is therefore attractive, as it enhances spindle detection quality.

No abstract

Non-invasive remote detection of cardiac and blood displacements is an important topic in cardiac telemedicine. Here we propose kino-cardiography (KCG), a non-invasive technique based on measurement of body vibrations produced by myocardial contraction and blood flow through the cardiac chambers and major vessels. KCG is based on ballistocardiography and measures 12 degrees-of-freedom (DOF) of body motion. We tested the hypothesis that KCG reliably assesses dobutamine-induced haemodynamic changes in healthy subjects. Using a randomized double-blinded placebo-controlled crossover study design, dobutamine and placebo were infused to 34 volunteers (25 ± 2 years, BMI 22 ± 2 kg/m², 18 females). Baseline recordings were followed by 3 sessions of increasing doses of dobutamine (5, 10, 20 μg/kg.min) or saline solution. During each session, stroke volume (SV) and cardiac output (CO) were determined by echocardiography and followed by a 90 s KCG recording. Measured linear accelerations and angular velocities were used to compute total Kinetic energy (iK) and power (Pmax). KCG sorted dobutamine infusion vs. placebo with 96.9% accuracy. Increases in SV and CO were correlated to iK (r = +0.71 and r = +0.8, respectively, p < 0.0001). Kino-cardiography, with 12-DOF, allows detecting dobutamine-induced haemodynamic changes with a high accuracy and present a major improvement over single axis ballistocardiography or seismocardiography.

No abstract

Emerging evidence suggests that emotion and affect modulate the relation between sleep and cognition. In the present study, we investigated the role of rapid-eye movement (REM) sleep in mood regulation and memory consolidation for sad stories. In a counterbalanced design, participants (n = 24) listened to either a neutral or a sad story during two sessions, spaced one week apart. After listening to the story, half of the participants had a short (45 min) morning nap. The other half had a long (90 min) morning nap, richer in REM and N2 sleep. Story recall, mood evolution and changes in emotional response to the re-exposure to the story were assessed after the nap. Although recall performance was similar for sad and neutral stories irrespective of nap duration, sleep measures were correlated with recall performance in the sad story condition only. After the long nap, REM sleep density positively correlated with retrieval performance, while re-exposure to the sad story led to diminished mood and increased skin conductance levels. Our results suggest that REM sleep may not only be associated with the consolidation of intrinsically sad material, but also enhances mood reactivity, at least on the short term.