The use of time-of-flight camera to assess respiratory rates and thoracoabdominal depths in patients with chronic respiratory disease.

INTRODUCTION: Over the last 5 years, the analysis of respiratory patterns presents a growing usage in clinical and research purposes, but there is still currently a lack of easy-to-use and affordable devices to perform such kind of evaluation. OBJECTIVES: The aim of this study is to validate a new specifically developed method, based on Kinect sensor, to assess respiratory patterns against spirometry under various conditions. METHODS: One hundred and one participants took parts in one of the three validations studies. Twenty-five chronic respiratory disease patients (14 with chronic obstructive pulmonary disease (COPD) [65 ± 10 years old, FEV1 = 37 (15% predicted value), VC = 62 (20% predicted value)], and 11 with lung fibrosis (LF) [64 ± 14 years old, FEV1 = 55 (19% predicted value), VC = 62 (20% predicted value)]) and 76 healthy controls (HC) were recruited. The correlations between the signal of the Kinect (depth and respiratory rate) and the spirometer (tidal volume and respiratory rate) were computed in part 1. We then included 66 HC to test the ability of the system to detect modifications of respiratory patterns induced by various conditions known to modify respiratory pattern (cognitive load, inspiratory load and combination) in parts 2 and 3. RESULTS: There is a strong correlation between the depth recorded by the Kinect and the tidal volume recorded by the spirometer: r = 0.973 for COPD patients, r = 0.989 for LF patients and r = 0.984 for HC. The Kinect is able to detect changes in breathing patterns induced by different respiratory disturbance conditions, gender and oral task. CONCLUSIONS: Measurements performed with the Kinect sensors are highly correlated with the spirometer in HC and patients with COPD and LF. Kinect is also able to assess respiratory patterns under various loads and disturbances. This method is affordable, easy to use, fully automated and could be used in the current clinical context. Respiratory patterns are important to assess in daily clinics. However, there is currently no affordable and easy-to-use tool to evaluate these parameters in clinics. We validated a new system to assess respiratory patterns using the Kinect sensor in patients with chronic respiratory diseases.

Automated epileptic seizure detection based on break of excitation/inhibition balance.

Physiological models are attractive for seizure detection, as their parameters are related to physiological meanings. We propose an algorithm to early detect epileptic seizures based on automatic estimation of average synaptic gains (excitatory Ae, slow and fast inhibitory B and G) by combining clinical data with a neural mass model. Three indices (Ae/B, Ae/G and Ae/(B + G)), all related to excitation/inhibition balance, were calculated and used as cues to detect seizures. A simple thresholding method was employed. We evaluated the algorithm against the manual scoring of a human expert on intracranial EEG samples from 23 patients suffering from different types of epilepsy. Best performance was achieved using Ae/(B + G) as a cue, i.e. excitation/(slow + fast) inhibition, on temporal lobe epilepsy (TLE) patients. A leave-one-out cross-validation showed that the algorithm achieved 92.98% sensitivity for TLE patients. The median false positive rate was 0.16 per hour, and median detection delay was 14.5 s. Of interest, the threshold values determined by a leave-one-out cross-validation did nearly not vary among TLE patients, suggesting a general excitation/inhibition balance baseline in TLE patients. The same approach could be used with other types of epilepsy by adapting the neural mass model to these types.

Seizure evolution can be characterized as path through synaptic gain space of a neural mass model.

Physiologically based models could facilitate better understanding of mechanisms underlying epileptic seizures. In this paper, we attempt to reveal the dynamic evolution of intracranial EEG activity during epileptic seizures based on synaptic gain identification procedure of a neural mass model. The distribution of average excitatory, slow and fast inhibitory synaptic gain in the parameter space and their temporal evolution, i.e., the path through the model parameter space, were analyzed in thirty seizures from ten temporal lobe epileptic patients. Results showed that the synaptic gain values located roughly on a plane before seizure onset, dispersed during seizure and returned to the plane when seizure terminated. Cluster analysis was performed on seizure paths and demonstrated consistency in synaptic gain evolution across different seizures from the individual patient. Furthermore, two patient groups were identified, each one corresponding to a specific synaptic gain evolution in the parameter space during a seizure. Results were validated by a bootstrapping approach based on comparison with random paths. The differences in the path revealed variations in EEG dynamics for patients despite showing identical seizure onset pattern. Our approach may have the potential to classify the epileptic patients into subgroups based on different mechanisms revealed by subtle changes in synaptic gains and further enable more robust decisions regarding treatment strategy.

Dynamics underlying interictal to ictal transition in temporal lobe epilepsy: insights from a neural mass model.

We propose an approach that combines a neural mass model and clinical intracranial electroencephalographic (iEEG) recordings to explore the potential pathophysiological mechanisms (at the neuronal population level) of ictogenesis. Thirty iEEG recordings from 10 temporal lobe epilepsy (TLE) patients around seizure onset were investigated. Physiologically meaningful parameters [average excitatory (Ae ), slow (B), and fast (G) inhibitory synaptic gain] were identified during interictal to ictal transition. Four ratios (Ae /G, Ae /B, Ae /(B + G), and B/G) were derived from these parameters, and their evolution over time was analyzed. The excitation/inhibition ratio increased around seizure onset and decreased before seizure offset, indicating the impairment and re-emergence of excitation/inhibition balance around seizure onset and before seizure offset, respectively. Moreover, the slow inhibition may have an earlier effect on excitation/inhibition imbalance. We confirm the decrease in excitation/inhibition ratio upon seizure termination in human temporal lobe epilepsy, as revealed by optogenetic approaches both in vivo in animal models and in vitro. The increase in excitation/inhibition ratio around seizure occurrence could be an indicator to detect seizures.

REM-Enriched Naps Are Associated with Memory Consolidation for Sad Stories and Enhance Mood-Related Reactivity.

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.