[Efficacy of HRV-biofeedback as additional treatment of depression and PTSD]. / Effectiviteit van hartritmevariabiliteitbiofeedback als aanvulling bij behandeling van depressie en posttraumatische stressstoornis.

BACKGROUND: Heartrate variability biofeedback (HRVB) is a non-invasive treatment in which patients are assumed to self-regulate a physiological dysregulated vagal nerve. Although the therapeutic approach of HRVB is promising in various stress-related disorders, it has only been offered on a regular basis in a few mental health treatment settings. AIM: To analyse the efficacy of HRV biofeedback as an additional psychophysiological treatment for depression and PTSD. METHOD: Systematic review with search terms HRV, biofeedback, PTSD, depression, panic disorder and anxiety disorder. RESULTS: Our search of the literature yielded 789 studies. After critical appraisal using the GRADE method, we selected 6 randomised controlled trials (RCTs) and 4 relevant studies. The RCTs with control groups 'treatment as usual' and muscle relaxation training revealed significant clinical efficacy and better results than control conditions after 4 to 8 weeks training. CONCLUSION: Although this systematic review shows the popularity of HRV in literature, it does not indicate that HRVB really has been reviewed systematically. Significant outcomes of this limited number of randomised studies indicate there may be a clinical improvement when HRVB training is integrated into treatment of PTSD and depression, particularly when this integration procedure is combined with psychotherapy. More research needs to be done with larger groups and further efforts are needed to integrate HRVB into treatment of stress-related disorders in psychiatry. Future research also needs to focus on the psychophysiological mechanisms involved.

Functional connectivity in preterm infants derived from EEG coherence analysis.

OBJECTIVE: To quantify the neuronal connectivity in preterm infants between homologous channels of both hemispheres. METHODS: EEG coherence analysis was performed on serial EEG recordings collected from preterm infants with normal neurological follow-up. The coherence spectrum was divided in frequency bands: δnewborn(0-2 Hz), θnewborn(2-6 Hz), αnewborn(6-13 Hz), ßnewborn(13-30 Hz). Coherence values were evaluated as a function of gestational age (GA) and postnatal maturation. RESULTS: All spectra show two clear peaks in the δnewborn and θnewborn-band, corresponding to the delta and theta EEG waves observed in preterm infants. In the δnewborn-band the peak magnitude coherence decreases with GA and postnatal maturation for all channels. In the θnewborn-band, the peak magnitude coherence decreases with GA for all channels, but increases with postnatal maturation for the frontal polar channels. In the ßnewborn-band a modest magnitude coherence peak was observed in the occipital channels, which decreases with GA. CONCLUSIONS: Interhemispherical connectivity develops analogously with electrocortical maturation: signal intensities at low frequencies decrease with GA and postnatal maturation, but increase at high frequencies with postnatal maturation. In addition, peak magnitude coherence is a clear trend indicator for brain maturation. SIGNIFICANCE: Coherence analysis can aid in the clinical assessment of the functional connectivity of the infant brain with maturation.

A higher illuminance induces alertness even during office hours: findings on subjective measures, task performance and heart rate measures.

Nocturnal white light exposure has shown marked results on subjective and objective indicators of alertness, vitality and mood, yet effects of white light during daytime and under usual office work conditions have not been investigated extensively. The current study employed a mixed-group design (N=32), testing effects of two illuminance levels (200lx or 1000lx at eye level, 4000K) during one hour of morning versus afternoon exposure. In four repeated blocks, subjective reports, objective performance and physiological arousal were measured. Results showed effects of illuminance on subjective alertness and vitality, sustained attention in tasks, and heart rate and heart rate variability. Participants felt less sleepy and more energetic in the high versus the low lighting condition, had shorter reaction times on the psychomotor vigilance task and increased physiological arousal. Effects of illuminance on the subjective measures, as well as those on heart rate were not dependent on time of day or duration of exposure. Performance effects were most pronounced in the morning sessions and towards the end of the one-hour exposure period. The effect on heart rate variability was also most pronounced at the end of the one-hour exposure. The results demonstrate that even under normal, i.e., neither sleep nor light deprived conditions, more intense light can improve feelings of alertness and vitality, as well as objective performance and physiological arousal.

Automatic breath-to-breath analysis of nocturnal polysomnographic recordings.

Diagnosis of sleep-disordered breathing is based on the presence of an abnormal breathing pattern during sleep. In this study, an algorithm was developed for the offline breath-to-breath analysis of the nocturnal respiratory recordings. For that purpose, respiratory signals (nasal airway pressure, thoracic and abdominal movements) were divided into half waves using period amplitude analysis. Individual breaths were characterized by the parameters of the half waves (duration, amplitude, and slope). These values can be used to discriminate between normal and abnormal breaths. This algorithm was applied to six polysomnographic recordings to distinguish abnormal breathing events (apneas and hypopneas). The algorithm was robust for the identification of breaths (sensitivity = 96.8%, positive prediction value (PPV) = 99.5%). The detection of apneas and hypopneas was compared to the manual scoring of two experienced sleep technicians: sensitivity was, respectively, 89.2 and 88.9%, PPV was 54.1 and 59.3%. The classification of apneas into central, obstructive, or mixed was in concordance with the observers in 68% of the apneas. Although the algorithm tended to detect more hypopneas than the clinical standard, this study shows that the extraction of breath-to-breath parameters is useful for detection of abnormal respiratory events and provides a basis for further characterization of these events.

Designing for reliable textile neonatal ECG monitoring using multi-sensor recordings.

When designing an ECG monitoring system embedded with textile electrodes for comfort, it is challenging to ensure reliable monitoring, because textile electrodes suffer from motion artifacts and incidental poor signal quality. For the design of a comfortable monitoring system for prematurely born babies in the Neonatal Intensive Care Unit (NICU), we propose the concepts of 'diversity measurement' and 'context awareness' to improve reliability. Clinical multi-modal sensor data was collected in the NICU with the Smart Jacket connected to a state-of-the-art amplifier. We found that the ECG signals quality varied among sensors and varied over time, and found correlations between ECG signal, acceleration data, and context, which supports the feasibility of the concepts. Our explorative system level approach has lead to design parameters and meta-insights into the role of clinical validation in the design process.

An algorithm for the automatic detection of seizures in neonatal amplitude-integrated EEG.

AIM: To develop and evaluate an algorithm for the automatic screening of electrographic neonatal seizures (ENS) in amplitude-integrated electroencephalography (aEEG) signals. METHODS: CFM recordings were recorded in asphyxiated (near)term newborns. ENS of at least 60 sec were detected based on their characteristic pattern in the aEEG signal, an increase of its lower boundary. The algorithm was trained using five CFM recordings (training set) annotated by a neurophysiologist, observer1. The evaluation of the algorithm was based on eight different CFM recordings annotated by observer1 (test set observer 1) and an independent neurophysiologist, observer2 (test set observer 2). RESULTS: The interobserver agreement between observer1 and 2 in interpreting ENS from the CFM recordings was high (G coefficient: 0.82). After dividing the eight CFM recordings into 1-min segments and classification in ENS or non-ENS, the intraclass correlation coefficient showed high correlations of the algorithm with both test sets (respectively, 0.95 and 0.85 with observer1 and 2). The algorithm showed in five recordings a sensitivity > or = 90% and approximately 1 false positive ENS per hour. However, the algorithm showed in three recordings much lower sensitivities: one recording showed ENSs of extremely high amplitude that were incorrectly classified by the algorithm as artefacts and two recordings suffered from low interobserver agreement. CONCLUSION: This study shows the feasibility of automatic ENS screening based on aEEG signals and may facilitate in the bed-side interpretation of aEEG signals in clinical practice.