Auditory deviance detection and involuntary attention allocation in occupational burnout-A follow-up study.

Here, we investigated the central auditory processing and attentional control associated with both recovery and prolongation of occupational burnout. We recorded the event-related brain potentials N1, P2, mismatch negativity (MMN) and P3a to nine changes in speech sounds and to three rarely presented emotional (happy, angry and sad) utterances from individuals with burnout (N = 16) and their matched controls (N = 12). After the 5 years follow-up, one control had acquired burnout, half (N = 8) of the burnout group had recovered, and the other half (prolonged burnout) still had burnout. The processing of acoustical changes in speech sounds was mainly intact. Prolongation of the burnout was associated with a decrease in MMN amplitude and an increase in P3a amplitude for the happy stimulus. The results suggest that, in the absence of interventions, burnout is a persistent condition, associated with alterations of attentional control, that may be amplified with the prolongation of the condition.

Saccadic eye movements estimate prolonged time awake.

Prolonged time awake increases the need to sleep. Sleep pressure increases sleepiness, impairs human alertness and performance and increases the probability of human errors and accidents. Human performance and alertness during waking hours are influenced by homeostatic sleep drive and the circadian rhythm. Cognitive functions, especially attentional ones, are vulnerable to circadian rhythm and increasing sleep drive. A reliable, objective and practical metrics for estimating sleepiness could therefore be valuable. Our aim is to study whether saccades measured with electro-oculography (EOG) outside the laboratory could be used to estimate the overall time awake without sleep of a person. The number of executed saccades was measured in 11 participants during an 8-min saccade task. The saccades were recorded outside the laboratory (Naval Academy, Bergen) using EOG every sixth hour until 54 hr of time awake. Measurements were carried out on two occasions separated by 10 weeks. Five participants participated in both measurement weeks. The number of saccades decreased during sustained wakefulness. The data correlated with the three-process model of alertness; performance differed between participants but was stable within individual participants. A mathematically monotonous relation between performance in the saccade task and time awake was seen after removing the circadian rhythm component from measured eye movement data. The results imply that saccades measured with EOG can be used as a time-awake metric outside the laboratory.

A chair based ballistocardiogram time interval measurement with cardiovascular provocations.

The objective of this study was to measure ballistocardiogram (BCG) based time intervals and compare them with systolic blood pressure values. Electrocardiogram (ECG) and BCG signals of six subjects sitting in a chair were measured with a ferroelectret film sensor. Time intervals between ECG R peak and BCG I and J waves were calculated to obtain RJ, RI and IJ intervals. The time intervals were modified with two cardiovascular provocations, controlled breathing and Valsalva maneuver. The controlled breathing changed all the time intervals (RJ, RI and IJ) whereas the Valsalva maneuver mainly caused variations in the RJ and RI intervals. The calculated time intervals were compared with reference arterial blood pressure values. Correlation coefficients of r = -0.61 and r = -0.78 were found between the RJ and RI time intervals and systolic blood pressure during Valsalva maneuver, respectively.

Variations of Heart Rate, Pulse Arrival Time and Blood Pressure in a Versatile Laboratory Protocol.

Many studies dealing with blood pressure modeling are evaluated based on a single type of provocation. This paper investigates widely used provocations such as controlled breathing, mental arithmetic and Stroop tests, Valsalva maneuver, cold pressor and muscle tension and combines them in a versatile laboratory protocol. The protocol was tested in an experiment where pulse arrival time (PAT) and heart rate (HR) were measured with chest ECG and finger PPG sensors and blood pressure (BP) with continuous fingercuff monitor. The experiment results show that mental tasks provoked HR, BP and PAT very little while cold pressor and muscle tension had strong impact in all parameters. Valsalva maneuver had strongest impact in HR and PAT but the effect was transient like. We also predicted systolic BP based on the PAT values. We selected nine points in the protocol to calculate linear prediction model for each subject and then fitted data points to the models. If only the calibration points are taken into account, the correlation between the predicted and measured systolic BP was 0.91. When all the data points are fed into model, correlation was 0.75.

Enhanced Memory Consolidation Via Automatic Sound Stimulation During Non-REM Sleep.

Introduction: Slow-wave sleep (SWS) slow waves and sleep spindle activity have been shown to be crucial for memory consolidation. Recently, memory consolidation has been causally facilitated in human participants via auditory stimuli phase-locked to SWS slow waves. Aims: Here, we aimed to develop a new acoustic stimulus protocol to facilitate learning and to validate it using different memory tasks. Most importantly, the stimulation setup was automated to be applicable for ambulatory home use. Methods: Fifteen healthy participants slept 3 nights in the laboratory. Learning was tested with 4 memory tasks (word pairs, serial finger tapping, picture recognition, and face-name association). Additional questionnaires addressed subjective sleep quality and overnight changes in mood. During the stimulus night, auditory stimuli were adjusted and targeted by an unsupervised algorithm to be phase-locked to the negative peak of slow waves in SWS. During the control night no sounds were presented. Results: Results showed that the sound stimulation increased both slow wave (p = .002) and sleep spindle activity (p < .001). When overnight improvement of memory performance was compared between stimulus and control nights, we found a significant effect in word pair task but not in other memory tasks. The stimulation did not affect sleep structure or subjective sleep quality. Conclusions: We showed that the memory effect of the SWS-targeted individually triggered single-sound stimulation is specific to verbal associative memory. Moreover, the ambulatory and automated sound stimulus setup was promising and allows for a broad range of potential follow-up studies in the future.

Alterations in attention capture to auditory emotional stimuli in job burnout: an event-related potential study.

Job burnout is a significant cause of work absenteeism. Evidence from behavioral studies and patient reports suggests that job burnout is associated with impairments of attention and decreased working capacity, and it has overlapping elements with depression, anxiety and sleep disturbances. Here, we examined the electrophysiological correlates of automatic sound change detection and involuntary attention allocation in job burnout using scalp recordings of event-related potentials (ERP). Volunteers with job burnout symptoms but without severe depression and anxiety disorders and their non-burnout controls were presented with natural speech sound stimuli (standard and nine deviants), as well as three rarely occurring speech sounds with strong emotional prosody. All stimuli elicited mismatch negativity (MMN) responses that were comparable in both groups. The groups differed with respect to the P3a, an ERP component reflecting involuntary shift of attention: job burnout group showed a shorter P3a latency in response to the emotionally negative stimulus, and a longer latency in response to the positive stimulus. Results indicate that in job burnout, automatic speech sound discrimination is intact, but there is an attention capture tendency that is faster for negative, and slower to positive information compared to that of controls.

Heart rate variability for evaluating vigilant attention in partial chronic sleep restriction.

STUDY OBJECTIVES: Examine the use of spectral heart rate variability (HRV) metrics in measuring sleepiness under chronic partial sleep restriction, and identify underlying relationships between HRV, Karolinska Sleepiness Scale ratings (KSS), and performance on the Psychomotor Vigilance Task (PVT). DESIGN: Controlled laboratory study. SETTING: Experimental laboratory of the Brain Work Research Centre of the Finnish Institute of Occupational Health, Helsinki, Finland. PARTICIPANTS: Twenty-three healthy young males (mean age ± SD = 23.77 ± 2.29). INTERVENTIONS: A sleep restriction group (N = 15) was subjected to chronic partial sleep restriction with 4 h sleep for 5 nights. A control group (N = 8) had 8 h sleep on all nights. MEASUREMENTS AND RESULTS: Based on a search over all HRV frequency bands in the range [0.00, 0.40] Hz, the band [0.01, 0.08] Hz showed the highest correlation for HRV-PVT (0.60, 95% confidence interval [0.49, 0.69]) and HRV-KSS (0.33, 95% confidence interval [0.16, 0.46]) for the sleep restriction group; no correlation was found for the control group. We studied the fraction of variance in PVT explained by HRV and a 3-component alertness model, containing circadian and homeostatic processes coupled with sleep inertia, respectively. HRV alone explained 33% of PVT variance. CONCLUSIONS: The findings suggest that HRV spectral power reflects vigilant attention in subjects exposed to partial chronic sleep restriction. CITATION: Henelius A, Sallinen M, Huotilainen M, Müller K, Virkkala J, Puolamäki K. Heart rate variability for evaluating vigilant attention in partial chronic sleep restriction.

Algorithm for automatic analysis of electro-oculographic data.

BACKGROUND: Large amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets. METHODS: The auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks. RESULTS: The algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate. CONCLUSION: The developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.

Breakfast high in whey protein or carbohydrates improves coping with workload in healthy subjects.

Dietary components may affect brain function and influence behaviour by inducing the synthesis of neurotransmitters. The aim of the present study was to examine the influence of consumption of a whey protein-containing breakfast drink v. a carbohydrate drink v. control on subjective and physiological responses to mental workload in simulated work. In a randomised cross-over design, ten healthy subjects (seven women, median age 26 years, median BMI 23 kg/m(2)) participated in a single-blinded, placebo-controlled study. The subjects performed demanding work-like tasks after having a breakfast drink high in protein (HP) or high in carbohydrate (HC) or a control drink on separate sessions. Subjective states were assessed using the NASA Task Load Index (NASA-TLX), the Karolinska sleepiness scale (KSS) and the modified Profile of Mood States. Heart rate was recorded during task performance. The ratio of plasma tryptophan (Trp) to the sum of the other large neutral amino acids (LNAA) and salivary cortisol were also analysed. The plasma Trp:LNAA ratio was 30 % higher after the test drinks HP (median 0·13 (µmol/l)/(µmol/l)) and HC (median 0·13 (µmol/l)/(µmol/l)) than after the control drink (median 0·10 (µmol/l)/(µmol/l)). The increase in heart rate was smaller after the HP (median 2·7 beats/min) and HC (median 1·9 beats/min) drinks when compared with the control drink (median 7·2 beats/min) during task performance. Subjective sleepiness was reduced more after the HC drink (median KSS - 1·5) than after the control drink (median KSS - 0·5). There were no significant differences between the breakfast types in the NASA-TLX index, cortisol levels or task performance. We conclude that a breakfast drink high in whey protein or carbohydrates may improve coping with mental tasks in healthy subjects.

Sleep restriction for the duration of a work week impairs multitasking performance.

It is important to develop shift schedules that minimise the chance for sleep-related human error in safety-critical domains. Experimental data on the effects of sleep restriction (SR) play a key role in this development work. In order to provide such data, we conducted an experiment in which cognitively demanding and long-duration task performance, simulating task performance at work, was measured under SR and following recovery. Twenty healthy male volunteers, aged 19-29 years, participated in the study. Thirteen of them had first two baseline days (8-h sleep opportunity per day), then five SR days (4-h sleep) and finally two recovery days (8-h sleep). Seven controls were allowed to sleep for 8 h each night. On each experimental day, multitask performance was tested in 50-min sessions, physiological sleepiness was evaluated during multitask performance using electroencephalogram (EEG)/electrooculogram (EOG) recordings, and psychomotor vigilance task performance and Karolinska Sleepiness Scale were recorded. Sleep-wake rhythm was monitored throughout the experiment. The multitask performance progressively deteriorated as a result of prolongation of the SR and the time spent on the task. The effect was significant at group level, but individual differences were large: performance was not markedly deteriorated in all participants. Similar changes were observed also in EEG/EOG-defined sleepiness. The recovery process of performance and sleepiness from the SR continued over the two recovery sleep opportunities. In all, our findings emphasise the importance of shift systems that do not restrict sleep for several consecutive days.

Improving the saccade peak velocity measurement for detecting fatigue.

The aim of the study was to compare saccadic peak velocity (SPV) values measured with video based Fitness Impairment Tester (FIT) and electro-oculography (EOG) during prolonged wakefulness. We tested different numbers of saccades and two saccade paradigms to improve the EOG measurements for detecting fatigue. The SPVs were measured from 11 fast patrol boat navigators with FIT and EOG every sixth hour until 54 h. Subjective sleepiness was assessed with the Karolinska Sleepiness Scale. EOG was measured using an overlap and a gap paradigm and the data was divided into sequential five 20-saccade blocks and cumulative blocks of 20, 40, 60, 80, and 100 saccades. Compared to the gap paradigm, the overlap paradigm produced a higher number of analyzable saccades for a given measurement time. The shorter measurements (20-40 saccades) appeared to be more sensitive for fatigue, whereas the longer measurements (60-100 saccades) were more sensitive to time spent on the task. Thus, the optimal number of saccades varies also depending on the research question. The EOG method was more sensitive to fatigue than FIT. The FIT values measured after 30 and 36 h of wakefulness did not differ significantly from the baseline values, while subjective sleepiness and the EOG values showed that the participants were significantly less alert at these time points. The EOG measurements can be improved for detecting fatigue by using the overlap saccade paradigm. The SPV values measured with the EOG method appear to be somewhat more sensitive in detecting fatigue than the FIT method.

Mental workload classification using heart rate metrics.

The ability of different short-term heart rate variability metrics to classify the level of mental workload (MWL) in 140 s segments was studied. Electrocardiographic data and event related potentials (ERPs), calculated from electroencephalographic data, were collected from 13 healthy subjects during the performance of a computerised cognitive multitask test with different task load levels. The amplitude of the P300 component of the ERPs was used as an objective measure of MWL. Receiver operating characteristics analysis (ROC) showed that the time domain metric of average interbeat interval length was the best-performing metric in terms of classification ability.

Sleep stage classification with low complexity and low bit rate.

Standard sleep stage classification is based on visual analysis of central (usually also frontal and occipital) EEG, two-channel EOG, and submental EMG signals. The process is complex, using multiple electrodes, and is usually based on relatively high (200-500 Hz) sampling rates. Also at least 12 bit analog to digital conversion is recommended (with 16 bit storage) resulting in total bit rate of at least 12.8 kbit/s. This is not a problem for in-house laboratory sleep studies, but in the case of online wireless self-applicable ambulatory sleep studies, lower complexity and lower bit rates are preferred. In this study we further developed earlier single channel facial EMG/EOG/EEG-based automatic sleep stage classification. An algorithm with a simple decision tree separated 30 s epochs into wakefulness, SREM, S1/S2 and SWS using 18-45 Hz beta power and 0.5-6 Hz amplitude. Improvements included low complexity recursive digital filtering. We also evaluated the effects of a reduced sampling rate, reduced number of quantization steps and reduced dynamic range on the sleep data of 132 training and 131 testing subjects. With the studied algorithm, it was possible to reduce the sampling rate to 50 Hz (having a low pass filter at 90 Hz), and the dynamic range to 244 microV, with an 8 bit resolution resulting in a bit rate of 0.4 kbit/s. Facial electrodes and a low bit rate enables the use of smaller devices for sleep stage classification in home environments.

Estimating brain load from the EEG.

Modern work requires cognitively demanding multitasking and the need for sustained vigilance, which may result in work-related stress and may increase the possibility of human error. Objective methods for estimating cognitive overload and mental fatigue of the brain on-line, during work performance, are needed. We present a two-channel electroencephalography (EEG)-based index, theta Fz/alpha Pz ratio, potentially implementable into a compact wearable device. The index reacts to both acute external and cumulative internal load. The index increased with the number of tasks to be performed concurrently (p = 0.004) and with increased time awake, both after normal sleep (p = 0.002) and sleep restriction (p = 0.004). Moreover, the increase of the index was more pronounced in the afternoon after sleep restriction (p = 0.006). As a measure of brain state and its dynamics, the index can be considered equivalent to the heartbeat, an indicator of the cardiovascular state, thus inspiring the name "brainbeat".

Recovery of cognitive performance from sleep debt: do a short rest pause and a single recovery night help?

We studied the recovery of multitask performance and sleepiness from acute partial sleep deprivation through rest pauses embedded in performance sessions and an 8 h recovery sleep opportunity the following night. Sixteen healthy men, aged 19-22 yrs, participated in normal sleep (two successive nights with 8 h sleep) and sleep debt (one 2 h night sleep followed by an 8 h sleep the following night) conditions. In both conditions, the participants performed four 70 min multitask sessions, with every other one containing a 10 min rest pause with light neck-shoulder exercise. The multitask consisted of four simultaneously active subtasks, with the level of difficulty set in relation to each participant's ability. Physiological sleepiness was assessed with continuous electroencephalography/electro-oculography recordings during themultitask sessions, and subjective sleepiness was self-rated with the Karolinska Sleepiness Scale. Results showed that multitask performance and physiological and subjective sleepiness were impaired by the sleep debt ( p > .001). The rest pause improved performance and subjective sleepiness for about 15 min, regardless of the amount of prior sleep ( p > .01-.05). Following recovery sleep, all outcome measures showed marked improvement ( p < .001), but they failed to reach the levels observed in the control condition ( p < .001-.05). A correlation analysis showed the participants whose multitask performance deteriorated the most following the night of sleep loss tended to be the same persons whose performance was most impaired following the night of the recovery sleep ( p < .001). Taken together, our results suggest that a short rest pause with light exercise is not an effective countermeasure in itself for sleep debt-induced impairments when long-term effects are sought. In addition, it seems that shift arrangements that lead to at least a moderate sleep debt should be followed by more than one recovery night to ensure full recovery. Persons whose cognitive performance is most affected by sleep debt are likely to require the most sleep to recover.

Automatic sleep stage classification using two facial electrodes.

Standard sleep stage classification is based on visual analysis of central EEG, EOG and EMG signals. Automatic analysis with a reduced number of sensors has been studied as an easy alternative to the standard. In this study, a single-channel electro-oculography (EOG) algorithm was developed for separation of wakefulness, SREM, light sleep (S1, S2) and slow wave sleep (S3, S4). The algorithm was developed and tested with 296 subjects. Additional validation was performed on 16 subjects using a low weight single-channel Alive Monitor. In the validation study, subjects attached the disposable EOG electrodes themselves at home. In separating the four stages total agreement (and Cohen's Kappa) in the training data set was 74% (0.59), in the testing data set 73% (0.59) and in the validation data set 74% (0.59). Self-applicable electro-oculography with only two facial electrodes was found to provide reasonable sleep stage information.

Reducing the effects of electrocardiographic artifacts on electro-oculography in automatic sleep analysis.

Removal of electrocardiographic (ECG) artifacts of QRS complexes from a single channel electroencephalography (EEG) and electro-oculography (EOG) can be problematic especially when no reference ECG signal is available. This study examined a simple estimation method excluding the possible QRS part of the EOG trace before spectrum estimation. The method was tested using a simple sleep classifier based on 0.5-30 Hz mean frequency of single channel sleep EOG, with the left EOG electrode referenced to the left mastoid (EOG L-M1). When QRS peaks were automatically excluded from the least square (LS) mean frequency estimation the average optimal mean frequency threshold decreased from 9.3 Hz to 8.8 Hz and agreement and Cohen's Kappa increased respectively from 89% to 90% and from 0.44 to 0.50 when compared to the traditional spectral estimation.

Automatic sleep stage classification using two-channel electro-oculography.

An automatic method for the classification of wakefulness and sleep stages SREM, S1, S2 and SWS was developed based on our two previous studies. The method is based on a two-channel electro-oculography (EOG) referenced to the left mastoid (M1). Synchronous electroencephalographic (EEG) activity in S2 and SWS was detected by calculating cross-correlation and peak-to-peak amplitude difference in the 0.5-6 Hz band between the two EOG channels. An automatic slow eye-movement (SEM) estimation was used to indicate wakefulness, SREM and S1. Beta power 18-30 Hz and alpha power 8-12 Hz was also used for wakefulness detection. Synchronous 1.5-6 Hz EEG activity and absence of large eye movements was used for S1 separation from SREM. Simple smoothing rules were also applied. Sleep EEG, EOG and EMG were recorded from 265 subjects. The system was tuned using data from 132 training subjects and then applied to data from 131 validation subjects that were different to the training subjects. Cohen's Kappa between the visual and the developed new automatic scoring in separating 30s wakefulness, SREM, S1, S2 and SWS epochs was substantial 0.62 with epoch by epoch agreement of 72%. With automatic subject specific alpha thresholds for offline applications results improved to 0.63 and 73%. The automatic method can be further developed and applied for ambulatory sleep recordings by using only four disposable, self-adhesive and self-applicable electrodes.

Automatic detection of slow wave sleep using two channel electro-oculography.

An automatic method was developed for detecting slow wave sleep (SWS). The automatic method is based on a two-channel electro-oculography (EOG) with left mastoid (M1) as reference. Synchronous electroencephalographic (EEG) activity was detected by calculating cross-correlation between the two EOG channels by using 0.5-6 Hz band. An amplitude criterion was used for detecting slow waves and beta power 18-30 Hz was used to exclude artefacts. The automatic scoring was compared to a standard visual sleep scoring based on EOG, central EEG and submental EMG. Sleep EEG and EOG were recorded from 265 subjects. The optimal cross-correlation, amplitude and beta thresholds were derived using data from 133 training subjects and then applied to the data from different 132 validation subjects. Results were most sensitive to the changes in the amplitude criteria. Cohen's Kappa between the visual and the new developed automatic scoring in separating non-SWS and SWS was substantial (0.70) with epoch-by-epoch agreement of 93%. SWS epoch detection sensitivity was 75% and specificity was 96%. Also the total amount of slow waves, slow wave time (SWT), was estimated. The advantage of the automatic method is that it could be applied during online recordings using only four disposable self-adhesive electrodes.