Jack Doman

All-night spectral analysis of the sleep EEG in untreated depressives and normal controls

Sleep was recorded in nine drug-free depressive patients and nine age- and sex-matched normal control subjects. All-night spectral analysis of the sleep electroencephalogram (EEG) showed a significantly reduced power density in the 0.25-2.50 Hz band in the depressive group. Power density values integrated over the entire frequency range (0.25-25.0 Hz) exhibited for both groups a decreasing trend over the first three non-REM/REM sleep cycles. In each cycle depressives had lower values than controls. The results are consistent with hypothesis that the build-up of a sleep-dependent process is deficient in the sleep regulation of depressive patients.

Application of automated REM and slow wave sleep analysis: II. Testing the assumptions of the two-process model of sleep regulation in normal and depressed subjects

Abnormalities in a two-process model of sleep regulation (a sleep-dependent process, termed Process S, and a sleep-independent circadian process, termed Process C) have been proposed to account for sleep abnormalities in depressive states. The major tenets of the two-process model of sleep regulation as applied to depression are: the level of process S, as reflected by the electroencephalographic (EEG) slow-wave activity, corresponds to the sleep-dependent facet of sleep propensity; the pathognomonic changes of sleep in depressives are a consequence of a deficiency in the build-up of process S. The application of automated rapid eye movement (REM) and delta wave analyses in normal subjects and younger depressed patients supports the model to some extent: The time spent asleep is positively correlated with total delta waves (normals and depressives) and average delta waves (depressives); delta sleep is lower in depressives than in normals; the average delta wave count is significantly reduced in younger depressives over the total night and in non-REM period 1. The model also postulates that measures of phasic REM activity are inversely related to process S, suggesting that process S can be regarded as exerting an inhibitory influence on phasic REM activity.

Application of automated REM and slow wave sleep analysis: I. Normal and depressed subjects.

Computerized analysis of rapid eye movement (REM) and delta electroencephalographic (EEG) sleep patterns in normal and depressed subjects offers opportunities to examine sleep more precisely than previously possible. In the present study, automated REM analyses demonstrated good reliability with traditional manual procedures in both normal and depressed subjects. However, automated delta analyses correlated well with traditional scoring in normal subjects, but not in depressed patients. These findings suggest the use of automated delta techniques similar to those employed in this report or spectral analytic techniques in the following types of studies: specificity of delta sleep in various psychiatric syndromes, changes in delta sleep produced by the administration of psychotropic agents, relationships between delta sleep and sleep-related neuro-endocrine patterns, and, finally, relationships between delta sleep patterns and other biological rhythms such as activity and temperature.

Automating the sleep laboratory: Implementation and validation of digital recording and analysis

OBJECTIVES We report on the implementation of digital processing in a large clinical and research sleep laboratory. The system includes the digital collection, display, analysis, and repository of physiological signals collected during sleep. METHODS After describing the original analog system, the computer equipment and software necessary for the digital implementation are presented and we explain our algorithms for rapid eye movement (REM) and delta-wave detection. Finally, we describe an experiment validating the digital system of display and analyses. CONCLUSIONS The digital processing of sleep signals saves computer operator, polysomnographic technologist, and computer time. It also saves resources such as polysomnographic paper and FM tape. The digital signals lend themselves to a large array of analysis techniques and result in improved signal quality. Automated REM and delta-wave detection via digital processing correlate highly with visual counts of rapid eye movements and delta waves.

Power spectral analysis of EEG in a multiple-bedroom, multiple-polygraph sleep laboratory

OBJECTIVES We describe the methods for power spectral analysis (PSA) of sleep electroencephalogram (EEG) data at a large clinical and research sleep laboratory. The multiple-bedroom, multiple-polygraph design of the sleep laboratory poses unique challenges for the quantitative analysis of the data. This paper focuses on the steps taken to ensure that our PSA results are not biased by the particular bedroom or polygraph from which the data were acquired. METHODS After describing the data acquisition system hardware, we present our signal amplitude calibration procedure and our methods for performing PSA. We validate the amplitude calibration procedure in several experiments using PSA to establish tolerances for data acquisition from multiple bedrooms and polygraphs. RESULTS Since it is not possible to acquire identical digitized versions of an EEG signal using different sets of equipment, the best that can be achieved is data acquisition that is polygraph-independent within a known tolerance. We are able to demonstrate a tolerance in signal amplitude of +/- 0.25% when digitizing data from different bedrooms. When different data acquisition hardware is used, the power tolerance is approximately +/- 3% for frequencies from 1 to 35 Hz. The power tolerance is between +/- 3 and +/- 7% for frequencies below 1 Hz and frequencies between 35 and 50 Hz. Additional data demonstrate that variability due to the hardware system is small relative to the inherent variability of the sleep EEG. CONCLUSION The PSA results obtained in one location can be replicated elsewhere (subject to known tolerances) only if the data acquisition system and PSA method are adequately specified.

Computer analysis of EEG, EOG, and NPT activity during sleep

Enhancements to DELREM, a real-time computer program which concurrently analyzes EEG and EOG activity, are presented. These include the programs ability to monitor nocturnal penile tumescence (NPT) during a sleep recording, and the use of a standard calibration signal for time synchronization and adjustment to differences in tape recorder amplification and speed settings is used. Some of the advantages of using DELREM are discussed.

A computer algorithm to determine the nadir and rise time in nocturnal cortisol secretion

The nadir concentration value and the time of the circadian rise are two important characteristics of the nocturnal cortisol secretory pattern. A computer algorithm has been developed which objectively determines these parameters, supplementing the subjective evaluating methods previously used. The algorithm smooths the cortisol values and incorporates the intra-assay variability when calculating the nadir and rise. It was tested on 156 nights of cortisol data for healthy control and depressed subjects. The algorithm results closely matched the nadir and rise time subjectively determined by the investigators. With careful screening of the computer generated results, the algorithm decreases the reliance on subjective methods to determine the actual nocturnal cortisol nadir and rise time.

Real-time fatigue reduction in emergency care clinicians: the SleepTrackTXT randomized trial

BACKGROUND We assessed performance characteristics and impact of a mobile phone text-message intervention for reducing intra-shift fatigue among emergency clinician shift workers. METHODS We used a randomized controlled trial of 100 participants. All participants received text-message assessments at the start, every 4 hr during, and at end of scheduled shifts over a 90-day period. Text-message queries measured self-rated sleepiness, fatigue, and difficulty with concentration. Additional text-messages were sent to intervention participants to promote alertness. A performance measure of interest was compliance with answering text-messages. RESULTS Ninety-nine participants documented 2,621 shifts and responded to 36,073 of 40,947 text-messages (88% compliance rate). Intervention participants reported lower mean fatigue and sleepiness at 4 hr, 8 hr, and at the end of 12 hr shifts compared to controls (P < 0.05). Intervention participants reported better sleep quality at 90-days compared to baseline (P = 0.01). CONCLUSIONS We showed feasibility and short-term efficacy of a text-message based assessment and intervention tool.

Real-Time Fatigue Mitigation with Air-Medical Personnel: The SleepTrackTXT2 Randomized Trial

Abstract Objective: The aims of this study were: 1) to determine the short-term impact of the SleepTrackTXT2 intervention on air-medical clinician fatigue during work shifts and 2) determine the longer-term impact on sleep quality over 120 days. Methods: We used a multi-site randomized controlled trial study design with a targeted enrollment of 100 (ClinicalTrials.gov NCT02783027). The intervention was behavioral (non-pharmacological) and participation was scheduled for 120 days. Participation was voluntary. All consented participants answered baseline as well as follow-up surveys. All participants answered text message queries, which assessed self-rated fatigue, sleepiness, concentration, recovery, and hours of sleep. Intervention participants received additional text messages with recommendations for behaviors that can mitigate fatigue. Intervention participants received weekly text messages that promoted sleep. Our analysis was guided by the intent-to-treat principle. For the long-term outcome of interest (sleep quality at 120 days), we used a two-sample t-test on the change in sleep quality to determine the intervention effect. Results: Eighty-three individuals were randomized and 2,828 shifts documented (median shifts per participant =37, IQR 23–49). Seventy-one percent of individuals randomized (n = 59) participated up to the 120-day study period and 52% (n = 43) completed the follow-up survey. Of the 69,530 text messages distributed, participants responded to 61,571 (88.6%). Mean sleep quality at 120 days did not differ from baseline for intervention (p > 0.05) or control group participants (p > 0.05), and did not differ between groups (p > 0.05). There was no change from baseline to 120 days in the proportion with poor sleep quality in either group. Intra-shift fatigue increased (worsened) over the course of 12-hour shifts for participants in both study arms. Fatigue at the end of 12-hour shifts was higher among control group participants than participants in the intervention group (p < 0.05). Pre-shift hours of sleep were often less than 7 hours and did not differ between the groups over time. Conclusions: The SleepTrackTXT2 behavioral intervention showed a positive short-term impact on self-rated fatigue during 12-hour shifts, but did not impact longer duration shifts or have a longer-term impact on sleep quality among air-medical EMS clinicians.