Jon Tippin

Neurological soft signs in manic patients: A comparison with schizophrenic and control groups

Abstract Manic (N = 28), schizophrenic (N = 44) and control (N = 29) males were tested for a series of 30 neurological soft signs. Both manic and schizophrenic groups performed significantly worse overall compared to the control group. Manic patients were not significantly different from the schizophrenic patients on 27 of 30 soft signs, and were significantly better in three. The data suggest that neurological soft signs may be nonspecific pathological correlates of schizophrenia or mania.

Visual vigilance in drivers with obstructive sleep apnea

OBJECTIVE To determine the effects of obstructive sleep apnea (OSA) on visual vigilance during simulated automobile driving. METHODS Twenty-five drivers with OSA and 41 comparison drivers participated in an hour-long drive in a high-fidelity driving simulator. Drivers responded to light targets flashed at seven locations across the forward horizon. Dependent measures were percent correct [hit rate (HR)] and reaction time (RT). Self-assessment of sleepiness used the Stanford Sleepiness Scale (SSS) before and after the drive and the Epworth Sleepiness Scale (ESS). RESULTS OSA drivers showed reduced vigilance based on lower HR than comparison drivers, especially for peripheral targets (80.7+/-14.8% vs. 86.7+/-8.8%, P=.03). OSA drivers were sleepier at the end of the drive than comparison drivers (SSS=4.2+/-1.2 vs. 3.6+/-1.2, P=.03), and increased sleepiness correlated with decreased HR only in those with OSA (r=-0.49, P=.01). Lower HR and higher post-drive SSS predicted greater numbers of driving errors in all subjects. Yet, ESS, predrive SSS, and most objective measures of disease severity failed to predict driving and vigilance performance in OSA. CONCLUSIONS Reduced vigilance for peripheral visual targets indicates that OSA drivers have restriction of their effective field of view, which may partly explain their increased crash risk. This fatigue-related decline in attention is predicted by increased subjective sleepiness during driving. These findings may suggest a means of identifying and counseling high-risk drivers and aid in the development of in-vehicle alerting and warning devices.

Prolonged Cortical Electrical Depression and Diffuse Vasospasm Without Ischemia in a Case of Severe Hemiplegic Migraine During Pregnancy

The pathophysiology of the neurological deficits in hemiplegic migraine remains unclear. Both neurogenic and vascular etiologies have been proposed to explain this phenomenon. We present the case of a patient with hemiplegic migraine in whom there was no diffusion weighted magnetic resonance imaging evidence of cerebral ischemia, despite persistent left hemiplegia and diffuse vasospasm on cerebral angiography, there was no diffusion weighted magnetic resonance imaging evidence of cerebral ischemia. In addition, hypoperfusional changes were seen diffusely, more so on the less symptomatic hemisphere, whereas depression of cortical electrical activity was seen for several days on the right side. These findings support the notion that although both neurogenic and vascular changes occur in hemiplegic migraine, the former seems to be a more likely explanation for the neurological deficits.

Sleep remains disturbed in patients with obstructive sleep apnea treated with positive airway pressure: a three-month cohort study using continuous actigraphy

OBJECTIVE Some patients with obstructive sleep apnea (OSA) remain sleepy despite positive airway pressure (PAP) therapy. The mechanisms by which this occurs are unclear but could include persistently disturbed sleep. The goal of this study was to explore the relationships between subjective sleepiness and actigraphic measures of sleep during the first three months of PAP treatment. METHODS We enrolled 80 patients with OSA and 50 comparison subjects prior to treatment and observed them through three months of PAP therapy. PAP adherence and presence of residual respiratory events were determined from PAP machine downloads. Epworth Sleepiness Scale (ESS), Functional Outcomes of Sleep Questionnaire (FOSQ), and actigraphic data were collected before and at monthly intervals after starting PAP. RESULTS Patients with OSA were sleepier and showed a greater degree of sleep disruption by actigraphy at the baseline. After three months of PAP, only ESS and number of awakenings (AWAKE#) normalized, while wake after sleep onset and sleep efficiency remained worse in patients with OSA. FOSQ was improved in patients with OSA but never reached the same level as that of comparison subjects. ESS and FOSQ improved slowly over the study period. CONCLUSIONS As a group, patients with OSA show actigraphic evidence of persistently disturbed sleep and sleepiness-related impairments in day-to-day function after three months of PAP therapy. Improvements in sleepiness evolve over months with more severely affected patients responding quicker. Persistent sleep disruption may partially explain residual sleepiness in some PAP-adherent OSA patients.

The Language of Driving: Advantages and Applications of Symbolic Data Reduction for Analysis of Naturalistic Driving Data

Recent advances in onboard vehicle data recording devices have created an abundance of naturalistic driving data. The amount of data exceeds the resources available for analysis; this situation forces researchers to focus on analyses of critical events and to use simple heuristics to identify those events. Critical event analysis eliminates the context that can be critical in understanding driver behavior and can reduce the generalizability of the analysis. This work introduced a method of naturalistic driving data analysis that would allow researchers to examine entire data sets by reducing the sets by more than 90%. The method utilized a symbolic data reduction algorithm, symbolic aggregate approximation (SAX), which reduced time series data to a string of letters. SAX can be applied to any continuous measurement, and SAX output can be reintegrated into a data set to preserve categorical information. This work explored the application of SAX to speed and acceleration data from a naturalistic driving data set and demonstrated SAXs integration with other methods that could begin to tame the complexity of naturalistic data.

Non-motor symptoms in Parkinson's disease

The degeneration of the dopaminergic nigrostriatal system and parkinsonism (rest tremor, rigidity, bradykinesia and postural instability/gait disorder) represent only one aspect of Parkinson’s disease (PD), a multifaceted and complex disorder. In addition to this typical motor dysfunction, non-motor symptoms (NMS) also significantly reduce of quality of life. Several non-motor features are associated with deficits in extranigral dopaminergic pathways (e.g. mesolimbic, mesocortical), while others involve non-dopaminergic systems in the nervous system (e.g. cholinergic, noradrenergic, serotoninergic). Sleep (e.g. rapid eye movement behaviour disorder), olfactory and autonomic dysfunction (e.g. constipation) may precede the onset of parkinsonism by many years, consistent with the debated notion that PD pathology starts in the lower brainstem and that midbrain (i.e. nigral) involvement represents stage three out of six pathological stages. Considering parkinsonism as just the tip of the iceberg of a multifaceted and complex disorder, PD might be better viewed as a ‘centrosympathomyenteric neuronopathy,’ as per Langston. In this article, various non-motor aspects of PD (see Table 1) are discussed.

Variations on a theme: topic modeling of naturalistic driving data

This paper introduces Probabilistic Topic Modeling (PTM) as a promising approach to naturalistic driving data analyses. Naturalistic driving data present an unprecedented opportunity to understand driver behavior. Novel strategies are needed to achieve a more complete picture of these datasets than is provided by the local event-based analytic strategy that currently dominates the field. PTM is a text analysis method for uncovering word-based themes across documents. In this application, documents were represented by drives and words were created from speed and acceleration data using Symbolic Aggregate approximation (SAX). A twenty-topic Latent Dirichlet Allocation (LDA) topic model was developed using words from 10,705 documents (real-world drives) by 26 drivers. The resulting LDA model clustered the drives into meaningful topics. Topic membership probabilities were successfully used as features in subsequent analyses to differentiate between healthy drivers and those suffering from Obstructive Sleep Apnea.

Neuroergonomics of Sleep and Alertness

Sleepiness is a major problem in modern life. For example, more than a third of respondents to the National Sleep Foundation 2002 Sleep in America poll reported being so sleepy that it interfered with their daily activities at least a few days a month. According to the United States Bureau of Labor Statistics (2004), about 15% of the US workforce works outside of the regular daytime work hours of 8 a.m. to 5 p.m., and these shift workers have been shown to have shorter sleep durations and increased sleepiness during their major wake periods than those who regularly work during the day. Because chronic sleepiness is common and frequently associated with impaired cognition, it should come as no surprise that sleepy people will, at times, experience failures in critical aspects of daily functioning that may lead to catastrophic results. The crash of the Exxon Valdez and the Three Mile Island, Chernobyl and Challenger disasters are dramatic and often-quoted examples of the dangers of sleepiness, but countless other, less-celebrated, examples are found in everyday life. Sleepiness is a public health and work policy issue that cannot be ignored.

Using kinematic driving data to detect sleep apnea treatment adherence

ABSTRACT People spend a significant amount of time behind the wheel of a car. Recent advances in data collection facilitate continuously monitoring this behavior. Previous work demonstrates the importance of this data in driving safety but does not extended beyond the driving domain. One potential extension of this data is to identify driver states related to health conditions such as obstructive sleep apnea (OSA). We collected driving data and medication adherence from a sample of 75 OSA patients over 3.5 months. We converted speed and acceleration behaviors to symbols using symbolic aggregate approximation and converted these symbols to pattern frequencies using a sliding window. The resulting frequency data was matched with treatment adherence information. A random forest model was trained on the data and evaluated using a held-aside test dataset. The random forest model detects lapses in treatment adherence. An assessment of variable importance suggests that the important patterns of driving in classification correspond to route decisions and patterns that may be associated with drowsy driving. The success of this approach suggests driving data may be valuable for evaluating new treatments, analyzing side effects of medications, and that the approach may benefit other drowsiness detection algorithms.

Driving Performance and Driver State in Obstructive Sleep Apnea: What Changes with Positive Airway Pressure?

We evaluated naturalistic driving in 65 drivers with obstructive sleep apnea (OSA) before and after positive airway pressure (PAP) therapy and in 43 comparison drivers. Driving performance metrics included speed (mean, variability), and lateral, and longitudinal acceleration (gs). Driver state measures included sleepiness and attention to the driving task based on sampled trigger and baseline video clips. OSA drivers showed less variability in speed and lateral gs compared to control drivers before and after PAP treatment when vehicle speed was <45mph. There were no driving performance differences when vehicle speed exceeded 45 mph. OSA drivers remained less alert than comparison drivers before and after PAP. Average hours of nightly PAP-use predicted improved alertness and lower levels of sleepiness among OSA drivers. The findings suggest increased crash risk among OSA drivers may result from lower levels of attention to the driving task that result in performance lapses that may lead to crashes, rather than to clear and specific patterns of performance deficits in vehicle control.