The Relationship between Sleep Apnea and Cognitive Functioning

The Relationship between Sleep Apnea and Cognitive Functioning

M. S. S. Khan (University of Louisville, USA)
DOI: 10.4018/978-1-61520-723-7.ch017

Abstract

The brain is the most complicated and least studied area of Neuro Sceince. In recent times, it has been one of the fastest growing areas of study in the Medical Sciences. This is mostly due to computers and computational techniques that have emerged in the last 10-15 years. Cognitive Neuropsychology aims to understand how the structure and function of the brain relates to psychological processes. It places emphasis on studying the cognitive effects of brain injury or neurological illness with a view to inferring models of normal cognitive functioning. We investigate the relationship between sleep apnea and learning disorders. Sleep apnea is a neural disorder, where individuals find it difficult to sleep because they stop breathing. We want to see if patients with learning disabilities should be treated for sleep apnea.
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Background

Sleep Apnea

We have a number of different datasets of EEG data of children collected concerning both the reactions to different stimuli and from sleep apnea studies. These data have been completely de-identified and will be used throughout this project. Generally, EEG data are collected using a Geodesic Sensor Net.(Johnson et al., 2001; Tucker, 1993) This is a system that allows the mapping of brain activity data using a cap containing 128 electrodes. The cap is placed on the subject’s head. This system makes it much easier to collect brain activity data from children since previously, collecting these type of data required electrodes to be placed on a person’s head one-at-a-time using applicator gel. Most children will not sit still for this type of research. The Geodesic Sensor Net allows all of the electrodes to be placed on a child’s head at once and collection of data is much faster than using the old EEG or electro-encephalogram method. Data are recorded at fixed time intervals, usually measured in seconds. For example, in the data analyzed for any one child, an entire collection of 250 data points can be recorded in under 20 minutes.

Figure 1 represents the positioning of the electrodes for a 128 channel Geodesic Sensor Net.

Figure 1.

Geodesic sensor net configuration

The electrodes that appear vertically in the center of the chart, running from top to bottom, divide the other electrodes into left and right sections. For statistical analyses, all electrode readings from the left side of the brain can be averaged to one value as can all values from the right side of the brain. The brain positioning of all electrodes is shown in Table 1.

Table 1.
Electrode/Brain position
PositionBrain PositionElectrodes
FLFront Left18 19 20 22 23 24 25 26 27 28 33 34 39 128
FRFront Right1 2 3 4 8 9 10 14 15 121 122 123 124 125
CLCenter Left7 12 13 21 29 30 31 32 35 36 37 38 41 42 43 46 47 48 51
CRCenter Right5 81 88 94 98 99 103 104 105 106 107 109 110 111 112 113 117 118 119
PLParietal Left54 61 67 53 60 52 59 58 64 63
PRParietal Right78 79 80 87 86 93 92 97 96 100
OLOccipital Left65 66 69 70 71 72 74 75
OROccipital Right77 83 84 85 89 90 91 95
TLTemporal Left40 44 45 49 50 56 57
TRTemporal Right101 102 108 114 115 116 120

The EEG data, then, have hundreds, sometimes, thousands of data points recorded in sequence from each of the net sensors. There may be just a handful of subjects in a study, with each subject having these multiple recordings of data. The methods used to model the data must be able to accommodate the type of data collected.

For each electrode, then, we have a sequence Xi1, Xi2, …, Xin representing the first to the last timed reading (assuming a total of n readings). The value i represents the specific electrode. This sequence is not a random sample since it is clear that Xi,t is related to Xi,t+1. It is also questionable whether we can assume stationarity, meaning that Xi,t and Xi,t+1 have the same probability distribution. For the purposes of this study, we will make such an assumption. Moreover, if i and j are in the same general location, we must assume that Xi,t and Xj,t are related in some way.

Because of these relationships and the lack of randomness in the variables, we cannot use standard regression techniques to investigate the data because these techniques make the assumption that the data are both independent and identically distributed, as well as coming from a normal distribution. Such assumptions are clearly false in data collected from EEG monitoring. In the past, attempts have been made to classify, or group the EEG readings to simplify the problem.(Kook, Gupta, Kota, & Molfese, 2007) Another approach, specifically used in hypothesis testing, has been to reduce the sample data to its averages, and to analyze the average.(Mayes, Molfese, Key, & Hunter, 2005) Such an approach greatly reduces the amount of information from the EEG data that is used in the analysis. By using techniques that were specifically designed to work with these types of data, we can greatly expand the amount of knowledge extracted from the data. Therefore, we must work with techniques that do not assume independence in the data points.

The three techniques that will be used in the proposed short course are part of the general topic of data mining. Data mining is a general term that is used to describe a process of data analysis, beginning with required data preprocessing followed by exploration and hypothesis generation and ending with the validation of results and their use in making decisions from the data.

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