A look at the strength of micro and macro EEG analysis for distinguishing insomnia within an HIV cohort - PubMed (original) (raw)

A look at the strength of micro and macro EEG analysis for distinguishing insomnia within an HIV cohort

Kristin M Gunnarsdottir et al. Annu Int Conf IEEE Eng Med Biol Soc. 2015.

Abstract

HIV patients are often plagued by sleep disorders and suffer from sleep deprivation. However, there remains a wide gap in our understanding of the relationship between HIV status, poor sleep, overall function and future outcomes; particularly in the case of HIV patients otherwise well controlled on cART (combined anti-retroviral therapy). In this study, we compared two groups: 16 non-HIV subjects (seronegative controls) and 12 seropositive HIV patients with undetectable viral loads. We looked at sleep behavioral (macro-sleep) features and sleep spectral (micro-sleep) features obtained from human-scored overnight EEG recordings to study whether the scored EEG data can be used to distinguish between controls and HIV subjects. Specifically, the macro-sleep features were defined by sleep stages and included sleep transitions, percentage of time spent in each sleep stage, and duration of time spent in each sleep stage. The micro-sleep features were obtained from the power spectrum of the EEG signals by computing the total power across all channels and frequencies, as well as the average power in each sleep stage and across different frequency bands. While the macro features do not distinguish between the two groups, there is a significant difference and a high classification accuracy for the scoring-independent micro features. This spectral separation is interesting because evidence suggests a relationship between sleep complaints and cognitive dysfunction in HIV patients stable on cART. Furthermore, there are currently no biomarkers that predict the early development of cognitive decline in HIV patients. Thus, a micro-sleep architectural approach could serve as a biomarker to identify HIV patients vulnerable to cognitive decline, providing an avenue to explore the utility of early intervention.

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Figures

Figure 1

Figure 1

EEG electrode placements in PSG.

Figure 2

Figure 2

Normalized counts of all sleep transitions as a feature vector in PC space. There is little to no separation between controls and HIV participants (top). The total average power over the entire night gives the most significant difference between the two groups and yields the highest accuracy of classifying subjects into controls and HIV participants (bottom).

Figure 3

Figure 3

The accuracy of the likelihood ratio classification for sleep transitions with the three lowest p-values without correction (top), the percentage of time spent in each sleep stage (middle) and the duration of time spent in each sleep stage (bottom). Not enough data was available to calculate the accuracy for stage N3 for this feature.

Figure 4

Figure 4

The accuracy of the likelihood ratio classification for the scoring-independent spectral features (top). The highest accuracy was obtained using the average total power over the entire night. The accuracy of the likelihood ratio classification for the scoring-dependent spectral features (bottom). All features had a low classification accuracy.

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