Recently, the correlation between the level of alpha waves and insomnia has been studied. Research papers such as “Alpha-waves frequency characteristics in health and insomnia during sleep” published in the Journal of Sleep Research in January 2016, and “Covert Waking Brain Activity Reveals Instantaneous Sleep Depth,” published by Harvard University in March 2011 suggest the level of alpha waves carries information about sleep stability. Also, quantitative research about “EEG spectral analysis of NREM sleep in a large sample of patients with insomnia and good sleepers: effects of age, sex and part of the night” published in September 2016 evaluates differences between patients with insomnia (N=803) and those who are not affected (N=811). Often the number of participants is limited to at most 15, but this specific research with quantitative data could obtain statistically significant correlation.
Several research papers have attempted to detect insomnia-related changes by means of the EEG, and some of the authors adopted power spectral analysis as a method to detect the signals (Freedman, 1986; Merica et al., 1998; Spiegelhalder et al., 2012). The increased alpha, beta, sigma and gamma power found during sleep in such patients are consistent with the theory of chronic, pathological hyper-arousal (Feige et al., 2013; Riemann et al., 2010, 2015).
The frequency of alpha waves measured by the EEG is a strikingly stable trait in healthy humans with a population standard deviation of only about 1 Hz (Grandy et al., 2013; Niedermeyer et al., 1997). Recently, a correlation between the power spectrum level of alpha waves and certain pathological processes has been revealed. Especially, larger deviations from the normal, physiological alpha range have been associated with clinical symptoms such as deteriorating working memory performance, schizophrenia, Alzheimer’s disease, and addiction. As quantitative and qualitative research papers about the correlation between the level of alpha waves and insomnia have been published recently, we will implement a system that can accurately self-diagnose insomnia given a person’s dataset. We are looking forward to expanding our research to implement similar models for diagnosing other pathological processes mentioned above after finishing our senior design project.