Temporal characterization of electroencephalogram slowing activity types

Life-threatening diseases often remain undetected until irreversible consequences manifest. EEG (electroencephalogram; electrical activity in the brain) slowing, a common phenomenon in diseases like epilepsy and dementia, also appears in other critical conditions. In this study, we analyzed data samples from the Temple University Hospital dataset — comprised of a large general population — of EEG slowing to discern distinct characteristics. We hypothesized that we would identify distinct slowing characteristics and patterns in EEG data, identified through various analysis methods showing that detection and categorization of these patterns may serve as crucial indicators for the early detection of life-threatening diseases. We identified characteristics such as generalized or focal slowing and classified them into three categories. Through time-frequency analysis, frequency-domain clustering, time-domain clustering, and additional frequency analysis methods, we explored variations in EEG slowing patterns. Our findings indicate that computational analysis using K-Means clustering, UMAP, and t-distributed Stochastic Neighbor Embedding (t-SNE) algorithms against EEG data is able to identify distinct slowing patterns, suggesting that EEG features could be used for early detection and be pivotal in early intervention and prevention treatment strategies, thus confirming our hypothesis. This study highlights the critical features of EEG slowing and how these features correlate to specific types of slowing, suggesting a promising path toward new insights into disease-prevention mechanisms and further insights into disease etiology. A comprehensive understanding of the temporal aspects of EEG slowing may lead to further insights into the etiology of these diseases and facilitate future discoveries.