COMPLEXITY-BASED ANALYSIS OF THE VARIATIONS IN BRAIN ACTIVITY IN SHORT DEEP BREATHING

Investigating the impact of deep breathing on brain activity is a crucial research area in biomedical science and engineering. In this paper, we examine the alterations in the complex structure of electroencephalogram (EEG) signals, which serve as indicators of brain activity, during rest and varying durations of short deep breathing exercises. We analyzed the fractal dimension (FD), approximate entropy (ApEn), and sample entropy (SampEn) of EEG signals during normal breathing, deep breathing sessions lasting 5, 7, and 9 min, immediately after deep breathing, and in a follow-up session conducted seven days later. The findings revealed that for all durations of deep breathing, the complexity of EEG signals decreased compared to the pre-deep breathing (normal breathing) baseline. However, the complexity of EEG signals increased after the deep breathing sessions and continued to rise in the follow-up session. On the other hand, the longer duration of deep breathing causes greater decreases in the complexity of EEG signals during deep breathing, after that, and also in the follow-up session. This trend suggests that extended periods of deep breathing may lead to sustained changes in neural activity.