การวิเคราะห์ความสัมพันธ์ระหว่างสัญญาณสมองและการหายใจโดยใช้เทคนิคการแปลงเวฟเล็ต

Abstract

This study aims to propose an approach for monitoring respiratory rhythm using electroencephalogram (EEG) signals. The Discrete Wavelet Transform (DWT) technique was applied to extract significant features from EEG signals associated with diaphragmatic breathing patterns. In this experiment, EEG signals were recorded from eight electrode channels based on the international 10􀄱10 electrode placement system. The channels were divided into two groups: Group 1 consisting of C3, C4, Cz, and T8, and Group 2 consisting of Fz, FC2, F4, and FC6. The experimental procedure involved simultaneous acquisition of EEG and respiratory signals from five volunteers. Participants were instructed to perform diaphragmatic breathing under natural unpaced conditions as well as different breathing patterns, including rapid breathing, normal breathing, and deep breathing. The recorded EEG signals were then analyzed using the Discrete Wavelet Transform technique. The results revealed that respiratory-related EEG components were primarily observed within the D8-A9 decomposition levels. Correlation Coefficient analysis was employed to evaluate the relationship between the extracted EEG components and respiratory signals. The highest correlation coefficient obtained in this study was (r = 0.8). These findings indicate that wavelet transform techniques are capable of extracting respiratory related characteristics from EEG signals. Therefore, this approach may serve as an alternative method for developing brain-signal feedback systems for respiratory training in patients with chronic pulmonary diseases in the future.