자율신경계의 활성도 측정을 위한 Data Acquisition System의 개발 및 임상응용
Development of Data Acquisition System for Quantification of Autonomic Nervous System Activity and It's Clinic Use
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Background: Power spectrum analysis method is a powerful noninvasive tool for quantifying autonomic nervous system activity. In this paper, we developed a data acquistion system for estimating the activity of the autonomic nervous system by the analysis of heart rate and respiratory rate variability using power spectrum analysis. Materials and Methods: For the detection of QRS peak and measurement of respiratory rate from patient's ECG, we used low-pass filter and impedence method respectively. This system adopt an isolated power for patient's safety. In this system, two output signnals can be obtained: R-R interval(heart rate) and respiration rate time series. Experimental ranges are 30-240 BPM for ECG and 15-80 BPM for respiration. Results: The system can acquire two signals accurately both in the experimental test using simulator and in real clinical setting. Conclusion: The system developed in this paper if efficient for the acquisition of heart rate and respiration signals. The system will play a role in research area for improving our understanding of the pathophysiologic involvement of the autonomic nervous system in various disease states.
Abstract
Background: Power spectrum analysis method is a powerful noninvasive tool for quantifying autonomic nervous system activity. In this paper, we developed a data acquistion system for estimating the activity of the autonomic nervous system by the analysis of heart rate and respiratory rate variability using power spectrum analysis. Materials and Methods: For the detection of QRS peak and measurement of respiratory rate from patient's ECG, we used low-pass filter and impedence method respectively. This system adopt an isolated power for patient's safety. In this system, two output signnals can be obtained: R-R interval(heart rate) and respiration rate time series. Experimental ranges are 30-240 BPM for ECG and 15-80 BPM for respiration. Results: The system can acquire two signals accurately both in the experimental test using simulator and in real clinical setting. Conclusion: The system developed in this paper if efficient for the acquisition of heart rate and respiration signals. The system will play a role in research area for improving our understanding of the pathophysiologic involvement of the autonomic nervous system in various disease states.