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論文
【年度】103
年研發成果
【項目】
論文
【領域】
關鍵技術科專
【類別】
生醫材化
| 計畫名稱 | 整合式麻醉深度監測儀開發 | | 論文名稱 | Applications of Empirical Mode Decomposition and Hilbert-Huang Transform in EEG signal analysis | | 論文類型 | 研討會 | | 發表處 | The 1st Global Conference on Biomedical Engineering (GCBME 2014) and 9th Asian Pacific Conference on medical and Biological Engineering (APCMBE 2014), | | 發表人 | M. T. Shih, F. Doctor,S. Z. Fan, K. K. Jen,J. S. Shieh | | 發表日期 | 2014/10/09 | | 國家 | 國外 | | 內容摘要 | Depth of anesthesia (DOA) is an important measure for assessing the degree to which the central nervous system of a patient is depressed by a general anesthetic agent, depending on the potency and concentration with which anesthesia is administered during surgery. We can monitor the DOA by observing thepatient’sElectroencephalography (EEG) signals during the surgical procedure. Ahigh frequency EEG signalsindicates the patient is conscious. On the contrary, low frequency of EEG signals mean the patient is inageneral anes-thetic state. If the anesthetistis able to observe the instantane-ous frequency changes of the patient’s EEG signalsduring surgerythis can help to better regulate and monitor DOA reducing surgical and post-operative risks.This paper describe an approach towards the development of a3D real-time visua-lization application which can show the instantaneous frequen-cy and instantaneous amplitude of EEG simultaneously by using empirical mode decomposition (EMD) and Hilbert-Huang transform (HHT).We investigate this approach based onanalyzing EEG data collected from patients undergoing surgical procedures.The result shows that this approach is able to distinguish between key operational stages related to DOA, which is consistent with the clinical observation. |
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