Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background and objective#R##N#Intraoperative awareness refers that patients can recall aspects of their surgery after being put under general anesthesia. This distressing complication causes affected patients to be conscious and probably feel pain, leading to emotional trauma or other sequelae. Monitoring and administrating the depth of anesthesia is necessary to prevent patients from awareness during a medical operation. ,  analyzed the electroencephalograms (EEGs) of patients to characterize their anesthesia. The data set, “awareness” and “anesthesia” groups, each contained 558 samples, including patients who had undergone different types of surgeries.#R##N#Methods#R##N#EEG signals acquired from patients in an aware state or under anesthesia re decomposed into a set of intrinsic mode functions (IMFs) through empirical mode decomposition (EMD). Fast Fourier transform (FFT) and Hilbert transform (HT) analyses re then performed on each IMF to determine the frequency spectra. The probability distributions of expected values of frequencies re generated for the same IMF in the two groups of patients. The corresponding statistical data, including analysis of variance tests, re also calculated. A receiver operating characteristic curve was used to identify optimal frequency value to discriminate beten the two states of consciousness.#R##N#Results#R##N#The frequencies of the IMFs for aware patients re found to be higher than those for anesthetized patients. The optimal frequency threshold by using FFT (or HT) for IMF 1 was 21.08 (or 25.00) Hz. IMF1 performed the highest with respect to the area under the curve (AUC) of 0.993 for FFT (or 0.989 for HT); hence it can be applied as a useful classifier to distinguish beten fully anesthetized patients and aware patients.#R##N#Conclusions#R##N# proposes a method for identifying whether patients' state of consciousness during a range of surgery types is “under anesthesia” or “aware.” Our method involves using EEG to characterize the depth of anesthesia through two frequency analysis techniques. On the basis of our analyses,  conclude that the performance of IMF1 is satisfactory in distinguishing beten patients' states of consciousness during surgery requiring general anesthesia.
['Shih-Jui Chen', 'Chia-Ju Peng', 'Yi-Chun Chen', 'Yean-Ren Hwang', 'Ying-Sian Lai', 'Shou-Zen Fan', 'Kuo-Kuang Jen']
I am writing a research paper on a idea Comparison of FFT and marginal spectra of EEG using empirical mode decomposition to monitor anesthesia
[{"from": "human", "value": "I am writing a research paper on a idea Comparison of FFT and marginal spectra of EEG using empirical mode decomposition to monitor anesthesia"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background and objective#R##N#Intraoperative awareness refers that patients can recall aspects of their surgery after being put under general anesthesia. This distressing complication causes affected patients to be conscious and probably feel pain, leading to emotional trauma or other sequelae. Monitoring and administrating the depth of anesthesia is necessary to prevent patients from awareness during a medical operation. ,  analyzed the electroencephalograms (EEGs) of patients to characterize their anesthesia. The data set, \u201cawareness\u201d and \u201canesthesia\u201d groups, each contained 558 samples, including patients who had undergone different types of surgeries.#R##N#Methods#R##N#EEG signals acquired from patients in an aware state or under anesthesia re decomposed into a set of intrinsic mode functions (IMFs) through empirical mode decomposition (EMD). Fast Fourier transform (FFT) and Hilbert transform (HT) analyses re then performed on each IMF to determine the frequency spectra. The probability distributions of expected values of frequencies re generated for the same IMF in the two groups of patients. The corresponding statistical data, including analysis of variance tests, re also calculated. A receiver operating characteristic curve was used to identify optimal frequency value to discriminate beten the two states of consciousness.#R##N#Results#R##N#The frequencies of the IMFs for aware patients re found to be higher than those for anesthetized patients. The optimal frequency threshold by using FFT (or HT) for IMF 1 was 21.08 (or 25.00) Hz. IMF1 performed the highest with respect to the area under the curve (AUC) of 0.993 for FFT (or 0.989 for HT); hence it can be applied as a useful classifier to distinguish beten fully anesthetized patients and aware patients.#R##N#Conclusions#R##N# proposes a method for identifying whether patients' state of consciousness during a range of surgery types is \u201cunder anesthesia\u201d or \u201caware.\u201d Our method involves using EEG to characterize the depth of anesthesia through two frequency analysis techniques. On the basis of our analyses,  conclude that the performance of IMF1 is satisfactory in distinguishing beten patients' states of consciousness during surgery requiring general anesthesia."}]