The automatic seizure detection is significant for epilepsy diagnosis and it can alleviate the work intensity of inspecting prolonged electroencephalogram （EEG）. This paper presents and investigates a novel machine ...The automatic seizure detection is significant for epilepsy diagnosis and it can alleviate the work intensity of inspecting prolonged electroencephalogram （EEG）. This paper presents and investigates a novel machine learning approach utilizing gradient boosting to detect seizures from long-term EEG. We apply relative fluctuation index to extract features of long-term intracranial EEG data. A classifier trained with the gradient boosting algorithm is adopted to discriminate the seizure and non-seizure EEG signals. Smoothing and collar technique are finally used as post-processing in order to improve the detection accuracy further. The seizure detection method is assessed on Freiburg EEG datasets from 21 patients. The experimental results indicate that the proposed method yields an average sensitivity of 94. 60% with a false detection rate of 0. 18/h.展开更多
Monitoring students’ level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic s...Monitoring students’ level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic signals (EEG) as a tool to monitor the mental engagement index of novice medicine students during a reasoning process. More precisely, the objectives were first, to track students’ mental engagement evolution in order to investigate whether there were particular sections within the learning environment that aroused the highest engagement level among the students, and, if so, did these sections have an impact on learners’ performance. Experimental analyses showed the same trends in the different resolution phases as well as across the different regions of the environments. However, we noticed a higher engagement index during the treatment identification phase since it aroused more mental effort. Moreover statistically significant effects were found between mental engagement and students’ performance.展开更多
Autoregressive （AR） power spectral density estimate method was used to analyze the electroencephalogram （EEG） signals in eyes-open and eyes-closed states. From the topographical distributions of delta, theta, alph...Autoregressive （AR） power spectral density estimate method was used to analyze the electroencephalogram （EEG） signals in eyes-open and eyes-closed states. From the topographical distributions of delta, theta, alpha, and beta power spectrum, these two states can be clearly discriminated. In these two states, frontal areas were activated in delta power, both frontal and occipital areas were activated in theta band, and occipital areas were activated in alpha and beta bands. These four bands had significantly higher power in frontal, parietal, and occipital areas when eyes were close. The results also implied that the optimum order of AR model could be more suitable for estimating EEG power spectrum of different states.展开更多
Carotid angioplasty and stenting (CAS) was developed to be a less invasive and complex procedure compared to carotid endarterectomy (CEA). It has emerged as an alternative for patients who are considered to have high ...Carotid angioplasty and stenting (CAS) was developed to be a less invasive and complex procedure compared to carotid endarterectomy (CEA). It has emerged as an alternative for patients who are considered to have high surgical risks due to medical comorbidities or anatomical high-risk features [1]. The procedure is usually done under local anesthesia with light sedation, with the subsequent expectation of less neurologic injury, venous thromboembolisms, and myocardial infarctions—all well-known clinical risks of undergoing surgical procedures under general anesthesia. CAS, however, carries some increased risks of arterial dissection, dislocation of atherothrombotic debris and embolization to the brain or eye, late embolization due to thrombus formation on the damaged plaque, and bradycardia and hypotension as a result of carotid sinus stimulation. Electroencephalography can detect cerebral ischemia and hypoxia along with measuring hypnotic effects, but has not been reported to be used during CAS to signal impending neurological deficit and allow for intervention to prevent stroke. We report on the use of patient state index (PSI), an electroencephalographic (EEG) derived variable used by SEDLine monitor (Masimo Inc., San Diego, CA) to monitor changes in cerebral blood flow during carotid angioplasty and stenting in an awake patient under local anesthesia. PSI was developed to measure the level of hypnosis and sedation during anesthesia and in the ICU. The PSI is based on quantitative electroencephalogram features, recorded from anterior and posterior scalp sites, as input to a multivariate algorithm that quantifies the most probable level of anesthesia or sedation. The PSI is reported as a range from 0 to 100, with decreasing values indicating increasing levels of anesthesia or sedation. Adequate depth of anesthesia is reflected by PSI value of 25 - 50, and a fully awake state by a PSI of 100 [2]. Other EEG analysis techniques have been explored to detect changes in cerebral blood flow during carotid surgery [3], such as entropy described by Khan and Ozcan in his recent work entitled Disagreement in Bilateral State Entropy Values in Carotid Artery Disease [4], but there are no previous reports of the use of PSI during procedural sedation in carotid angioplasty and stenting in an awake patient.展开更多
基金Key Program of Natural Science Foundation of Shandong Province(No.ZR2013FZ002)The Program of Science and Technology of Suzhou(No.ZXY2013030)Independent Innovation Foundation of Shandong University(No.11170074611102)
文摘The automatic seizure detection is significant for epilepsy diagnosis and it can alleviate the work intensity of inspecting prolonged electroencephalogram （EEG）. This paper presents and investigates a novel machine learning approach utilizing gradient boosting to detect seizures from long-term EEG. We apply relative fluctuation index to extract features of long-term intracranial EEG data. A classifier trained with the gradient boosting algorithm is adopted to discriminate the seizure and non-seizure EEG signals. Smoothing and collar technique are finally used as post-processing in order to improve the detection accuracy further. The seizure detection method is assessed on Freiburg EEG datasets from 21 patients. The experimental results indicate that the proposed method yields an average sensitivity of 94. 60% with a false detection rate of 0. 18/h.
文摘Monitoring students’ level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic signals (EEG) as a tool to monitor the mental engagement index of novice medicine students during a reasoning process. More precisely, the objectives were first, to track students’ mental engagement evolution in order to investigate whether there were particular sections within the learning environment that aroused the highest engagement level among the students, and, if so, did these sections have an impact on learners’ performance. Experimental analyses showed the same trends in the different resolution phases as well as across the different regions of the environments. However, we noticed a higher engagement index during the treatment identification phase since it aroused more mental effort. Moreover statistically significant effects were found between mental engagement and students’ performance.
文摘Autoregressive （AR） power spectral density estimate method was used to analyze the electroencephalogram （EEG） signals in eyes-open and eyes-closed states. From the topographical distributions of delta, theta, alpha, and beta power spectrum, these two states can be clearly discriminated. In these two states, frontal areas were activated in delta power, both frontal and occipital areas were activated in theta band, and occipital areas were activated in alpha and beta bands. These four bands had significantly higher power in frontal, parietal, and occipital areas when eyes were close. The results also implied that the optimum order of AR model could be more suitable for estimating EEG power spectrum of different states.
文摘Carotid angioplasty and stenting (CAS) was developed to be a less invasive and complex procedure compared to carotid endarterectomy (CEA). It has emerged as an alternative for patients who are considered to have high surgical risks due to medical comorbidities or anatomical high-risk features [1]. The procedure is usually done under local anesthesia with light sedation, with the subsequent expectation of less neurologic injury, venous thromboembolisms, and myocardial infarctions—all well-known clinical risks of undergoing surgical procedures under general anesthesia. CAS, however, carries some increased risks of arterial dissection, dislocation of atherothrombotic debris and embolization to the brain or eye, late embolization due to thrombus formation on the damaged plaque, and bradycardia and hypotension as a result of carotid sinus stimulation. Electroencephalography can detect cerebral ischemia and hypoxia along with measuring hypnotic effects, but has not been reported to be used during CAS to signal impending neurological deficit and allow for intervention to prevent stroke. We report on the use of patient state index (PSI), an electroencephalographic (EEG) derived variable used by SEDLine monitor (Masimo Inc., San Diego, CA) to monitor changes in cerebral blood flow during carotid angioplasty and stenting in an awake patient under local anesthesia. PSI was developed to measure the level of hypnosis and sedation during anesthesia and in the ICU. The PSI is based on quantitative electroencephalogram features, recorded from anterior and posterior scalp sites, as input to a multivariate algorithm that quantifies the most probable level of anesthesia or sedation. The PSI is reported as a range from 0 to 100, with decreasing values indicating increasing levels of anesthesia or sedation. Adequate depth of anesthesia is reflected by PSI value of 25 - 50, and a fully awake state by a PSI of 100 [2]. Other EEG analysis techniques have been explored to detect changes in cerebral blood flow during carotid surgery [3], such as entropy described by Khan and Ozcan in his recent work entitled Disagreement in Bilateral State Entropy Values in Carotid Artery Disease [4], but there are no previous reports of the use of PSI during procedural sedation in carotid angioplasty and stenting in an awake patient.
