睡眠分期是评估睡眠质量的基础。然而,睡眠呼吸暂停(sleep apnea,SA)会改变测试者的睡眠结构,进而影响对睡眠分期的准确评估。因此,在评估睡眠质量时,准确检测睡眠呼吸暂停和睡眠分期至关重要。为准确评估睡眠分期,本研究通过研究脑区...睡眠分期是评估睡眠质量的基础。然而,睡眠呼吸暂停(sleep apnea,SA)会改变测试者的睡眠结构,进而影响对睡眠分期的准确评估。因此,在评估睡眠质量时,准确检测睡眠呼吸暂停和睡眠分期至关重要。为准确评估睡眠分期,本研究通过研究脑区之间的功能连接,探讨了脑功能连接的相互作用关系。采用锁相值(phase locking value,PLV)在不同时间段上进行特征提取,构建功能连接网络;然后利用多个时间段的PLV进行特征融合,并通过LibSVM(library for support vector machines,LibSVM)结合分类性能优化策略的方法进行睡眠分期。同时,本研究还分析了睡眠呼吸暂停和正常呼吸对脑网络的影响。实验结果显示,睡眠呼吸暂停时的各脑区连通紧密程度大于正常呼吸时,并在子时段数为30时,睡眠分期的分类准确率达到了88.87%,呼吸暂停的检测准确率达到了93.64%。该算法在睡眠分类和呼吸暂停检测方面表现出良好性能,有助于推动脑电睡眠分类和呼吸暂停检测系统的开发和应用。展开更多
The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the ru...The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the rules of Rechtschaffen and Kales(R&K rule) were used. Parameters were extracted from preprocessing process of EEG signal as feature vectors of each sleep stage analysis system through representatives of back propagation algorithm and support vector machine (SVM). As a result, SVM showed better performance as pattern recognition system for classification of sleep stages. It was found that easier analysis of sleep stage was possible using such simple system. Since accurate estimation of sleep state is possible through combination of algorithms, we could see the potential for the classifier to be used for sleep analysis system.展开更多
OBJECTIVE: To investigate the effects of the Sini San at different doses on each sleeping state[slow-wave sleep 1(SWS1), slow-wave sleep 2(SWS2), rapid-eye-movement(REM), wakefulness(W)] in insomnia rats and to identi...OBJECTIVE: To investigate the effects of the Sini San at different doses on each sleeping state[slow-wave sleep 1(SWS1), slow-wave sleep 2(SWS2), rapid-eye-movement(REM), wakefulness(W)] in insomnia rats and to identify its mode of ac-tion for improving sleep.METHODS: The insomnia rats were randomly divided into a high-, medium- or low-dose group of Sini San(equal to crude drug 8.8, 4.4, or 2.2 g/kg, respectively) for seven consecutive days.RESULTS: Compared with pre-administration,SWS2 was significantly increased after administration of the low dose. Compared with pre-administration, W was significantly decreased and SWS1,SWS2, and the total sleeping time(TST) were markedly increased after administration of the medium dose. Compared with pre-administration, W was significantly decreased and SWS1, SWS2, rapid-eye-movement sleep, and TST were significantly longer after administration of the high dose. The effects of Sini San on sleep-wake cycle are dose-dependent.CONCLUSION: The results suggest that Sini San extends SWS1 and SWS2, which increases the total sleeping time.展开更多
BACKGROUND： Routine electroencephalogram （EEG） usually cannot accurately reflect the discharge of epileptic patients due to the short examination, and long-term EEG can make up the shortcoming. OBJECTIVE： To compa...BACKGROUND： Routine electroencephalogram （EEG） usually cannot accurately reflect the discharge of epileptic patients due to the short examination, and long-term EEG can make up the shortcoming. OBJECTIVE： To comparatively analyze the long-term EEG of epileptic and non-epileptic patients, and investigate the values of long-term EEG in the diagnosis and differential diagnosis of epilepsy. DESIGN： A case-controlled study. SETTING： Ningjin County People＇s Hospital. PARTICIPANTS： Totally 122 patients with epilepsy （epilepsy group） were selected from the EEG room of Ningjin County People＇s Hospital from January 2000 to December 2006, including 76 males and 44 females, 7 months to 78 years of age, the disease course ranged from 7 days to 7.5 years, and they all according with the standards for epilepsy set by the International Association for Epilepsy in 1997. Meanwhile, 118 patients with non-epileptic paroxysmal diseases were selected as the control group, including 71 males and 47 females, 2.5-87 years of age, the disease course ranged from 3 days to 7.5 years. Informed contents were obtained from all the subjects. METHODS： OXFORD GATE WAY 2000 16-lead portable EEG recorder was used for 24-hour electroencephalographic procedure. The patients could move normally during the monitoring, their activities, sleeping conditions, time and manifestations of seizures were recorded in details. In the next day, EEG at wake was recorded for 10 minutes, followed by 3-minute hyperventilation and open/close eye induction test, the phases of non-rapid eye movement （Ⅰ-Ⅳ） and rapid eye movement were performed using EEG at sleep according to the international EEG standard. The abnormal rates of EEG epileptic discharge at wake and sleep at different sites were calculated. MAIN OUTCOME MEASURES： Abnormal rate of long-term EEG at wake and sleep in both groups; Epileptic discharge at different sleeping phases in both groups; Abnormal rates of EEG epileptic discharge at wake and sleep at different sites in th展开更多
Background:To observe the development of neonatal sleep among healthy infants of different conceptional age(CA)by analyzing the amplitude-integrated electroencephalography(aEEG)of their sleep-wake cycles(SWC).Methods:...Background:To observe the development of neonatal sleep among healthy infants of different conceptional age(CA)by analyzing the amplitude-integrated electroencephalography(aEEG)of their sleep-wake cycles(SWC).Methods:Bedside aEEG monitoring was carried out for healthy newborns from 32 to 46 weeks CA between September 1,2011 and August 30,2012.For each aEEG tracing,mean duration of every complete SWC,number of SWC repetition within 12 hours,mean duration of each narrow and broadband of SWC,mean voltage of the upper edge and lower edge of SWC,mean bandwidth of SWC were counted and calculated.Analysis of the correlations between voltages or bandwidth of SWC and CA was performed to assess the developmental changes of central nervous system of newborns with different CA.Results:The SWC of different CA on aEEG showed clearly identifiable trend after 32 weeks of CA.The occurrence of SWC gradually increases from preterm to post-term infants;term infants had longer SWC duration.The voltage of upper edge of the broadband decreased at 39 weeks,while the lower edge voltage increases and the bandwidth of broadband declined along with the growing CA.The upper edge of the narrowband dropped while the lower edge rised gradually,especially in preterm stage.The width of the narrowband narrowed down while CA increased.Conclusions:The SWC on aEEG of 32-46 weeks infants showed a continuous,dynamic and developmental progress.The appearance of SWC and the narrowing bandwidth of narrowband is the main indicator to identify the CA-dependent SWC from the preterm to the late preterm period.The lower edge of the broadband identifi es the term to post-term period.展开更多
文摘睡眠分期是评估睡眠质量的基础。然而,睡眠呼吸暂停(sleep apnea,SA)会改变测试者的睡眠结构,进而影响对睡眠分期的准确评估。因此,在评估睡眠质量时,准确检测睡眠呼吸暂停和睡眠分期至关重要。为准确评估睡眠分期,本研究通过研究脑区之间的功能连接,探讨了脑功能连接的相互作用关系。采用锁相值(phase locking value,PLV)在不同时间段上进行特征提取,构建功能连接网络;然后利用多个时间段的PLV进行特征融合,并通过LibSVM(library for support vector machines,LibSVM)结合分类性能优化策略的方法进行睡眠分期。同时,本研究还分析了睡眠呼吸暂停和正常呼吸对脑网络的影响。实验结果显示,睡眠呼吸暂停时的各脑区连通紧密程度大于正常呼吸时,并在子时段数为30时,睡眠分期的分类准确率达到了88.87%,呼吸暂停的检测准确率达到了93.64%。该算法在睡眠分类和呼吸暂停检测方面表现出良好性能,有助于推动脑电睡眠分类和呼吸暂停检测系统的开发和应用。
文摘The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the rules of Rechtschaffen and Kales(R&K rule) were used. Parameters were extracted from preprocessing process of EEG signal as feature vectors of each sleep stage analysis system through representatives of back propagation algorithm and support vector machine (SVM). As a result, SVM showed better performance as pattern recognition system for classification of sleep stages. It was found that easier analysis of sleep stage was possible using such simple system. Since accurate estimation of sleep state is possible through combination of algorithms, we could see the potential for the classifier to be used for sleep analysis system.
基金Supported by Hippocampus Neural Coding Mechanism Research on Sini San Intervention Sleep Disorders of PTSD in Myospalax cansus from the National Natural Science Foundation(No.81460611)Study on Sini San for regulation of expression of proteins of drosophila brain of sleep deprivation of Gansu Province Natural Science Foundation(No.145RJZA076)+3 种基金Fundamental Research Funds for the Gansu Provincial Department of Finance Universities(No.2013-2)Mechanisms of hippocampal neurons based on Jiawei Sini San intervention coding mode PTSD sleep disordersMinistry of Education,Sini San for intervention of sleep deprivation in drosophila Based nano-2D-LC/MS technology of Science and Technology Key Project(No.212186)Proteomics and effective substance basic of Sini San for improving sleep of Gansu Province Natural Science Foundation(No.1010RJZA212)
文摘OBJECTIVE: To investigate the effects of the Sini San at different doses on each sleeping state[slow-wave sleep 1(SWS1), slow-wave sleep 2(SWS2), rapid-eye-movement(REM), wakefulness(W)] in insomnia rats and to identify its mode of ac-tion for improving sleep.METHODS: The insomnia rats were randomly divided into a high-, medium- or low-dose group of Sini San(equal to crude drug 8.8, 4.4, or 2.2 g/kg, respectively) for seven consecutive days.RESULTS: Compared with pre-administration,SWS2 was significantly increased after administration of the low dose. Compared with pre-administration, W was significantly decreased and SWS1,SWS2, and the total sleeping time(TST) were markedly increased after administration of the medium dose. Compared with pre-administration, W was significantly decreased and SWS1, SWS2, rapid-eye-movement sleep, and TST were significantly longer after administration of the high dose. The effects of Sini San on sleep-wake cycle are dose-dependent.CONCLUSION: The results suggest that Sini San extends SWS1 and SWS2, which increases the total sleeping time.
文摘BACKGROUND： Routine electroencephalogram （EEG） usually cannot accurately reflect the discharge of epileptic patients due to the short examination, and long-term EEG can make up the shortcoming. OBJECTIVE： To comparatively analyze the long-term EEG of epileptic and non-epileptic patients, and investigate the values of long-term EEG in the diagnosis and differential diagnosis of epilepsy. DESIGN： A case-controlled study. SETTING： Ningjin County People＇s Hospital. PARTICIPANTS： Totally 122 patients with epilepsy （epilepsy group） were selected from the EEG room of Ningjin County People＇s Hospital from January 2000 to December 2006, including 76 males and 44 females, 7 months to 78 years of age, the disease course ranged from 7 days to 7.5 years, and they all according with the standards for epilepsy set by the International Association for Epilepsy in 1997. Meanwhile, 118 patients with non-epileptic paroxysmal diseases were selected as the control group, including 71 males and 47 females, 2.5-87 years of age, the disease course ranged from 3 days to 7.5 years. Informed contents were obtained from all the subjects. METHODS： OXFORD GATE WAY 2000 16-lead portable EEG recorder was used for 24-hour electroencephalographic procedure. The patients could move normally during the monitoring, their activities, sleeping conditions, time and manifestations of seizures were recorded in details. In the next day, EEG at wake was recorded for 10 minutes, followed by 3-minute hyperventilation and open/close eye induction test, the phases of non-rapid eye movement （Ⅰ-Ⅳ） and rapid eye movement were performed using EEG at sleep according to the international EEG standard. The abnormal rates of EEG epileptic discharge at wake and sleep at different sites were calculated. MAIN OUTCOME MEASURES： Abnormal rate of long-term EEG at wake and sleep in both groups; Epileptic discharge at different sleeping phases in both groups; Abnormal rates of EEG epileptic discharge at wake and sleep at different sites in th
基金This work was supported by the Guangzhou Science Technology and Innovation Commission 1563000668(Lian Zhang).
文摘Background:To observe the development of neonatal sleep among healthy infants of different conceptional age(CA)by analyzing the amplitude-integrated electroencephalography(aEEG)of their sleep-wake cycles(SWC).Methods:Bedside aEEG monitoring was carried out for healthy newborns from 32 to 46 weeks CA between September 1,2011 and August 30,2012.For each aEEG tracing,mean duration of every complete SWC,number of SWC repetition within 12 hours,mean duration of each narrow and broadband of SWC,mean voltage of the upper edge and lower edge of SWC,mean bandwidth of SWC were counted and calculated.Analysis of the correlations between voltages or bandwidth of SWC and CA was performed to assess the developmental changes of central nervous system of newborns with different CA.Results:The SWC of different CA on aEEG showed clearly identifiable trend after 32 weeks of CA.The occurrence of SWC gradually increases from preterm to post-term infants;term infants had longer SWC duration.The voltage of upper edge of the broadband decreased at 39 weeks,while the lower edge voltage increases and the bandwidth of broadband declined along with the growing CA.The upper edge of the narrowband dropped while the lower edge rised gradually,especially in preterm stage.The width of the narrowband narrowed down while CA increased.Conclusions:The SWC on aEEG of 32-46 weeks infants showed a continuous,dynamic and developmental progress.The appearance of SWC and the narrowing bandwidth of narrowband is the main indicator to identify the CA-dependent SWC from the preterm to the late preterm period.The lower edge of the broadband identifi es the term to post-term period.
