<strong>Background:</strong><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> The epileptic encephalo...<strong>Background:</strong><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> The epileptic encephalopathies collectively</span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">exact an immense personal, medical, and financial toll on</span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the affected children, their families, and</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">the healthcare system.</span><b><span style="font-family:Verdana;"> Objective:</span></b><span style="font-family:Verdana;"> This study was aimed to delineate the clinical spectrum of patients with Epileptic encephalopathies (EEs) and classify them under various epileptic syndromes. </span><b><span style="font-family:Verdana;">Methods:</span></b><span style="font-family:Verdana;"> This was a cross-sectional study that was carried out in the department of Neurophysiology of the National Institute of Neurosciences and Hospital, Bangladesh from July 2016 to June 2019.</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">Children with recurrent seizures which w</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ere </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">difficult to control and associated with developmental arrest or regression in absence of a progressive brain pathology were considered to be suffering from EE. Children under 12 years of age fulfilling the inclusion criteria were enrolled in the study. These patients were evaluated clinically and Electroencephalography (EEG) was done in all children at presentation. Based on the clinical profile and EEG findings the patients were categorized under various epileptic syndromes according to International League Against Epilepsy (ILAE) classification 2010.</span><b><span style="font-family:Verdana;"> Results:</span></b><span style="font-family:Verdana;"> A total of 1256 children under 12 years of age were referred to the Neurophysiology Department. Among them, 162</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">(12.90%) fulfilled the inclusion criteria. Most of the patients were male (64.2%) and below 1 year (37.7%) of age. The majority (56.8%) were delivered at the hospital and 40.1% had a history of perinatal asphyxia. Development was age-appropriate before the onset of a seizure in 38.9% of cases. Most (53.7%) of the patients had seizure onset within 3 months of age. Categorization of Epileptic syndromes found that majority had West Syndrome (WS)</span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">(37.65%) followed by Lennox-Gastaut syndrome (LGS) (22.22%), Otahara syndrome (11.73%), Continuous spike-and-wave during sleep (CSWS) (5.66%), Myoclonic astatic epilepsy (MAE)</span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">(4.94%), Early myoclonic encephalopathy (EME) (3.7%), Dravet</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">syndrome (3.7%) and Landau-Kleffner syndrome (LKS) (1.23%). 9.26% of syndromes were unclassified. </span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"> EEG was found to be a useful tool in the evaluation of Epileptic encephalopathies. The clinico-electroencephalographic features are age-related. Their recognition and appropriate management are critical.</span></span></span></span>展开更多
Manual acupuncture is widely used for pain relief and stress control.Previous studies on acupuncture have shown its modulatory effects on the functional connectivity associated with one or a few preselected brain regi...Manual acupuncture is widely used for pain relief and stress control.Previous studies on acupuncture have shown its modulatory effects on the functional connectivity associated with one or a few preselected brain regions.To investigate how manual acupuncture modulates the organization of functional networks at a whole-brain level,we acupuncture at ST36 of a right leg to obtain electroencephalograph(EEG) signals.By coherence estimation,we determine the synchronizations between all pairwise combinations of EEG channels in three acupuncture states.The resulting synchronization matrices are converted into functional networks by applying a threshold,and the clustering coefficients and path lengths are computed as a function of threshold.The results show that acupuncture can increase functional connections and synchronizations between different brain areas.For a wide range of thresholds,the clustering coefficient during acupuncture and postacupuncture period is higher than that during the pre-acupuncture control period,whereas the characteristic path length is shorter.We provide further support for the presence of "small-world" network characteristics in functional networks by using acupuncture.These preliminary results highlight the beneficial modulations of functional connectivity by manual acupuncture,which could contribute to the understanding of the effects of acupuncture on the entire brain,as well as the neurophysiological mechanisms underlying acupuncture.Moreover,the proposed method may be a useful approach to the further investigation of the complexity of patterns of interrelations between EEG channels.展开更多
To investigate whether and how manual acupuncture(MA) modulates brain activities,we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph(EEG) signals in heal...To investigate whether and how manual acupuncture(MA) modulates brain activities,we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph(EEG) signals in healthy subjects.We adopt the autoregressive(AR) Burg method to estimate the power spectrum of EEG signals and analyze the relative powers in delta(0 Hz-4 Hz),theta(4 Hz-8 Hz),alpha(8 Hz-13 Hz),and beta(13 Hz-30 Hz) bands.Our results show that MA at ST36 can significantly increase the EEG slow wave relative power(delta band) and reduce the fast wave relative powers(alpha and beta bands),while there are no statistical differences in theta band relative power between different acupuncture states.In order to quantify the ratio of slow to fast wave EEG activity,we compute the power ratio index.It is found that the MA can significantly increase the power ratio index,especially in frontal and central lobes.All the results highlight the modulation of brain activities with MA and may provide potential help for the clinical use of acupuncture.The proposed quantitative method of acupuncture signals may be further used to make MA more standardized.展开更多
As a convenient approach to the characterization of cerebral cortex electrical information,electroencephalograph (EEG) has potential clinical application in monitoring the acupuncture effects.In this paper,a method co...As a convenient approach to the characterization of cerebral cortex electrical information,electroencephalograph (EEG) has potential clinical application in monitoring the acupuncture effects.In this paper,a method composed of the mutual information method and Lempel-Ziv complexity method (MILZC) is proposed to investigate the effects of acupuncture on the complexity of information exchanges between different brain regions based on EEGs.In the experiments,eight subjects are manually acupunctured at 'Zusanli' acupuncture point (ST-36) with different frequencies (i.e.,50,100,150,and 200 times/min) and the EEGs are recorded simultaneously.First,MILZC values are compared in general.Then average brain connections are used to quantify the effectiveness of acupuncture under the above four frequencies.Finally,significance index P values are used to study the spatiality of the acupuncture effect on the brain.Three main findings are obtained:(i) MILZC values increase during the acupuncture;(ii) manual acupunctures (MAs) with 100 times/min and 150 times/min are more effective than with 50 times/min and 200 times/min;(iii) contralateral hemisphere activation is more prominent than ipsilateral hemisphere's.All these findings suggest that acupuncture contributes to the increase of brain information exchange complexity and the MILZC method can successfully describe these changes.展开更多
Machine learning(ML)is a fundamental concept in the field of state-of-the-art artificial intelligence(AI).Over the past two decades,it has evolved rapidly and been employed wildly in many fields.In medicine the widesp...Machine learning(ML)is a fundamental concept in the field of state-of-the-art artificial intelligence(AI).Over the past two decades,it has evolved rapidly and been employed wildly in many fields.In medicine the widespread usage of ML has been observed in recent years.The present review examines various ML approaches for electroencephalograph(EEG)signal procession in epilepsy research,highlighting applications in the aspect of automated seizure detection,prediction and orientation.The present review also presents advantage,challenge and future direction of ML techniques in the analysis of EEG signals in epilepsy.展开更多
Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the ...Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.展开更多
文摘<strong>Background:</strong><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> The epileptic encephalopathies collectively</span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">exact an immense personal, medical, and financial toll on</span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the affected children, their families, and</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">the healthcare system.</span><b><span style="font-family:Verdana;"> Objective:</span></b><span style="font-family:Verdana;"> This study was aimed to delineate the clinical spectrum of patients with Epileptic encephalopathies (EEs) and classify them under various epileptic syndromes. </span><b><span style="font-family:Verdana;">Methods:</span></b><span style="font-family:Verdana;"> This was a cross-sectional study that was carried out in the department of Neurophysiology of the National Institute of Neurosciences and Hospital, Bangladesh from July 2016 to June 2019.</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">Children with recurrent seizures which w</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ere </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">difficult to control and associated with developmental arrest or regression in absence of a progressive brain pathology were considered to be suffering from EE. Children under 12 years of age fulfilling the inclusion criteria were enrolled in the study. These patients were evaluated clinically and Electroencephalography (EEG) was done in all children at presentation. Based on the clinical profile and EEG findings the patients were categorized under various epileptic syndromes according to International League Against Epilepsy (ILAE) classification 2010.</span><b><span style="font-family:Verdana;"> Results:</span></b><span style="font-family:Verdana;"> A total of 1256 children under 12 years of age were referred to the Neurophysiology Department. Among them, 162</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">(12.90%) fulfilled the inclusion criteria. Most of the patients were male (64.2%) and below 1 year (37.7%) of age. The majority (56.8%) were delivered at the hospital and 40.1% had a history of perinatal asphyxia. Development was age-appropriate before the onset of a seizure in 38.9% of cases. Most (53.7%) of the patients had seizure onset within 3 months of age. Categorization of Epileptic syndromes found that majority had West Syndrome (WS)</span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">(37.65%) followed by Lennox-Gastaut syndrome (LGS) (22.22%), Otahara syndrome (11.73%), Continuous spike-and-wave during sleep (CSWS) (5.66%), Myoclonic astatic epilepsy (MAE)</span></span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">(4.94%), Early myoclonic encephalopathy (EME) (3.7%), Dravet</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">syndrome (3.7%) and Landau-Kleffner syndrome (LKS) (1.23%). 9.26% of syndromes were unclassified. </span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"> EEG was found to be a useful tool in the evaluation of Epileptic encephalopathies. The clinico-electroencephalographic features are age-related. Their recognition and appropriate management are critical.</span></span></span></span>
基金Project supported by the Key Program of the National Natural Science Foundation of China (Grant No. 50537030)the National Natural Science Foundation of China (Grant Nos. 61072012 and 61172009)+1 种基金the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos. 61104032 and 60901035)the Tianjin Municipal Natural Science Foundation,China (Grant No. 12JCZDJC21100)
文摘Manual acupuncture is widely used for pain relief and stress control.Previous studies on acupuncture have shown its modulatory effects on the functional connectivity associated with one or a few preselected brain regions.To investigate how manual acupuncture modulates the organization of functional networks at a whole-brain level,we acupuncture at ST36 of a right leg to obtain electroencephalograph(EEG) signals.By coherence estimation,we determine the synchronizations between all pairwise combinations of EEG channels in three acupuncture states.The resulting synchronization matrices are converted into functional networks by applying a threshold,and the clustering coefficients and path lengths are computed as a function of threshold.The results show that acupuncture can increase functional connections and synchronizations between different brain areas.For a wide range of thresholds,the clustering coefficient during acupuncture and postacupuncture period is higher than that during the pre-acupuncture control period,whereas the characteristic path length is shorter.We provide further support for the presence of "small-world" network characteristics in functional networks by using acupuncture.These preliminary results highlight the beneficial modulations of functional connectivity by manual acupuncture,which could contribute to the understanding of the effects of acupuncture on the entire brain,as well as the neurophysiological mechanisms underlying acupuncture.Moreover,the proposed method may be a useful approach to the further investigation of the complexity of patterns of interrelations between EEG channels.
基金Project supported by the Key Program of the National Natural Science Foundation of China (Grant No. 50537030)the National Natural Science Foundation of China (Grant Nos. 61072012 and 61172009)+1 种基金the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos. 61104032 and 60901035)the Tianjin Municipal Natural Science Foundation (Grant No. 12JCZDJC21100)
文摘To investigate whether and how manual acupuncture(MA) modulates brain activities,we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph(EEG) signals in healthy subjects.We adopt the autoregressive(AR) Burg method to estimate the power spectrum of EEG signals and analyze the relative powers in delta(0 Hz-4 Hz),theta(4 Hz-8 Hz),alpha(8 Hz-13 Hz),and beta(13 Hz-30 Hz) bands.Our results show that MA at ST36 can significantly increase the EEG slow wave relative power(delta band) and reduce the fast wave relative powers(alpha and beta bands),while there are no statistical differences in theta band relative power between different acupuncture states.In order to quantify the ratio of slow to fast wave EEG activity,we compute the power ratio index.It is found that the MA can significantly increase the power ratio index,especially in frontal and central lobes.All the results highlight the modulation of brain activities with MA and may provide potential help for the clinical use of acupuncture.The proposed quantitative method of acupuncture signals may be further used to make MA more standardized.
基金supported by the Key Program of the National Natural Science Foundation of China (Grant No.50537030)the National Natural Science Foundation of China (Grant No.61072012)the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos.50907044,61104032,and 60901035)
文摘As a convenient approach to the characterization of cerebral cortex electrical information,electroencephalograph (EEG) has potential clinical application in monitoring the acupuncture effects.In this paper,a method composed of the mutual information method and Lempel-Ziv complexity method (MILZC) is proposed to investigate the effects of acupuncture on the complexity of information exchanges between different brain regions based on EEGs.In the experiments,eight subjects are manually acupunctured at 'Zusanli' acupuncture point (ST-36) with different frequencies (i.e.,50,100,150,and 200 times/min) and the EEGs are recorded simultaneously.First,MILZC values are compared in general.Then average brain connections are used to quantify the effectiveness of acupuncture under the above four frequencies.Finally,significance index P values are used to study the spatiality of the acupuncture effect on the brain.Three main findings are obtained:(i) MILZC values increase during the acupuncture;(ii) manual acupunctures (MAs) with 100 times/min and 150 times/min are more effective than with 50 times/min and 200 times/min;(iii) contralateral hemisphere activation is more prominent than ipsilateral hemisphere's.All these findings suggest that acupuncture contributes to the increase of brain information exchange complexity and the MILZC method can successfully describe these changes.
基金the National Natural Science Foundation of China(NSFC)(NO.81701269).
文摘Machine learning(ML)is a fundamental concept in the field of state-of-the-art artificial intelligence(AI).Over the past two decades,it has evolved rapidly and been employed wildly in many fields.In medicine the widespread usage of ML has been observed in recent years.The present review examines various ML approaches for electroencephalograph(EEG)signal procession in epilepsy research,highlighting applications in the aspect of automated seizure detection,prediction and orientation.The present review also presents advantage,challenge and future direction of ML techniques in the analysis of EEG signals in epilepsy.
文摘Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.
