Sleep spindle is the characteristic waveform of electroencephalogram (EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were reco...Sleep spindle is the characteristic waveform of electroencephalogram (EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram (PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.展开更多
This paper presents an automatic techruque of suitable reference potential selection for quantitative EEG interpretation.The 16-channels EEG recording under mono-polar derivation is analyzed.There are two prejudgments...This paper presents an automatic techruque of suitable reference potential selection for quantitative EEG interpretation.The 16-channels EEG recording under mono-polar derivation is analyzed.There are two prejudgments defined for checking the amplitude distribution and ear lobe activation.After prejudgments,the EEG is classified into several cases in cluding diffused case,non-diffused case,and artifact contami nation case.Due to the cases,an automatic reference selection method is applied in order to find out suitable reference potential.Finally,the referential derivation constructed according to the obtained reference potential,is evaluated for further EEG rhythm analysis.The presented technique can high light the EEG rhythm of interest,which is useful for quantitative EEG interpretation by both visual inspection and automatic evaluation.展开更多
文摘Sleep spindle is the characteristic waveform of electroencephalogram (EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram (PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.
基金Grant sponsor:National Natural Science Foundation of China,grant number:61074113grant sponsor:Shanghai Leading Academic Discipline Project,grant number:B504grant sponsor:Fundamental Research Funds for the Central Universities,grant number:WH0914028
文摘This paper presents an automatic techruque of suitable reference potential selection for quantitative EEG interpretation.The 16-channels EEG recording under mono-polar derivation is analyzed.There are two prejudgments defined for checking the amplitude distribution and ear lobe activation.After prejudgments,the EEG is classified into several cases in cluding diffused case,non-diffused case,and artifact contami nation case.Due to the cases,an automatic reference selection method is applied in order to find out suitable reference potential.Finally,the referential derivation constructed according to the obtained reference potential,is evaluated for further EEG rhythm analysis.The presented technique can high light the EEG rhythm of interest,which is useful for quantitative EEG interpretation by both visual inspection and automatic evaluation.
