The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals.In this paper,we develop a model-based classification method to dete...The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals.In this paper,we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram(EEG) signals.The underlying idea was to design an EEG filter that enhances the waveform of epileptic signals.The filtered signal was fitted to a quadratic linear-parabolic model using the curve fitting technique.The model fitting was assessed using four statistical parameters,which were used as classification features with a random forest algorithm to discriminate seizure and non-seizure events.The proposed method was applied to 66 epochs from the Children Hospital Boston database.Results showed that the method achieved fast and accurate detection of epileptic seizures,with a92% sensitivity,96% specificity,and 94.1% accuracy.展开更多
The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram.120 adolescents (60 boys,60 girls) aged from 9.1 to 17.2 years old were randomly selected.Twenty-...The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram.120 adolescents (60 boys,60 girls) aged from 9.1 to 17.2 years old were randomly selected.Twenty-seven landmarks from the second to fifth cervical vertebrae on the lat-eral cephalogram were identified.In this study,the system of CACVL was developed and used to iden-tify and calculate the landmarks by fast marching method and parabolic curve fitting.The accuracy and repeatability in CACVL group were compared with those in two manual landmarking groups [orthodon-tic experts (OE) group and orthodontic novices (ON) group].The results showed that,as for the accu-racy,there was no significant difference between CACVL group and OE group no matter in x-axis or y-axis (P>0.05),but there was significant difference between CACVL group and ON group,as well as OE group and ON group in both axes (P<0.05).As for the repeatability,CACVL group was more reli-able than OE group and ON group in both axes.It is concluded that CACVL has the same or higher ac-curacy,better repeatability and less workload than manual landmarking methods.It’s reliable for cervi-cal parameters identification on the lateral cephalogram and cervical vertebral maturation prediction in orthodontic practice and research.展开更多
文摘The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals.In this paper,we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram(EEG) signals.The underlying idea was to design an EEG filter that enhances the waveform of epileptic signals.The filtered signal was fitted to a quadratic linear-parabolic model using the curve fitting technique.The model fitting was assessed using four statistical parameters,which were used as classification features with a random forest algorithm to discriminate seizure and non-seizure events.The proposed method was applied to 66 epochs from the Children Hospital Boston database.Results showed that the method achieved fast and accurate detection of epileptic seizures,with a92% sensitivity,96% specificity,and 94.1% accuracy.
基金supported by grants from National Natural Sciences Foundation of China (No. 30801314)China Hubei Provincial Science and Technology Department (No.2008CBD088)
文摘The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram.120 adolescents (60 boys,60 girls) aged from 9.1 to 17.2 years old were randomly selected.Twenty-seven landmarks from the second to fifth cervical vertebrae on the lat-eral cephalogram were identified.In this study,the system of CACVL was developed and used to iden-tify and calculate the landmarks by fast marching method and parabolic curve fitting.The accuracy and repeatability in CACVL group were compared with those in two manual landmarking groups [orthodon-tic experts (OE) group and orthodontic novices (ON) group].The results showed that,as for the accu-racy,there was no significant difference between CACVL group and OE group no matter in x-axis or y-axis (P>0.05),but there was significant difference between CACVL group and ON group,as well as OE group and ON group in both axes (P<0.05).As for the repeatability,CACVL group was more reli-able than OE group and ON group in both axes.It is concluded that CACVL has the same or higher ac-curacy,better repeatability and less workload than manual landmarking methods.It’s reliable for cervi-cal parameters identification on the lateral cephalogram and cervical vertebral maturation prediction in orthodontic practice and research.
