Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Purpose#R##N#This study is aimed at finding a simple method for detection of depression based on the analysis of single channel short-term EEG signals.#R##N#Materials and methods#R##N#The accuracy of linear, spectral asymmetry index (SASI), and nonlinear, detrended fluctuation analysis (DFA), methods for differentiating depressive and healthy subjects was compared. The eyes closed EEG was recorded from 18 common reference (Cz) channels for 34 subjects (17 depressive and 17 control). The signals re stored at 400 Hz sampling frequency and digitally filtered with cutoff frequencies at 0.5 Hz and at 40 Hz. The first 5 min of each recording was selected for further analysis.#R##N#Results#R##N#The experiments indicated maximum difference for SASI values in channel Pz and for DFA values in channels Pz and O2. Therefore, channel Pz was selected for comparison of two methods. The results of statistical analysis show that SASI values are significantly higher for depressive than for control group (p = 3.577e–05), while DFA values are significantly lor for depressive group (p = 0.033). SASI has superior discrimination ability with classification accuracy of 76.5%, while the classification accuracy of DFA was 70.6%. Linear combination of SASI and DFA resulted in 91.2% classification accuracy.#R##N#Conclusions#R##N#Our results demonstrate that the analysis of single channel signal can provide high accuracy of differentiation depression EEG.
['Maie Bachmann', 'Jaanus Lass', 'Hiie Hinrikus']
I am writing a research paper on a idea Single channel EEG analysis for detection of depression
[{"from": "human", "value": "I am writing a research paper on a idea Single channel EEG analysis for detection of depression"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Purpose#R##N#This study is aimed at finding a simple method for detection of depression based on the analysis of single channel short-term EEG signals.#R##N#Materials and methods#R##N#The accuracy of linear, spectral asymmetry index (SASI), and nonlinear, detrended fluctuation analysis (DFA), methods for differentiating depressive and healthy subjects was compared. The eyes closed EEG was recorded from 18 common reference (Cz) channels for 34 subjects (17 depressive and 17 control). The signals re stored at 400\u00a0Hz sampling frequency and digitally filtered with cutoff frequencies at 0.5\u00a0Hz and at 40\u00a0Hz. The first 5\u00a0min of each recording was selected for further analysis.#R##N#Results#R##N#The experiments indicated maximum difference for SASI values in channel Pz and for DFA values in channels Pz and O2. Therefore, channel Pz was selected for comparison of two methods. The results of statistical analysis show that SASI values are significantly higher for depressive than for control group (p\u00a0=\u00a03.577e\u201305), while DFA values are significantly lor for depressive group (p\u00a0=\u00a00.033). SASI has superior discrimination ability with classification accuracy of 76.5%, while the classification accuracy of DFA was 70.6%. Linear combination of SASI and DFA resulted in 91.2% classification accuracy.#R##N#Conclusions#R##N#Our results demonstrate that the analysis of single channel signal can provide high accuracy of differentiation depression EEG."}]