Background: Parkinson's disease (PD) gradually degrades the functionality of the brain. Because of its relevance to the abnormality of the brain, electroencephalogram (EEG) signal is used for the early detection of this disease. This paper introduces a novel computer-aided diagnosis method to detect PD, which is an efficient deep learning method based on a pooling-based deep recurrent neural network (PDRNN). Therefore, the purpose of this study is to detect Parkinson's disease based on deep recurrent neural network of EEG signal Methods: The EEG signals of 20 patients with Parkinson's disease and 20 healthy people in Henan ProvincialPeople's Hospital (People's Hospital of Zhengzhou University) were examined, and a PDRNN learning method was applied on the dataset for managing the demand of the traditional feature presentation step.
Results:The suggested DPRNN network gives the precision, sensitivity and specificity of 88.31%, 84.84% and 91.81%, respectively. Nevertheless, 11.28% of the healthy cases are wrongly categorized in Parkinson class. Also, 11.49% percent of Parkinson cases are classified wrongly in the healthy class.
Conclusions:The experimental model has high efficiency and can be used as a reliable tool for clinical PD detection. In future research, more cases should be used to test and develop the proposed model.
In this contribution, the azo dye (E)-1-(4-((4-(phenylamino)phenyl)diazenyl) phenyl)ethanone (DPA) was combined with reduced graphene oxide (RGO) for the electrochemical modification of a pencil graphite electrode (RGO/DPA/PGE) surface. A series of electrochemical measurements were used for the characterization of the modified electrode surfaces. At the modified electrode, nicotine was irreversibly reduced. An obvious increase was observed in the reductive peak current of nicotine at the modified electrode, indicating the capability of the RGO/DPA composite to increase the electron transfer rate. The current was found proportional to the nicotine concentration in a range of 31 to 1900 μM, and the limit of detection (LOD) was calculated as 7.6 μM.
The preparation of various aza‐heteroaromatic dithiocarbamates from aza‐heteroaromatic bromides and tetraalkylthiuram disulfides is reported. The transformation provides a convenient procedure, with good yields and functional group tolerance to various important nitrogen‐containing heteroaromatic dithiocarbamates such as benzothiazole, quinoline, pyridine, and pyrazine motifs. This protocol allows the facile synthesis of some potential biologically active compounds.
An efficient protocol for the straightforward synthesis of amides from readily available carboxylic acids and tetraalkylthiuram disulfides is presented. The reaction proceeds through direct cross‐coupling reactions promoted by catalytic amount of PPh3 in DMSO. This protocol is compatible with a wide variety of electron‐donating and ‐withdrawing acids, which shows its practical synthetic value in organic synthesis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.