Computer-aid Diagnosis on Major Depressive Disorder Based onEMG from the Splenius Capitis Muscle(Part 2)2010 Annual Symposium on Biomedical Engineering and TechnologyTsu-Wang Shen1 Hsin-Fang Li1 William Shao-Tsu Chen21 Department of Medical Informatics, Tzu Chi University2 Department of Psychiatry, Buddhist Tzu-Chi General HospitalPresenter: Tzu-Yu HuangAdvisor: Dr. Yen-Ting ChenDate: 12.29.2010

Methods and MaterialsAn artificial neural network (ANN)Artificial intelligenceMathematical modelLearning systemComputer-aid diagnosis

An artificial neural network (ANN):

Methods and MaterialsBack propagation neural network (BPNN)Supervised neural networkBack propagation neural network (BPNN):

Methods and MaterialsBack propagation neural network (BPNN)Input-to-hiddenWeight

Activation function : sigmoid

Methods and MaterialsBack propagation neural network (BPNN)Hidden-to-outputWeight

Activation function : pure-linear

Methods and MaterialsBack propagation neural network (BPNN)Adjust weightsOutput layer

Methods and MaterialsBack propagation neural network (BPNN)Adjust weights

Methods and MaterialsSupport vector machine (SVM)ClassificationStatisticsHyperplane

OSH:Support hyperplane:

ResultsT.O.V.AMDD Higher omission ratesHigher mean response times More variabilityEMG featuresMDDLower EA and RMS valuesHigher MF and MPF values

ResultsT.O.V.ACompare EMG Comparison by groups during rest *p

ResultsT.O.V.ACompare EMG Comparison by group during TOVA*p

ResultsAccuracy

Conclusion and DiscussionMDD becomes more distinguishable when restingHealth controls have wider range of EA and MFMDD patients have the lower capability on physiological regulationHopefully, the system can be used to detect and to control the MDD disorder in the future.

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Cell body Dendntes ()Synapse ()Axon ()

Support Vector Machines(SVM)(Classification) Vapnik