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书名: XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010
作者: Panagiotis D. Bamidis,Nicolas Pallikarakis
出版时间: 2010-01-01
ISBN: 9783642130380(P-ISBN) ,9783642130397(O-ISBN)
摘要:
摘要:
书目详情:
Quantitative Analysis of Two-Dimensional Catch-Up Saccades Executed to the Target Jumps in the Time-Continuous TrajectorySpike Sorting based on Dominant-Sets clusteringDifferentiation of human bone marrow stromal cells onto gelatin cryogel scaffoldsSimples Coherence vs. Multiple Coherence: A Somatosensory Evoked Response Detection InvestigationMeasure of Similarity of ECG CyclesWavelet phase synchronization between EHGs at different uterine sites: comparison of pregnancy and labor contractionsDynamic Generation of Physiological Model SystemsRandom Forest-Based Classification of Heart Rate Variability Signals by Using Combinations of Linear and Nonlinear FeaturesValidation of MRS metabolic markers in the classification of brain gliomas and their correlation to energy metabolismEvent-Related Synchronization/Desynchronization for Evaluating Cortical Response Detection Induced by Dynamic Visual StimuliInvestigating the EEG Alpha Band during Kinesthetic and Visual Motor Imagery of the Spike Volleyball MovementPrincipal Components Clustering through a Variance-Defined MetricA Kurtosis-Based Automatic System Using Naïve Bayesian Classifier to Identify ICA Components Contaminated by EOG or ECG ArtifactsCorrelation between Fractal Behavior of HRV and Neurohormonal and Functional Indexes in Chronic Heart FailureOn the Selection of Time Interval and Frequency Range of EEG Signal Preprocessing for P300 Brain-Computer InterfacingDevelopment of a Simple and Cheap Device for Movement AnalysisSignal Peptide Prediction in Single Transmembrane Proteins Using the Continuous Wavelet TransformComparison of AM-FM Features with Standard Features for the Classification of Surface Electromyographic SignalsStudying Brain Visuo-Tactile Integration through Cross-Spectral Analysis of Human MEG RecordingsPatient-specific seizure prediction using a multi-feature and multi-modal EEG-ECG classification
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