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1.
IEEE Trans Biomed Eng ; 52(4): 736-9, 2005 Apr.
Article in English | MEDLINE | ID: mdl-15825875

ABSTRACT

Esophageal electrical stimulation using short and a relatively small number of (200 micros, 0.2 Hz, n = 25) electrical pulses generates a characteristic and well defined cortical evoked potential response (EP). There are two methods of stimulation: either through intraesophageal electrodes or with transmural electrodes. The objective of this paper is to compare EP response, sensations and heart rate variability power spectra elicited by both stimulation modalities in healthy volunteers. Our results suggest that transmural stimulation is more accurately perceived and at lower intensities, produces more reproducible peaks of higher amplitude than during intraesophageal stimulation. During either mode of esophageal stimulation, power within the high-frequency component of the heart rate variability power spectrum is enhanced.


Subject(s)
Electric Stimulation/methods , Electroencephalography/methods , Esophagus/innervation , Esophagus/physiology , Evoked Potentials, Motor/physiology , Motor Cortex/physiology , Adult , Female , Heart Rate/physiology , Humans , Male , Vagus Nerve/physiology
2.
IEEE Trans Biomed Eng ; 50(4): 521-6, 2003 Apr.
Article in English | MEDLINE | ID: mdl-12723065

ABSTRACT

Identification of the short transient waveform, called a spike, in the cortical electroencephalogram (EEG) plays an important role during diagnosis of neurological disorders such as epilepsy. It has been suggested that artificial neural networks (ANN) can be employed for spike detection in the EEG, if suitable features are provided as input to an ANN. In this paper, we explore the performance of neural network-based classifiers using features selected by algorithms suggested by four previous investigators. Of these, three algorithms model the spike by mathematical parameters and use them as features for classification while the fourth algorithm uses raw EEG to train the classifier. The objective of this paper is to examine if there is any inherent advantage to any particular set of features, subject to the condition that the same data are used for all feature selection algorithms. Our results suggest that artificial neural networks trained with features selected using any one of the above three algorithms as well as raw EEG directly fed to the ANN will yield similar results.


Subject(s)
Algorithms , Electroencephalography/classification , Electroencephalography/methods , Epilepsy/diagnosis , Neural Networks, Computer , Pattern Recognition, Automated , Epilepsy/physiopathology , Humans , Reference Values
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