IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Sep 2004
Comparative StudyExtracellular recordings from patterned neuronal networks using planar microelectrode arrays.
Neuronal cell networks have been reconstructed on planar microelectrode arrays (MEAs) from dissociated hippocampal pyramidal neurons. Microcontact printing (microCP) and a photoresist-liftoff method were used to selectively localize poly-L-lysine (PLL) on the surface of MEAs. ⋯ Bursting activity with spike amplitude attenuation was observed, and multichannel recordings detected instances of coincident firing activity. Finally, we present here an extracellular recording from a approximately 2 microm bundle of guided neurites.
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IEEE Trans Biomed Eng · Sep 2004
Comparative StudyChronic measurement of the stimulation selectivity of the flat interface nerve electrode.
The flat interface nerve electrode (FINE) is an attempt to improve the stimulation selectivity of extraneural electrodes. By reshaping peripheral nerves into elliptical cylinders, central fibers are moved closer to the nerve-electrode interface, and additional surface area is created for contact placement. The goals of this study were to test the hypothesis that greater nerve reshaping leads to improved selectivity and to examine the chronic recruitment properties of the FINE. ⋯ Both the selectivity measurements and the recruitment curve characteristics were stable throughout the implant period. From an electrophysiological standpoint, the FINE is a viable alternative for neuroprosthetic devices. A histological analysis of the nerves is under way to evaluate the safety of the FINE.
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IEEE Trans Biomed Eng · Sep 2004
Comparative StudySimulation of surface EMG signals generated by muscle tissues with inhomogeneity due to fiber pinnation.
Surface electromyographic (EMG) signal modeling has important applications in the interpretation of experimental EMG data. Most models of surface EMG generation considered volume conductors homogeneous in the direction of propagation of the action potentials. However, this may not be the case in practice due to local tissue inhomogeneities or to the fact that there may be groups of muscle fibers with different orientations. ⋯ In these conditions, the potentials detected at the skin surface do not travel without shape changes. This determines numerical issues in the implementation of the model which are addressed in this work. The study provides the solution of the nonhomogenous, anisotropic problem, proposes an implementation of the results in complete surface EMG generation models (including finite-length fibers), and shows representative results of the application of the models proposed.
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IEEE Trans Biomed Eng · Aug 2004
Comparative Study Clinical TrialAssessment of average muscle fiber conduction velocity from surface EMG signals during fatiguing dynamic contractions.
In this paper, we propose techniques of surface electromyographic (EMG) signal detection and processing for the assessment of muscle fiber conduction velocity (CV) during dynamic contractions involving fast movements. The main objectives of the study are: 1) to present multielectrode EMG detection systems specifically designed for dynamic conditions (in particular, for CV estimation); 2) to propose a novel multichannel CV estimation method for application to short EMG signal bursts; and 3) to validate on experimental signals different choices of the processing parameters. Linear adhesive arrays of electrodes are presented for multichannel surface EMG detection during movement. ⋯ The method proposed is applied to signals detected from the vastus laterialis and vastus medialis muscles during cycling at 60 cycles/min. Ten subjects were investigated during a 4-min cycling task. The method provided reliable assessment of muscle fatigue for these subjects during dynamic contractions.
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IEEE Trans Biomed Eng · Jul 2004
Comparative StudyAutomatic classification of heartbeats using ECG morphology and heartbeat interval features.
A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. ⋯ This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.