IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Sep 2007
Estimation of muscle fiber conduction velocity with a spectral multidip approach.
We propose a novel method for estimation of muscle fiber conduction velocity from surface electromyographic (EMG) signals. The method is based on the regression analysis between spatial and temporal frequencies of multiple dips introduced in the EMG power spectrum through the application of a set of spatial filters. This approach leads to a closed analytical expression of conduction velocity as a function of the auto- and cross-spectra of monopolar signals detected along the direction of muscle fibers. ⋯ When 200 active motor units were simulated in an interference EMG signal, standard deviation of conduction velocity decreased from 0.95 m/s (10 dB SNR) and 0.60 m/s (20 dB SNR) with a single dip to 0.21 m/s (10 dB) and 0.11 m/s (20 dB) with 65 dips. In experimental signals detected from the abductor pollicis brevis muscle, standard deviation of estimation decreased from (mean +/- SD over 5 subjects) 1.25 +/- 0.62 m/s with one dip to 0.10 +/- 0.03 m/s with 100 dips. The proposed method does not imply limitation in resolution of the estimated conduction velocity and does not require any iterative procedure for the estimate since it is based on a closed analytical formulation.
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IEEE Trans Biomed Eng · Sep 2007
fMRI data analysis with nonstationary noise models: a Bayesian approach.
The assumption of noise stationarity in the functional magnetic resonance imaging (fMRI) data analysis may lead to the loss of crucial dynamic features of the data and thus result in inaccurate activation detection. In this paper, a Bayesian approach is proposed to analyze the fMRI data with two nonstationary noise models (the time-varying variance noise model and the fractional noise model). The covariance matrices of the time-varying variance noise and the fractional noise after wavelet transform are diagonal matrices. ⋯ The Bayesian estimator not only gives an accurate estimate of the weights in general linear model, but also provides posterior probability of activation in a voxel and, hence, avoids the limitations (i.e., using only hypothesis testing) in the classical methods. The performance of the proposed Bayesian methods (under the assumption of different noise models) are compared with the ordinary least squares (OLS) and the weighted least squares (WLS) methods. Results from the simulation studies validate the superiority of the proposed approach to the OLS and WLS methods considering the complex noise structures in the fMRI data.
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IEEE Trans Biomed Eng · Sep 2007
Redefining performance evaluation tools for real-time QRS complex classification systems.
In a heartbeat classification procedure, the detection of QRS complex waveforms is necessary. In many studies, this heartbeat extraction function is not considered: the inputs of the classifier are assumed to be correctly identified. ⋯ This study shows that a classification accuracy of 96.72% falls to 94.90% when a drop of 1.78% error rate is introduced in the detector quality. This corresponds to an increase of about 50% bad classifications.