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Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE

Date 3-6 Sept. 2009

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Displaying Results 1 - 25 of 1808
  • Welcome message from IEEE EMBS president

    Page(s): 1 - 2
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  • Committees

    Page(s): 1 - 15
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  • EMBS

    Page(s): 1 - 5
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  • Theme keynote lecture

    Page(s): 1 - 9
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    Provides an abstract of the keynote presentation and a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings. View full abstract»

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  • Program Themes

    Page(s): 1 - 4
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  • Special Symposia

    Page(s): 1 - 11
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  • Exhibitor descriptions

    Page(s): 1 - 6
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  • Partnership acknowledgements

    Page(s): 1 - 2
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  • [Advertisement]

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  • Program in chronological order

    Page(s): 1 - 184
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  • Author index

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  • Keyword index

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  • Conference Keynote Lecture

    Page(s): 1 - 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (630 KB)  

    Provides an abstract of the keynote presentation and a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings. View full abstract»

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  • [Copyright notice]

    Page(s): 1
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  • Time-varying spectrum estimation of heart rate variability signals with Kalman smoother algorithm

    Page(s): 1 - 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (751 KB) |  | HTML iconHTML  

    A time-varying parametric spectrum estimation method for analyzing dynamics of heart rate variability (HRV) signals is presented. In the method, HRV signal is first modeled with a time-varying autoregressive model and the model parameters are solved recursively with a Kalman smoother algorithm. Time-varying spectrum estimates are then obtained from the estimated model parameters. The obtained spectrum can be further decomposed into separate components, which is especially advantageous in HRV applications where low frequency (LF) and high frequency (HF) components are generally aimed to be distinguished. As case studies, the dynamics of HRV signals recorded during 1) orthostatic test, 2) exercise test and 3) simulated driving task are analyzed. View full abstract»

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  • Analysis and processing of heart rate variability by time-frequency representation: Quantification of the pedaling frequency modulation

    Page(s): 5 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (327 KB) |  | HTML iconHTML  

    It has been shown that a pedaling frequency component can be extracted from the heart rate variability (HRV) signal using a time-varying filter. It is shown that this filter can be implemented directly in the time-frequency plane with different approaches. The need of resampling the data is also discussed with regard to the artifacts produced when the Shanon condition is not fulfilled. In order to interpret the similar amplitude profiles of the pedaling component for untrained and trained subjects, an attempt for the model parameters setting is proposed. Consistent results on a large data set illustrate the feasibility of such processing. View full abstract»

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  • Sleep staging classification based on HRV: Time-variant analysis

    Page(s): 9 - 12
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    An algorithm to evaluate the sleep macrostructure based on heart rate fluctuations from ECG signal is presented. This algorithm is an attempt to evaluate the sleep quality out of sleep centers. The algorithm is made up by a) a time-variant autoregressive model used as feature extractor and b) a hidden Markov model used as classifier. Characteristics coming from the joint probability of HRV features were used to fed the HMM. 17 full polysomnography recordings from healthy subjects were used in the current analysis. When compared to Wake-NREM-REM given by experts, the automatic classifier achieved a total accuracy of 78.21plusmn6.44% and a kappa index of 0.41plusmn.1085 using two features and a total accuracy of 79.43plusmn8.83% and kappa index of 0.42plusmn.1493 using three features. View full abstract»

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  • New approach in features extraction for EEG signal detection

    Page(s): 13 - 16
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (404 KB) |  | HTML iconHTML  

    This paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG). Particularly, the method extracts features using the smoothed pseudo Wigner-Ville distribution combined with the McAulay-Quatieri sinusoidal model and identifies abnormal neural discharges. We propose a new feature based on the length of the track that, combined with energy and frequency features, allows to isolate a continuous energy trace from another oscillations when an epileptic seizure is beginning. We evaluate our approach using data consisting of 16 different seizures from 6 epileptic patients. The results show that our extraction method is a suitable approach for automatic seizure detection, and opens the possibility of formulating new criteria to detect and analyze abnormal EEGs. View full abstract»

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  • A semi-automated method for epileptiform transient detection in the EEG of the fetal sheep using time-frequency analysis

    Page(s): 17 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (538 KB) |  | HTML iconHTML  

    Perinatal hypoxia remains a significant cause of brain damage. Currently there are no biomarkers to detect the at risk brain. Recent research, however, suggests that the appearance of epileptiform transients in the first 6-8 hours after hypoxia (the latent phase of injury) are predictive of neural outcome. To quantify this further a key need is to automate EEG signal analysis to aid clinical staff with the vast amounts of complex data to review. In this study, we present a semi-automated method for spike detection in the fetal sheep EEG. The method utilizes the short time Fourier transform and peak separation to extract spikes. The performance of the method was found to be high in sensitivity and selectivity over 3 distinct time points. View full abstract»

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  • Denoising of multiscale/multiresolution structural feature dictionaries for rapid training of a brain computer interface

    Page(s): 21 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (572 KB) |  | HTML iconHTML  

    Multichannel neural activities such as EEG or ECoG in a brain computer interface can be classified with subset selection algorithms running on large feature dictionaries describing subject specific features in spectral, temporal and spatial domain. While providing high accuracies in classification, the subset selection techniques are associated with long training times due to the large feature set constructed from multichannel neural recordings. In this paper we study a novel denoising technique for reducing the dimensionality of the feature space which decreases the computational complexity of the subset selection step radically without causing any degradation in the final classification accuracy. The denoising procedure was based on the comparison of the energy in a particular time segment and in a given scale/level to the energy of the raw data. By setting denoising threshold a priori the algorithm removes those nodes which fail to capture the energy in the raw data in a given scale. We provide experimental studies towards the classification of motor imagery related multichannel ECoG recordings for a brain computer interface. The denoising procedure was able to reach the same classification accuracy without denoising and a computational complexity around 5 times smaller. We also note that in some cases the denoised procedure performed better classification. View full abstract»

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  • Seizure prediction for epilepsy using a multi-stage phase synchrony based system

    Page(s): 25 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (730 KB) |  | HTML iconHTML  

    Seizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques. Here we present a multi-stage phase synchrony based system that brings to bear the advantages of many techniques in each substage. The 1st stage of the system unmixes continuous long-term (2-4 days) multichannel scalp EEG using spatially constrained Independent Component Analysis and estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. It then projects multidimensional features onto a 2-D map using Neuroscale and evaluates the probability of predictive events using Gaussian Mixture Models. We show the possibility of seizure onset prediction within a prediction window of 35-65 minutes with a sensitivity of 65-100% and specificity of 65-80% across epileptic patients. View full abstract»

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  • Signal processing challenges for single-trial analysis of simultaneous EEG/fMRI

    Page(s): 29 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (227 KB) |  | HTML iconHTML  

    A relatively new neuroimaging modality is simultaneous EEG and fMRI. Though such a multi-modal acquisition is attractive given that it can exploit the temporal resolution of EEG and spatial resolution of fMRI, it comes with unique signal processing and pattern classification challenges. In this paper I will review some our work at developing signal processing and pattern recognition for analysis of simultaneous EEG and fMRI, with a focus on those algorithms enabling a single-trial analysis of the neural signal. In general, these algorithms exploit the multivariate nature of the EEG, removing MR induced artifacts and classifying event-related signals that then can be correlated with the BOLD signal to yield specific fMRI activations. View full abstract»

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  • Nonlinear chaotic component extraction for postural stability analysis

    Page(s): 31 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (319 KB) |  | HTML iconHTML  

    This paper proposes a nonlinear analysis of the human postural steadiness system. The analyzed signal is the displacement of the centre of pressure (COP) collected from a force plate used for measuring postural sway. Instead of analyzing the classical nonlinear parameters on the whole signal, the proposed method consists of analyzing the nonlinear dynamics of the intrinsic mode functions (IMF) of the COP signal. Based on the computation of the IMFs Lyapunov exponents, it is shown that pre-processing the COP signal with the empirical mode decomposition allows an efficient extraction of its chaotic component. View full abstract»

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  • Revealing the metabolic profile of brain tumors for diagnosis purposes

    Page(s): 35 - 38
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (665 KB) |  | HTML iconHTML  

    The metabolic behavior of complex brain tumors, like gliomas and meningiomas, with respect to their type and grade was investigated in this paper. Towards this direction the smallest set of the most representative metabolic markers for each brain tumor type was identified, using ratios of peak areas of well established metabolites, from 1H-MRSI (proton magnetic resonance spectroscopy imaging) data of 24 patients and 4 healthy volunteers. A feature selection method that embeds Fisher's filter criterion into a wrapper selection scheme was applied; support vector machine (SVM) and least squares-SVM (LS-SVM) classifiers were used to evaluate the ratio markers classification significance. The area under the receiver operating characteristic curve (AUROC) was adopted to evaluate the classification significance. It is found that the NAA/CHO, CHO/S, MI/S ratios can be used to discriminate gliomas and meningiomas from Healthy tissue with AUROC greater than 0.98. Ratios CHO/S, CRE/S, MI/S, LAC/CRE, ALA/CRE, ALA/S and LIPS/CRE can identify type and grade differences in gliomas giving AUROC greater than 0.98 apart from the scheme of gliomas grade II vs grade III where 0.84 was recorded due to high heterogeneity. Finally NAA/CRE, NAA/S, CHO/S, MI/S and ALA/S manage to discriminate gliomas from meningiomas providing AUROC exceeding 0.90. View full abstract»

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  • Adaptive time-frequency matrix features for T wave alternans analysis

    Page(s): 39 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (391 KB) |  | HTML iconHTML  

    T wave alternans (TWA) has been associated with ventricular arrhythmias. Hence, TWA detection can risk stratify patients with heart disease who may experience sudden death from ventricular arhythmias. However, accurate TWA detection is technically challenging due to the low microvolt TWA signal and the confounding effect of biological noise such as movement, myopotentials or respiration. In this paper, we propose nonnegative matrix factorization (NMF)-Adaptive spectral method to increase the robustness of TWA detection in ambulatory electrocardiograms (ECGs). The proposed method applies a non-linear time-frequency (TF) analysis and NMF to the aligned ST-T waveforms. This method separates the TWA signal from the other non-desired ECG signal components, and detects TWA with high accuracy. The performance of our proposed method is validated in a clinical study using ECGs which confirms a TWA detection of 92% compared to 47% using the conventional spectral method. View full abstract»

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