IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Jan 2007
Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI.
A framework that combines atlas registration, fuzzy connectedness (FC) segmentation, and parametric bias field correction (PABIC) is proposed for the automatic segmentation of brain magnetic resonance imaging (MRI). First, the atlas is registered onto the MRI to initialize the following FC segmentation. Original techniques are proposed to estimate necessary initial parameters of FC segmentation. ⋯ Thus, we avoid expert human intervention and provide a fully automatic method for brain MRI segmentation. Experiments on both simulated and real MRI images demonstrate the validity of the method, as well as the limitation of the method. Being a fully automatic method, it is expected to find wide applications, such as three-dimensional visualization, radiation therapy planning, and medical database construction.
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IEEE Trans Biomed Eng · Dec 2006
An algorithm for robust and efficient location of T-wave ends in electrocardiograms.
The purpose of this paper is to propose a new algorithm for T-wave end location in electrocardiograms, mainly through the computation of an indicator related to the area covered by the T-wave curve. Based on simple assumptions, essentially on the concavity of the T-wave form, it is formally proved that the maximum of the computed indicator inside each cardiac cycle coincides with the T-wave end. ⋯ It is also computationally very simple: the main computation can be implemented as a simple finite impulse response filter. When evaluated with the PhysioNet QT database in terms of the mean and the standard deviation of the T-wave end location errors, the proposed algorithm outperforms the other algorithms evaluated with the same database, according to the most recent available publications up to our knowledge.
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IEEE Trans Biomed Eng · Dec 2006
Online control of a brain-computer interface using phase synchronization.
Currently, almost all brain-computer interfaces (BCIs) ignore the relationship between phases of electroencephalographic signals detected from different recording sites (i.e., electrodes). The vast majority of BCI systems rely on feature vectors derived from e.g., bandpower or univariate adaptive autoregressive (AAR) parameters. ⋯ In our offline study, several PLV-based features were acquired and the optimal feature set was selected for each subject individually by a feature selection algorithm. The online sessions with three trained subjects revealed that all subjects were able to control three mental states (motor imagery of left hand, right hand, and foot, respectively) with single-trial accuracies between 60% and 66.7% (33% would be expected by chance) throughout the whole session.
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IEEE Trans Biomed Eng · Dec 2006
A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features.
An adaptive system for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats into one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard is presented. The heartbeat classification system processes an incoming recording with a global-classifier to produce the first set of beat annotations. An expert then validates and if necessary corrects a fraction of the beats of the recording. ⋯ The results of this study show that the performance of a patient adaptable classifier increases with the amount of training of the system on the local record. Crucially, the performance of the system can be significantly boosted with a small amount of adaptation even when all beats used for adaptation are from a single class. This study illustrates the ability to provide highly beneficial automatic arrhythmia monitoring and is an improvement on previously reported results for automated heartbeat classification systems.
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IEEE Trans Biomed Eng · Dec 2006
Analysis of biomedical signals by the lempel-Ziv complexity: the effect of finite data size.
The Lempel-Ziv (LZ) complexity and its variants are popular metrics for characterizing biological signals. Proper interpretation of such analyses, however, has not been thoroughly addressed. ⋯ We derive analytic expressions for the LZ complexity for regular and random sequences, and employ them to develop a normalization scheme. To gain further understanding, we compare the LZ complexity with the correlation entropy from chaos theory in the context of epileptic seizure detection from EEG data, and discuss advantages of the normalized LZ complexity over the correlation entropy.