Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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Annu Int Conf IEEE Eng Med Biol Soc · Jan 2012
Reducing classification accuracy degradation of pattern recognition based myoelectric control caused by electrode shift using a high density electrode array.
The robustness and usability of pattern recognition based myoelectric control systems degrade significantly if the sensors are displaced during usage. This effect inevitably occurs during donning, doffing or using an upper-limb prosthesis over a longer period of time. ⋯ In our experiment we use a 96 channel high density electrode array to distinguish 11 different hand and wrist movements. Our results show that for electrode shifts up to 1 cm an array of about 32 sensors in combination with state-of-the-art pattern recognition algorithms is sufficient to compensate the electrode displacement effect.
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Annu Int Conf IEEE Eng Med Biol Soc · Jan 2011
Review Comparative StudyDetection of Atrial fibrillation from non-episodic ECG data: a review of methods.
Atrial fibrillation (A-fib) is the most common cardiac arrhythmia. To effectively treat or prevent A-fib, automatic A-fib detection based on Electrocardiograph (ECG) monitoring is highly desirable. ⋯ In general the performances of these methods were evaluated in terms of sensitivity, specificity and overall detection accuracy on the datasets from the Physionet repository. Based on our survey, we conclude that no promising A-fib detection method that performs consistently well across various scenarios has been proposed yet.
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Annu Int Conf IEEE Eng Med Biol Soc · Jan 2011
Automatic detection of tree-in-bud patterns for computer assisted diagnosis of respiratory tract infections.
Abnormal nodular branching opacities at the lung periphery in Chest Computed Tomography (CT) are termed by radiology literature as tree-in-bud (TIB) opacities. These subtle opacity differences represent pulmonary disease in the small airways such as infectious or inflammatory bronchiolitis. ⋯ The proposed method combines shape index, local gradient statistics, and steerable wavelet features to automatically identify TIB patterns. Experimental results using 39 viral bronchiolitis human para-influenza (HPIV) CTs and 21 normal lung CTs achieved an overall accuracy of 89.95%.
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Annu Int Conf IEEE Eng Med Biol Soc · Jan 2011
Algorithms for characterizing brain metabolites in two-dimensional in vivo MR correlation spectroscopy.
Traditional analyses of in vivo 1D MR spectroscopy of brain metabolites have been limited to the inspection of one-dimensional free induction decay (FID) signals from which only a limited number of metabolites are clearly observable. In this article we introduce a novel set of algorithms to process and characterize two-dimensional in vivo MR correlation spectroscopy (2D COSY) signals. 2D COSY data was collected from phantom solutions of topical metabolites found in the brain, namely glutamine, glutamate, and creatine. ⋯ Additionally, quantitative features are derived from peak and object structures, and we show that these measures are correlated with known phantom metabolite concentrations. These results are encouraging for future studies focusing on neurological disorders that induce subtle changes in brain metabolite concentrations and for which accurate quantitation is important.
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Annu Int Conf IEEE Eng Med Biol Soc · Jan 2011
Automatic REM sleep detection associated with idiopathic rem sleep Behavior Disorder.
Rapid eye movement sleep Behavior Disorder (RBD) is a strong early marker of later development of Parkinsonism. Currently there are no objective methods to identify and discriminate abnormal from normal motor activity during REM sleep. Therefore, a REM sleep detection without the use of chin electromyography (EMG) is useful. This is addressed by analyzing the classification performance when implementing two automatic REM sleep detectors. The first detector uses the electroencephalography (EEG), electrooculography (EOG) and EMG to detect REM sleep, while the second detector only uses the EEG and EOG. ⋯ The overall result indicates that the EMG does not play an important role when classifying REM sleep.