Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Respiratory rate estimation from the oscillometric waveform obtained from a non-invasive cuff-based blood pressure device.
The presence of respiratory activity in the electrocardiogram (ECG), the pulse oximeter's photoplethysmo-graphic and continuous arterial blood pressure signals is a well-documented phenomenon. In this paper, we demonstrate that such information is also present in the oscillometric signal acquired from automatic non-invasive blood pressure monitors, and may be used to estimate the vital sign respiratory rate (RR). ⋯ Results demonstrated a good RR estimation accuracy of our method when compared to the reference values extracted from the reference respiration waveforms (mean absolute error of 2.69 breaths/min), which is comparable to existing methods in the literature that extract RR from other physiological signals. The proposed method has been implemented in Java on the Android device for use in an mHealth platform.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Computational modeling analysis of a spinal cord stimulation paddle lead reveals broad, gapless dermatomal coverage.
Spinal cord stimulation (SCS) is an effective therapy for treating chronic pain. The St. Jude Medical PENTA(TM) paddle lead features a 4 × 5 contact array for achieving broad, selective coverage of dorsal column (DC) fibers. ⋯ We found that across contact configurations used clinically in the sweep algorithm, the activation region shifted smoothly between left and right DC, and could achieve gapless medio-lateral coverage in dermatomal fiber tract zones. Increasing stimulation amplitude between the DC threshold and discomfort threshold led to a greater area of activation and number of dermatomal zones covered on the left and/or right DC, including L1-2 zones corresponding to dermatomes of the lower back. This work demonstrates that the flexibility in contact selection offered by the PENTA lead may enable patient-specific tailoring of SCS.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
An augmented reality framework for optimization of computer assisted navigation in endovascular surgery.
Endovascular surgery is performed by placing a catheter through blood vessels. Due to the fragility of arteries and the difficulty in controlling a long elastic wire to reach the target region, training plays an extremely important role in helping a surgeon acquire the required complex skills. ⋯ We have developed an augmented reality system for ultrasound-guided endovascular surgical training, where real ultrasound images captured during the procedure are registered with a pre-scanned phantom model to give the operator a realistic experience. Our goal is to extend the planning and training environment to deliver a system for computer assisted remote endovascular surgery where the navigation of a catheter can be controlled through a robotic device based on the guidance provided by an endovascular surgeon.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Alveolar air volatile organic compound extractor for clinical breath sampling.
Alveolar air Volatile Organic Compound (VOC) extractor is a handheld breath-sampling device for clinical breath analysis. The device consists two main components: (1) An alveolar air separator, (2) A VOC extractor. ⋯ Feasibility of using the SPME filament to collect a quantifiable breath sample directly from exhaled breath is experimentally validated. Exhaled breath acetone is quantified using alveolar air VOC extractor and a GC/MS system.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Multicategory classification of 11 neuromuscular diseases based on microarray data using support vector machine.
We applied multicategory machine learning methods to classify 11 neuromuscular disease groups and one control group based on microarray data. To develop multicategory classification models with optimal parameters and features, we performed a systematic evaluation of three machine learning algorithms and four feature selection methods using three-fold cross validation and a grid search. This study included 114 subjects of 11 neuromuscular diseases and 31 subjects of a control group using microarray data with 22,283 probe sets from the National Center for Biotechnology Information (NCBI). ⋯ In addition, a gene symbol, SPP1 was selected as the top-ranked gene by the BW method. We confirmed relationships between the gene (SPP1) and Duchenne muscular dystrophy (DMD) from a previous study. With our models as clinically helpful tools, neuromuscular diseases could be classified quickly using a computer, thereby giving a time-saving, cost-effective, and accurate diagnosis.