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
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
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.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Seizure detection using wavelet decomposition of the prediction error signal from a single channel of intra-cranial EEG.
This paper presents a novel patient-specific algorithm for detection of seizures in epileptic patients from a single-channel intra-cranial electroencephaolograph (iEEG) recording. Instead of extracting features from the EEG signal, first the EEG signal is filtered by a prediction error filter (PEF) to compute a prediction error signal. A two-level wavelet decomposition of the prediction error signal leads to two detail signals and one approximate signal. ⋯ The AdaBoost classifier achieves a sensitivity of 98.75% and an average FPR of 0.075 per hour. These results are obtained with leave-one-out cross-validation. In addition, for 13 out of 18 patients, the AdaBoost classifier requires only one feature, while it requires 4 features for the remaining 5 patients.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Signal quality quantification and waveform reconstruction of arterial blood pressure recordings.
Arterial blood pressure (ABP) is an important vital sign of the cardiovascular system. As with other physiological signals, its measurement can be corrupted by different sources of noise, interference, and artifact. Here, we present an algorithm for the quantification of signal quality and for the reconstruction of the ABP waveform in noise-corrupted segments of the measurement. ⋯ In segments of poor signal quality, the ABP wavelets are then reconstructed on the basis of the expected cycle duration and envelope information derived from neighboring ABP wavelet segments. The algorithm was tested on two datasets of ABP waveform signals containing both invasive radial artery ABP and noninvasive ABP waveforms. Our results show that the approach is efficient in identifying the noisy segments (accuracy, sensitivity and specificity over 95%) and reliable in reconstructing beats that were artificially corrupted.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
BioWatch - a wrist watch based signal acquisition system for physiological signals including blood pressure.
A wrist watch based system, which can measure electrocardiogram (ECG) and photoplethysmogram (PPG), is presented in this work. By using both ECG and PPG we also measure pulse transit time (PTT), which studies show to correlate well with blood pressure (BP). ⋯ We also validate measurements on different postures and show the value of calibrating the device for each posture. This system, called BioWatch, can potentially facilitate continuous and ubiquitous monitoring of ECG, PPG, heart rate, blood oxygenation and BP.