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
Analysis of EEG to quantify depth of anesthesia using Hidden Markov Model.
Real-time quantification of the patient's consciousness level during anesthesia is an important issue to avoid intraoperative awareness and post-operative side effects. A depth-of-anesthesia (DoA) monitoring method called Bispectral Index (BIS) is generally used for this purpose. However, BIS is known to be inaccurate at the transitory state, and also shows a critical time delay in quantifying the patient's consciousness level. ⋯ Since the evaluation of DoA using HMM is training based method, it have better performance with more training process. Experiments show that HDoA has a high correlation with BIS at a steady state, and outperforms BIS in two ways: (1) shorter delay time in transition state, and (2) higher Fisher Score. The validity of HDoA has been tested by 8 real clinical data.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Classification of serous ovarian tumors based on microarray data using multicategory support vector machines.
Ovarian cancer, the most fatal of reproductive cancers, is the fifth leading cause of death in women in the United States. Serous borderline ovarian tumors (SBOTs) are considered to be earlier or less malignant forms of serous ovarian carcinomas (SOCs). SBOTs are asymptomatic and progression to advanced stages is common. ⋯ Application of the optimal model of support vector machines one-versus-rest with signal-to-noise as a feature selection method gave an accuracy of 97.3%, relative classifier information of 0.916, and a kappa index of 0.941. In addition, 5 features, including the expression of putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and SOC groups. An accurate diagnosis of ovarian tumor subclasses by application of multicategory machine learning would be cost-effective and simple to perform, and would ensure more effective subclass-targeted therapy.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Optimising the Windkessel model for cardiac output monitoring during changes in vascular tone.
Algorithms for estimating cardiac output (CO) from the arterial blood pressure wave have been observed to be inaccurate during changes in vascular tone. Many such algorithms are based on the Windkessel model of the circulation. We investigated the optimal analytical approaches and assumptions that make up each algorithm during changes in vascular tone. ⋯ They produced a percentage error of ±31% by maintaining the compliance and outflow terms in the Windkessel model. For any algorithm, the following assumptions gave highest accuracy: (i) outflow pressure into the microcirculation is zero; (ii) end of systole is identified using the second derivative of pressure. None of the tested algorithms reached the clinically acceptable accuracy of ±30%.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Prediction of mortality from respiratory distress among long-term mechanically ventilated patients.
With the advent of inexpensive storage, pervasive networking, and wireless devices, it is now possible to store a large proportion of the medical data that is collected in the intensive care unit (ICU). These data sets can be used as valuable resources for developing and validating predictive analytics. In this report, we focus on the problem of prediction of mortality from respiratory distress among long-term mechanically ventilated patients using data from the publicly-available MIMIC-II database. ⋯ We also find that variables related to respiration rate have more predictive accuracy than variables related to oxygenation status. Ultimately, we have developed a model which predicts mortality from respiratory distress in the ICU with a cross-validated area-under-the-curve (AUC) of approximately 0.74. Four methodologies are utilized for model dimensionality-reduction: univariate logistic regression, multivariate logistic regression, decision trees, and penalized logistic regression.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Facilitating medical information search using Google Glass connected to a content-based medical image retrieval system.
Wearable computing devices are starting to change the way users interact with computers and the Internet. Among them, Google Glass includes a small screen located in front of the right eye, a camera filming in front of the user and a small computing unit. Google Glass has the advantage to provide online services while allowing the user to perform tasks with his/her hands. ⋯ In this paper, we developed a Google Glass application able to take a photo and send it to a medical image retrieval system along with keywords in order to retrieve similar cases. As a preliminary assessment of the usability of the application, we tested the application under three conditions (images of the skin; printed CT scans and MRI images; and CT and MRI images acquired directly from an LCD screen) to explore whether using Google Glass affects the accuracy of the results returned by the medical image retrieval system. The preliminary results show that despite minor problems due to the relative stability of the Google Glass, images can be sent to and processed by the medical image retrieval system and similar images are returned to the user, potentially helping in the decision making process.