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 2013
Demonstrating the accuracy of an in-hospital ambulatory patient monitoring solution in measuring respiratory rate.
This paper presents clinical testing conducted to evaluate the accuracy of Aingeal, a wireless in-hospital patient monitor, in measuring respiration rate via impedance pneumography. Healthy volunteers were invited to simultaneously wear a CE Marked Aingeal vital signs monitor and a capnograph, the current gold standard in respiration rate measurement. ⋯ Statistical analysis of the data collected shows a mean difference of -0.73, a standard deviation of 1.61, limits of agreement of -3.88 and +2.42 bpm and a P-value of 0.22. This testing demonstrates comparable performance of the Aingeal device in measuring respiration rate with a well-accepted and widely used alternative method.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Fully automatic rapid DNA Ploidy Analyzer for intraoperative rapid diagnosis support.
Frozen section studies are a useful method to rapidly define tumor malignancy and identify the extent of surgical resection. However, diagnosis with a frozen section is qualitative and sometimes difficult. Therefore a quantitative method for grading tumors is desired. ⋯ We also obtained a good correlation between the MI and histological grade (WHO grading). Our new system also enabled finishing the process from sample preparation to the end of analysis in ten minutes or less. These results demonstrate that our fully automatic rapid DNA ploidy analyzer is feasible for rapid determination of glioma presence in a surgical biopsy sample.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Osteoporosis risk prediction using machine learning and conventional methods.
A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). ⋯ Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Microwave technology for localization of traumatic intracranial bleedings-a numerical simulation study.
Traumatic brain injury (TBI) is a major public health problem worldwide. Intracranial bleedings represents the most serious complication of TBI and need to be surgically evacuated promptly to save lives and mitigate injury. ⋯ The classification accuracy is 94-100% for all classes, a result that encourages us to pursue our efforts with MWT for more realistic scenarios. This indicates that MWT has potential for localizing a detected bleeding, which would increase the diagnostic value of this technique.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Combined use of sEMG and accelerometer in hand motion classification considering forearm rotation.
Hand motion classification using surface electromyography (sEMG) has been widely studied for its applications in upper-limb prosthesis and human-machine interface etc. Pattern-recognition based control methods have many advantages, and the reported classification accuracy can meet the requirements of practical applications. ⋯ In this paper, we give a pilot study of the reverse effect of forearm rotations on hand motion classification, and the results show that the forearm rotations can substantially degrade the classifier's performance: the average intra-position error is only 2.4%, but the average interposition classification error is as high as 44.0%. To solve this problem, we use an extra accelerometer to estimate the forearm rotation angles, and the best combination of sEMG data and accelerometer outputs can reduce the average classification error to 3.3%.