Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
-
Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Analysis of adventitious lung sounds originating from pulmonary tuberculosis.
Tuberculosis is a common and potentially deadly infectious disease, usually affecting the respiratory system and causing the sound properties of symptomatic infected lungs to differ from non-infected lungs. Auscultation is often ruled out as a reliable diagnostic technique for TB due to the random distribution of the infection and the varying severity of damage to the lungs. However, advancements in signal processing techniques for respiratory sounds can improve the potential of auscultation far beyond the capabilities of the conventional mechanical stethoscope. ⋯ These features were then employed to train a neural network to automatically classify the auscultation recordings into their respective healthy or TB-origin categories. The neural network yielded a diagnostic accuracy of 73%, but it is believed that automated filtering of the noise in the clinics, more training samples and perhaps other signal processing methods can improve the results of future studies. This work demonstrates the potential of computer-aided auscultation as an aid for the diagnosis and treatment of TB.
-
Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Multivariate temporal symptomatic characterization of cardiac arrest.
We model the temporal symptomatic characteristics of 171 cardiac arrest patients in Intensive Care Units. The temporal and feature dependencies in the data are illustrated using a mixture of matrix normal distributions. We found that the cardiac arrest temporal signature is best summarized with six hours data prior to cardiac arrest events, and its statistical descriptions are significantly different from the measurements taken in the past two days. This matrix normal model can classify these patterns better than logistic regressions with lagged features.
-
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.
-
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.
-
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.