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
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
Assessment of white matter microstructure in stroke patients using NODDI.
Diffusion weighted imaging (DWI) is widely used to study changes in white matter following stroke. In various studies employing diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) modalities, it has been shown that fractional anisotropy (FA), mean diffusivity (MD), and generalized FA (GFA) can be used as measures of white matter tract integrity in stroke patients. However, these measures may be non-specific, as they do not directly delineate changes in tissue microstructure. ⋯ By computing NODDI indices over the entire brain in two stroke patients, and comparing tissue regions in ipsilesional and contralesional hemispheres, we demonstrate that NODDI modeling provides specific information on tissue microstructural changes. We also introduce an information theoretic analysis framework to investigate the non-local effects of stroke in the white matter. Our initial results suggest that the NODDI indices might be more specific markers of white matter reorganization following stroke than other measures previously used in studies of stroke recovery.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Respiratory rate assessment from photoplethysmographic imaging.
We present a study investigating the suitability of a respiratory rate estimation algorithm applied to photoplethysmographic imaging on a mobile phone. The algorithm consists of a cascade of previously developed signal processing methods to detect features and extract respiratory induced variations in photoplethysmogram signals to estimate respiratory rate. With custom-built software on an Android phone (Camera Oximeter), contact photoplethysmographic imaging videos were recorded using the integrated camera from 19 healthy adults breathing spontaneously at respiratory rates between 6 and 40 breaths/min. ⋯ The algorithm detected 22 recordings with poor photoplethysmogram quality and 46 recordings with insufficient respiratory information. Of the 232 remaining recordings, a root mean square error of 5.9 breaths/min and a median absolute error of 2.3 breaths/min was obtained. The study showed that it is feasible to estimate respiratory rates by placing a finger on a mobile phone camera, but that it becomes increasingly challenging at respiratory rates higher than 20 breaths/min.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Wearable seismocardiography for the beat-to-beat assessment of cardiac intervals during sleep.
Seismocardiogram (SCG) can be detected during sleep by a textile-based wearable system. This pilot study preliminarily explores the feasibility of a beat-to-beat estimation of cardiac mechanical features (RR interval, RRI, Pre-Ejection Period, PEP, Isovolumic Contraction Time, ICT, Left Ventricular Ejection Time, LVET, Isovolumic Relaxation Time, IRT) from the joint ECG and SCG assessment during sleep. ⋯ These findings represent the very first description of the beat-to-beat variability of cardiac mechanical indexes. Further investigations on a larger population are in progress to confirm the present results.