Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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Annu Int Conf IEEE Eng Med Biol Soc · Aug 2015
Estimation of heart rate from photoplethysmography during physical exercise using Wiener filtering and the phase vocoder.
A system for estimation of the heart rate (HR) from the photoplethysmographic (PPG) signal during intensive physical exercises is presented. The Wiener filter is used to attenuate the noise introduced by the motion artifacts in the PPG signals. The frequency with the highest magnitude estimated using Fourier transformation is selected from the resultant de-noised signal. ⋯ A relative error rate reduction of 18% is obtained when comparing with the state-of-the art PPG-based HR estimation methods. The proposed system is shown to be robust to strong motion artifact, produces high accuracy results and has very few free parameters, in contrast to other available approaches. The algorithm has low computational cost and can be used for fitness tracking and health monitoring in wearable devices.
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Annu Int Conf IEEE Eng Med Biol Soc · Aug 2015
In-vitro testing of RF-enabled low force mechanical thrombectomy for ischemic stroke.
Mechanical thrombectomy for ischemic stroke has high recanalization rate, long treatment time window and low hemorrhage risk. However, the clot engagement approach of caging the clot against the vessel wall can cause vessel stenosis and stroke recurrence. A device with reduced radial stenting force that reduces vessel wall friction would minimize stenosis and damage. ⋯ New mechanical thrombectomy devices enabled with RF (Patent No.: US 62/172,043) were built and tested on human blood clots in vessels ex vivo. Test results showed that the RF-mechanical thrombectomy successfully and reproducibly captured and retrieved the clots without relying on stent caging of the clot against the vessel wall. Further work will be conducted on animals to compare vessel wall damage between conventional and RF-mechanical thrombectomy.
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Annu Int Conf IEEE Eng Med Biol Soc · Aug 2015
Evaluation of the variation in sensory test results using Semmes-Weinstein monofilaments.
The purpose of this study is to examine the variability in sensory test of tactile results using Semmes-Weinstein monofilament (SWM). At present, several methods for measuring the tactile sensitivity are clinically used in diabetic peripheral neuropathy screening. One of these methods is a touch test that uses a device with nylon SWMs, i.e., SWMs embedded in a plastic handle. ⋯ Additionally, manual training for standardizing skills of medical staff members needs to be developed. Furthermore, the characteristics of the SWMs deteriorated over time. In future work, we aimto find a solution to minimize the variability in the SWM test results and develop a new testing system that uses tactile sensibility for diabetic peripheral neuropathy screening.
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Annu Int Conf IEEE Eng Med Biol Soc · Aug 2015
The predictive power of structural MRI in Autism diagnosis.
Diagnosis of Autism Spectrum Disorder (ASD) using structural magnetic resonance imaging (sMRI) of the brain has been a topic of significant research interest. Previous studies using small datasets with well-matched Typically Developing Controls (TDC) report high classification accuracies (80-96%) but studies using the large heterogeneous ABIDE dataset report accuracies less than 60%. In this study we investigate the predictive power of sMRI in ASD using 373 ASD and 361 TDC male subjects from the ABIDE. ⋯ In general, important features for classification were present in the frontal and temporal regions and these regions have been implicated in ASD. This study also explores the effect of demographics and behavioral measures on the predictive power of sMRI. Results show that classification accuracy increases with autism severity and that ASD detection with sMRI is easier before the age of 10 years.
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Annu Int Conf IEEE Eng Med Biol Soc · Aug 2015
Identification of epileptogenic networks from dense EEG: A model-based study.
Epilepsy is a network disease. Identifying the epileptogenic networks from noninvasive recordings is a challenging issue. In this context, M/EEG source connectivity is a promising tool to identify brain networks with high temporal and spatial resolution. ⋯ Results show that the choice of the combination has a high impact on the identified network. Results suggest also that nonlinear methods (nonlinear correlation coefficient and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The dSPM as inverse solution shows the lowest performance compared to cMEM and wMNE.