Journal of medical engineering & technology
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With the rising popularity of activity tracking, there is a desire to not only count the number of steps a person takes, but also identify the type of step (e.g., walking or running) they are taking. For rehabilitation and athletic training, this difference is important to the prescribed regiment. Fourteen healthy adults walked, jogged and ran on a treadmill at three different constant speeds (1.21, 2.01, 2.68 m/s) for 90 s. ⋯ Using primarily acceleration data in the trained ANN yielded the best results (>94% correct stride-type identification) after cross-validation. The ANN models were able to accurately classify the gait type of each stride using a single wearable IMU. The accuracy of the method should improve further as more data is added to the ANN training.
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Electric activity of brain gets disturbed prior to epileptic seizure onset. Early prediction of an upcoming seizure can help to increase effectiveness of antiepileptic drugs. The scalp electroencephalogram signals contain information about the dynamics of brain and have been used to predict an upcoming seizure and localise its zone. ⋯ So, temporal region is identified as the epileptogenic region in this work. For prediction of the epileptic seizure machine learning algorithm artificial neural network (ANN) is proposed. The proposed machine learning algorithm has an accuracy of 92.3%, sensitivity of 100% and specificity of 83.3%.
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Aortic valve (AV) stenosis is described as the deposition of calcium within the valve leaflets. With the growth of stenosis, haemodynamic, mechanical performances of the AV and blood flow through the valve are changed. In this study, we proposed two fluid-structure interaction (FSI) finite element (FE) models. ⋯ In addition, pressure and velocity results were consistent with the echocardiography data literature. We have compared the performance of healthy and stenotic AV models during a complete cardiac cycle. Although improvements are still needed, there was good agreement between our computed data and other published studies.
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The objective of this study was to develop an algorithm for prediction of exacerbation onset in Chronic Obstructive Pulmonary Disease (COPD) patients based on continuous self-monitoring of physiological parameters from telehome-care monitoring. 151 physiological parameters of COPD patients were monitored on a daily/weekly basis for up to 2 years. Data were segmented in 30-day periods leading up to an exacerbation (exacerbation episode) and starting from a 14-day recovery period post-exacerbation (control episode) and tested in 6 intervals to predict exacerbation onset using k-nearest neighbour (k = 1, 3, 5). A classifier with sensitivity of 73%, specificity of 74%, positive predictive value of 69%, negative predictive value of 78% and an accuracy of 74% was achieved using data intervals consisting of 5 days. Intelligent processing of physiological recordings have potential for predicting exacerbation onset.
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Review
Practicability of avoiding hypothermia in resuscitation room phase in severely injured patients.
Hypothermia in severely injured patients is a high demanding situation resulting from an effect of injury severity, surrounding temperature at trauma site and admittance. This article reviews the possible options to combat hypothermia in the resuscitation room with respect to practicability. This review summarizes available passive and active re-warming techniques and trys to offer a practicable chronology to restore normothermia. Resources should be applied depending on the availability of each institution and manifestation of hypothermia, but there is a strong demand for improvements with respect to practicability, convenience and safety for the patient.