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This paper presents the development of two machine learning algorithms on a 32-bit ARM® Cortex® M4 microcontroller core from Freescale Semiconductors. A neural network (ANN) and a support vector machine (SVM) were implemented for real time detection of ventricular tachycardia (VT) and ventricular fibrillation (VF), and they were compared in terms of accuracy. In the feature extraction step a Fast...
A novel approach for reconstructing lost data from correlative signals among multi-parameter physiologic signals is proposed in this paper. The approach extracts a sample form a target signal that has data lost, and then lays the sample one by one according to singularity of a reference signal that has tight correlation with the target signal, to form a reconstructed signal in which a substitution...
Real-time monitoring of vital physiological signals is of significant clinical relevance. Disruptions in the signals are frequently encountered and make it difficult for precise diagnosis. Thus, the ability to accurately predict/recover the lost signals could greatly impact medical research and application. In response to the PhysioNet/CinC Challenge 2010: Mind the Gap, we develop an algorithm based...
Obstructive sleep apnea (OSA) is a common sleeping disorder resulting in the temporary blockage of the upper airways that can lead to increased hypertension and risk to other cardiovascular diseases. Most sleep apnea cases currently remain undiagnosed due to cost and resource limitations of overnight polysomnography at sleep laboratories. In this project, we demonstrate a Simulink-based, modular and...
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