IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Dec 2018
Medical Image Synthesis with Deep Convolutional Adversarial Networks.
Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality without incurring an actual scan. ⋯ Experimental results show that our method is accurate and robust for synthesizing target images from the corresponding source images. In particular, we evaluate our method on three datasets, to address the tasks of generating CT from MRI and generating 7T MRI from 3T MRI images. Our method outperforms the state-of-the-art methods under comparison in all datasets and tasks.
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IEEE Trans Biomed Eng · Nov 2017
A Novel Framework for Motion-Tolerant Instantaneous Heart Rate Estimation by Phase-Domain Multiview Dynamic Time Warping.
long-term wearable instantaneous heart rate (IHR) monitoring is essential to enable pervasive heart health and fitness management. In this paper, a novel framework is proposed to robustly estimate the IHR from electrocardiogram (ECG) signals corrupted by large amounts of daily motion artifacts, which are one of the major impediments against the long-term IHR monitoring. ⋯ the proposed framework greatly outperforms previously reported approaches and is expected to contribute to long-term IHR monitoring.
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IEEE Trans Biomed Eng · Sep 2017
Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification.
The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. ⋯ Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.
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IEEE Trans Biomed Eng · Sep 2017
Accurate Heart Rate Monitoring During Physical Exercises Using PPG.
The challenging task of heart rate (HR) estimation from the photoplethysmographic (PPG) signal, during intensive physical exercises, is tackled in this paper. ⋯ The proposed system is shown to be accurate in the presence of strong motion artifacts and in contrast to existing alternatives has very few free parameters to tune. The algorithm has a low computational cost and can be used for fitness tracking and health monitoring in wearable devices. The MATLAB implementation of the algorithm is provided online.
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IEEE Trans Biomed Eng · Sep 2017
Towards Photoplethysmography-Based Estimation of Instantaneous Heart Rate During Physical Activity.
Recently numerous methods have been proposed for estimating average heart rate using photoplethysmography (PPG) during physical activity, overcoming the significant interference that motion causes in PPG traces. We propose a new algorithm framework for extracting instantaneous heart rate from wearable PPG and Electrocardiogram (ECG) signals to provide an estimate of heart rate variability during exercise. ⋯ Instantaneous heart rate provides a direct measure of vagal nerve and sympathetic nervous system activity and is of substantial use in a number of analyzes and applications. Previously it has not been possible to estimate instantaneous heart rate from wrist wearable PPG signals.