- Read more about ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION
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poster.pdf
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- Read more about Cross-domain Joint Dictionary Learning for ECG Reconstruction from PPG
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An emerging research direction considers the inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) to bring about the synergy between the easy measurability of PPG and the rich clinical knowledge of ECG to facilitate preventive healthcare. Previous reconstruction using a universal basis has limited accuracy due to the lack of rich representative power. This paper proposes a cross-domain joint dictionary learning (XDJDL) framework to maximize the expressive power for the two cross-domain signals.
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- Read more about Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides
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- Read more about Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network
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Blood pressure (BP) is a vital sign of the human body and an important parameter for early detection of cardiovascular diseases. It is usually measured using cuff-based devices or monitored invasively in critically-ill patients. This paper presents two techniques that enable continuous and noninvasive cuff-less BP estimation using photoplethysmography (PPG) signals with Convolutional Neural Networks (CNNs). The first technique is calibration-free.
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- Read more about Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion
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Pathological Hand Tremor (PHT) is one of the most prevalent symptoms of some neurological movement disorders such as Parkinson’s Disease (PD) and Essential Tremor (ET). Characterization, estimation, and extraction of PHT is a crucial requirement for assistive and robotic rehabilitation technologies that aim to counteract or resist PHT as an input noise to the system. In general, research in the literature on the topic of PHT removal can be categorized into two major categories, namely, classic and data-driven methods.
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- Read more about AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications
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For their analysis with conventional signal processing tools, non-stationary signals are assumed to be stationary (or at least wide-sense stationary) in short intervals. While this approach allows them to be studied, it disregards the temporal evolution of their statistics. As such, to analyze this type of signals, it is desirable to use a representation that registers and characterizes the temporal changes in the frequency content of the signals, as these changes may occur in single or multiple periodic ways.
globalsip_2019.pdf
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- Read more about Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption – A β-Divergence Approach
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- Read more about A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators
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Thermal analysis using resonating micro-electromechanical systems shows great promise in characterizing materials in the early stages of research. Through thermal cycles and actuation using a piezoelectric speaker, the resonant behaviour of a model drug, theophylline monohydrate, is measured across the surface whilst using a laser-Doppler vibrometer for readout. Acquired is a sequence of spectra that are strongly correlated in time, temperature and spatial location of the readout. Traditionally, each spectrum is analyzed individually to locate the resonance peak.
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- Read more about Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise
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The extraction of multi-exponential decay parameters from multi-temporal images corrupted with Rician noise and with limited time samples proves to be a challenging problem frequently encountered in clinical and food MRI studies. This work aims at proposing a method for the estimation of multiexponential transverse relaxation times from noisy magnitude MRI images. A spatially regularized Maximum-Likelihood estimator accounting for the Rician distribution of the noise is introduced.
ICIP_1976.pdf
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- Read more about DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT
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Depression is a common mental disorder, which greatly affects the patients' daily life and work. Current depression detection relies almost exclusively on the clinical interview and structured questionnaire, consuming a lot of medical resources and risking a range of subjective biases. Our goal is to achieve a convenient and objective depression detection system, which can assist clinicians in their diagnosis of clinical depression.
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