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A novel approach to quantify sympathetic function via the electrodermal activity (EDA) variability (EDAV) is proposed. EDAV involves power spectral density analysis of EDA data, focusing on the normalized power within the frequency band from 0.045 to 0.15 Hz, termed EDALFn. To test this index, orthostatic stress was induced on N = 10 subjects. Results showed a significant increase in the EDALFn when...
The focus of this research is the implementation of a control system which actuates a wearable mechatronics-enabled elbow brace (ME-brace) through elbow motion tracking tasks using estimates derived from an EMG-driven mapping model. The accuracy and repeatability of the control system during a tracking task, using data collected from healthy subjects, has been quantified to support improvement of...
The transformation of the American healthcare payment system from fee-for-service to value-based care increasingly makes it valuable to develop patient registries for specialized populations, to better assess healthcare quality and costs. Recent widespread adoption of Electronic Health Records (EHRs) in the U.S. now makes possible construction of EHR-based specialty registry data collection tools...
Data analytics is playing an important role in health care because of the potential actionable insights that can be derived from individual-level medical records in the electronic health records (EHRs). This paper explores the utilization of EHR data for predictive analytics at an academic health system in Singapore to facilitate patient stratification for intensive case management among individuals...
One of the prominent clinical manifestations of schizophrenia is flat or altered facial activity, and flattening of emotional expressiveness (Flat Affect). In this study we used a structured-light depth camera and dedicated software to automatically measure the facial activity of schizophrenia patients and healthy individuals during a short structured interview. Based on K-means clustering analysis,...
BioAssay involves the use of live animal or plant (in vivo) or tissue or cell (in vitro) to determine the biological activity of a substance, such as hormone or drug, which plays a key role especially in evaluation of clinical efficacy for drug development. How to better utilize the biological information contained in the BioAssay for systematical identification of novel biological associations is...
The accuracy of part of speech (POS) tagging reported in medical natural language processing (NLP) literature is typically very high when training and testing data sets are from the same domain and have similar characteristics, but is lower when these differ. This presents a problem for clinical NLP, where it is difficult to obtain large corpora of training data suitable for localized tasks. We experimented...
With aim of reducing the incidence of false critical arrhythmia alarms in intensive care units, a novel data fusion and machine learning algorithm is presented in this article. The 2015 PhysioNet/Computing in Cardiology Challenge database was used in this present algorithm, with each grouped as an asystole (AS), extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT) or ventricular...
This work investigates how Hidden Semi-Markov Model (HSMM) can be used to monitor and evaluate physical rehabilitation exercises by Kinect v2 to support medical personnel and patients during rehabilitation at home. Authors developed an exercises assessment method based on the extraction of motion features determined by clinicians. Five different rehabilitation exercises are modeled using a HSMM to...
Current diagnosis of coronary artery disease relies on visual examination of angiograms by operators to identify significant stenoses in arteries. The significant limitations of this approach are both under and over-calling of stenoses. Although techniques such as quantitative coronary angiography (QCA) are available, these still require human input and resource costs. Thus, an automated system is...
Brain-computer interfaces (BCI) have the potential to play a vital role in future healthcare technologies by providing an alternative way of communication and control. More specifically, steady-state visual evoked potential (SSVEP) based BCIs have the advantage of higher accuracy and higher information transfer rate (ITR). In order to fully exploit the capabilities of such devices, it is necessary...
This paper is to develop an automated assessment system of upper-limb motor function impairment for clinical environment. Although we had proposed the system in our previous work, there are some rooms to be improved. Using glove sensor was difficult due to stroke patient's hand contracture. Moreover, it was based on machine learning, and thus required huge effort to collect reference data to increase...
Kidney stone is among the common painful disorders of the urinary system. The presence of stones in the kidney refers to Nephrolithiasis. For nephrolithiasis and kidney stones detection, a C-arm tomographic technique was investigated in this paper to generate three dimensional kidney structural information. A series of two dimensional (2D) x-ray projection images were acquired by moving the x-ray...
Biomedical in vivo imaging has been playing an essential role in diagnoses and treatment in modern medicine. However, compared with the fast development of medical imaging systems, the medical imaging informatics, especially automated prediction, has not been fully explored. In our paper, we compared different feature extraction and classification methods for prediction pipeline to analyze in vivo...
Complications during pregnancy and childbirth are the leading cause of death among women and neonates in developing countries. To mitigate some of the risks, our research set out to develop an Android Based Digital Fetoscope, an application that would ease fetal heart monitoring and potentially reduce the number of neonatal deaths. We developed the data acquisition circuitry using a conventional pinard...
Anatomical network analysis is considered as a significant way to study brains. The attributes of anatomical networks vary across network nodal scales and therefore a scalable network mapping method is needed. Here, a new framework for mapping scalable brain anatomical networks via d-MRI is presented. The modelling of nodes is based on the structural basis of brain connections (white matter) and the...
Passive RFID tags provide a promising way to create wireless and battery-free heart rate monitors. However, the reliability of these tags is limited in the presence of common noise sources in their environment. In this paper, we propose an algorithm to improve the beat detection for RFID based heart rate monitors in noisy environments. To achieve this, a logistic regression model is first employed...
Deep brain stimulation, as a primary surgical treatment for various neurological disorders, involves implanting electrodes to stimulate target nuclei within millimeter accuracy. Accurate pre-operative target selection is challenging due to the poor contrast in its surrounding region in MR images. In this paper, we present a learning-based method to automatically and rapidly localize the target using...
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