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Full-Text Articles in Biomedical

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette Jan 2023

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette

Electrical & Computer Engineering Faculty Publications

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …


Atlas-Based Shared-Boundary Deformable Multi-Surface Models Through Multi-Material And Two-Manifold Dual Contouring, Tanweer Rashid, Sharmin Sultana, Mallar Chakravarty, Michel Albert Audette Jan 2023

Atlas-Based Shared-Boundary Deformable Multi-Surface Models Through Multi-Material And Two-Manifold Dual Contouring, Tanweer Rashid, Sharmin Sultana, Mallar Chakravarty, Michel Albert Audette

Electrical & Computer Engineering Faculty Publications

This paper presents a multi-material dual “contouring” method used to convert a digital 3D voxel-based atlas of basal ganglia to a deformable discrete multi-surface model that supports surgical navigation for an intraoperative MRI-compatible surgical robot, featuring fast intraoperative deformation computation. It is vital that the final surface model maintain shared boundaries where appropriate so that even as the deep-brain model deforms to reflect intraoperative changes encoded in ioMRI, the subthalamic nucleus stays in contact with the substantia nigra, for example, while still providing a significantly sparser representation than the original volumetric atlas consisting of hundreds of millions of voxels. The …


Algorithm For Premature Ventricular Contraction Detection From A Subcutaneous Electrocardiogram Signal, Iris Lynn Shelly Dec 2016

Algorithm For Premature Ventricular Contraction Detection From A Subcutaneous Electrocardiogram Signal, Iris Lynn Shelly

Dissertations and Theses

Cardiac arrhythmias occur when the normal pattern of electrical signals in the heart breaks down. A premature ventricular contraction (PVC) is a common type of arrhythmia that occurs when a heartbeat originates from an ectopic focus within the ventricles rather than from the sinus node in the right atrium. This and other arrhythmias are often diagnosed with the help of an electrocardiogram, or ECG, which records the electrical activity of the heart using electrodes placed on the skin. In an ECG signal, a PVC is characterized by both timing and morphological differences from a normal sinus beat.

An implantable cardiac …


Fusion Of Visual And Thermal Images Using Genetic Algorithms, Sertan Erkanli, Jiang Li, Ender Oguslu, Shangce Gao (Ed.) Jan 2012

Fusion Of Visual And Thermal Images Using Genetic Algorithms, Sertan Erkanli, Jiang Li, Ender Oguslu, Shangce Gao (Ed.)

Electrical & Computer Engineering Faculty Publications

No abstract provided.


Histogram Analysis Of Adc In Brain Tumor Patients, Debrup Banerjee, Jihong Wang, Jiang Li, Norbert J. Pelc (Ed.), Ehsan Samei (Ed.), Robert M. Nishikawa (Ed.) Jan 2011

Histogram Analysis Of Adc In Brain Tumor Patients, Debrup Banerjee, Jihong Wang, Jiang Li, Norbert J. Pelc (Ed.), Ehsan Samei (Ed.), Robert M. Nishikawa (Ed.)

Electrical & Computer Engineering Faculty Publications

At various stage of progression, most brain tumors are not homogenous. In this presentation, we retrospectively studied the distribution of ADC values inside tumor volume during the course of tumor treatment and progression for a selective group of patients who underwent an anti-VEGF trial. Complete MRI studies were obtained for this selected group of patients including pre- and multiple follow-up, post-treatment imaging studies. In each MRI imaging study, multiple scan series were obtained as a standard protocol which includes T1, T2, T1-post contrast, FLAIR and DTI derived images (ADC, FA etc.) for each visit. All scan series (T1, T2, FLAIR, …


In Vivo Ultrasonic Attenuation Slope Estimates For Detecting Cervical Ripening In Rats: Preliminary Results, Timothy A. Bigelow, Barbara L. Mcfarlin, William D. O'Brien Jr., Michael L. Oelze Mar 2008

In Vivo Ultrasonic Attenuation Slope Estimates For Detecting Cervical Ripening In Rats: Preliminary Results, Timothy A. Bigelow, Barbara L. Mcfarlin, William D. O'Brien Jr., Michael L. Oelze

Timothy A. Bigelow

To effectively postpone preterm birth, cervical ripening needs to be detected and delayed. As the cervix ripens, the spacing between the collagen fibers increases and fills with water, hyaluronan, decorin, and enzymes suggesting that the ultrasonic attenuation of the cervix should decrease. The decrease in ultrasonic attenuation may be detectable, leading to an effective means of detecting cervical ripening. Herein, the traditional attenuation slope-estimation algorithm based on measuring the downshift in center frequency of the ultrasonic backscattered signal with propagation depth was modified and applied to the cervix of rats. The modified algorithm was verified using computer simulations and an …


Hybrid Committee Classifier For A Computerized Colonic Polyp Detection System, Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara, Joseph M. Reinhardt (Ed.), Josien P.W. Pluim (Ed.) Jan 2006

Hybrid Committee Classifier For A Computerized Colonic Polyp Detection System, Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara, Joseph M. Reinhardt (Ed.), Josien P.W. Pluim (Ed.)

Electrical & Computer Engineering Faculty Publications

We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features …