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https://www.readbyqxmd.com/read/28231405/three-dimensional-printing-of-x-ray-computed-tomography-datasets-with-multiple-materials-using-open-source-data-processing
#1
Ian M Sander, Matthew T McGoldrick, My N Helms, Aislinn Betts, Anthony van Avermaete, Elizabeth Owers, Evan Doney, Taimi Liepert, Glen Niebur, Douglas Liepert, W Matthew Leevy
Advances in three-dimensional (3D) printing allow for digital files to be turned into a "printed" physical product. For example, complex anatomical models derived from clinical or pre-clinical X-ray computed tomography (CT) data of patients or research specimens can be constructed using various printable materials. Although 3D printing has the potential to advance learning, many academic programs have been slow to adopt its use in the classroom despite increased availability of the equipment and digital databases already established for educational use...
February 23, 2017: Anatomical Sciences Education
https://www.readbyqxmd.com/read/28230848/computational-approaches-to-fmri-analysis
#2
Jonathan D Cohen, Nathaniel Daw, Barbara Engelhardt, Uri Hasson, Kai Li, Yael Niv, Kenneth A Norman, Jonathan Pillow, Peter J Ramadge, Nicholas B Turk-Browne, Theodore L Willke
Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis...
February 23, 2017: Nature Neuroscience
https://www.readbyqxmd.com/read/28230796/a-sensor-data-fusion-system-based-on-k-nearest-neighbor-pattern-classification-for-structural-health-monitoring-applications
#3
Jaime Vitola, Francesc Pozo, Diego A Tibaduiza, Maribel Anaya
Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis...
February 21, 2017: Sensors
https://www.readbyqxmd.com/read/28230767/an-adaptive-multi-sensor-data-fusion-method-based-on-deep-convolutional-neural-networks-for-fault-diagnosis-of-planetary-gearbox
#4
Luyang Jing, Taiyong Wang, Ming Zhao, Peng Wang
A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections...
February 21, 2017: Sensors
https://www.readbyqxmd.com/read/28230725/towards-a-semantic-web-of-things-a-hybrid-semantic-annotation-extraction-and-reasoning-framework-for-cyber-physical-system
#5
Zhenyu Wu, Yuan Xu, Yunong Yang, Chunhong Zhang, Xinning Zhu, Yang Ji
Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability...
February 20, 2017: Sensors
https://www.readbyqxmd.com/read/28229175/survival-prediction-of-trauma-patients-a-study-on-us-national-trauma-data-bank
#6
I Sefrioui, R Amadini, J Mauro, A El Fallahi, M Gabbrielli
BACKGROUND: Exceptional circumstances like major incidents or natural disasters may cause a huge number of victims that might not be immediately and simultaneously saved. In these cases it is important to define priorities avoiding to waste time and resources for not savable victims. Trauma and Injury Severity Score (TRISS) methodology is the well-known and standard system usually used by practitioners to predict the survival probability of trauma patients. However, practitioners have noted that the accuracy of TRISS predictions is unacceptable especially for severely injured patients...
February 22, 2017: European Journal of Trauma and Emergency Surgery: Official Publication of the European Trauma Society
https://www.readbyqxmd.com/read/28228010/selective-fusion-of-heterogeneous-classifiers-for-predicting-substrates-of-membrane-transporters
#7
Naeem Shaikh, Mahesh Sharma, Prabha Garg
Membrane transporters play a crucial role in determining fate of administered drugs in a biological system. Early identification of plausible transporters for a drug molecule can provide insights into its therapeutic, pharmacokinetic and toxicological profile. In the present study, predictive models for classifying small molecules into substrates and non-substrates of various pharmaceutically important membrane transporters are developed using QSAR and proteochemometric (PCM) approaches. For this purpose, 4575 substrate interactions for these transporters were collected from Metabolism and Transport Database (Metrabase) and literature...
February 23, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28227975/an-adaptive-deep-learning-approach-for-ppg-based-identification
#8
V Jindal, J Birjandtalab, M Baran Pouyan, M Nourani, V Jindal, J Birjandtalab, M Baran Pouyan, M Nourani, V Jindal, M Baran Pouyan, J Birjandtalab, M Nourani
Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227970/combining-a-hybrid-robotic-system-with-a-bain-machine-interface-for-the-rehabilitation-of-reaching-movements-a-case-study-with-a-stroke-patient
#9
F Resquin, J Ibanez, J Gonzalez-Vargas, F Brunetti, I Dimbwadyo, S Alves, L Carrasco, L Torres, Jose Luis Pons, F Resquin, J IbaƱez, J Gonzalez-Vargas, F Brunetti, I Dimbwadyo, S Alves, L Carrasco, L Torres, Jose Luis Pons, S Alves, Jose Luis Pons, F Brunetti, J Gonzalez-Vargas, F Resquin, I Dimbwadyo, L Carrasco, L Torres, J Ibanez
Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user's movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227951/smartsock-a-wearable-platform-for-context-aware-assessment-of-ankle-edema
#10
Ramin Fallahzadeh, Mahdi Pedram, Hassan Ghasemzadeh, Ramin Fallahzadeh, Mahdi Pedram, Hassan Ghasemzadeh
Ankle edema an important symptom for monitoring patients with chronic systematic diseases. It is an important indicator of onset or exacerbation of a variety of diseases that disturb cardiovascular, renal, or hepatic system such as heart, liver, and kidney failure, diabetes, etc. The current approaches toward edema assessment are conducted during clinical visits. In-clinic assessments, in addition to being burdensome and expensive, are sometimes not reliable and neglect important contextual factors such as patient's physical activity level and body posture...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227926/a-fast-approximation-method-for-principal-component-analysis-applied-to-ecg-derived-respiration-for-osa-detection
#11
Nadi Sadr, Philip de Chazal, Nadi Sadr, Philip de Chazal, Nadi Sadr, Philip de Chazal
In this paper, we present an approximation method for principal component analysis (PCA) and apply it to estimating the respiration from the overnight ECG signal. The approximation method is computationally fast with low memory requirements. We compare it to a full PCA method which is applied to segments of the ECG. Features were calculated from the two ECG derived respiration signals (EDR) and classifiers trained to detect obstructive sleep apnoea (OSA). The Extreme Learning Machine and Linear Discriminant classifier were used to classify the recordings...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227925/principal-component-analysis-can-decrease-neural-networks-performance-for-incipient-falls-detection-a-preliminary-study-with-hands-and-feet-accelerations
#12
Fiorenzo Artoni, Dario Martelli, Vito Monaco, Silvestre Micera, Fiorenzo Artoni, Dario Martelli, Vito Monaco, Silvestre Micera
Fall-related accidents constitute a major problem for elderly people and a burden to the health-care national system. It is therefore important to design devices (e.g., accelerometers) and machine learning algorithms able to recognize incipient falls as quickly and reliably as possible. Blind source separation (BSS) methods are often used as a preprocessing step before classification, however the effects of BSS on classification performance are not well understood. The aim of this work is to preliminarily characterize the effect that two methods, namely Principal and Independent Component Analysis (PCA and ICA) and their combined use have on the performance of a neural network in detecting incipient falls...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227914/automated-classification-of-pathological-gait-after-stroke-using-ubiquitous-sensing-technology
#13
Elham Dolatabadi, Babak Taati, Alex Mihailidis, Elham Dolatabadi, Babak Taati, Alex Mihailidis, Babak Taati, Alex Mihailidis, Elham Dolatabadi
This study uses machine learning methods to distinguish between healthy and pathological gait. Examples of multi-dimensional pathological and normal gait sequences were collected from post-stroke and healthy individuals in a real clinical setting and with two Kinect sensors. The trajectories of rotational angle and global velocity of selected body joints (hips, spine, shoulders, neck, knees and ankles) over time formed the gait sequences. The combination of k nearest neighbor (kNN) and dynamic time warping (DTW) was used for classification...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227877/a-wearable-computing-platform-for-developing-cloud-based-machine-learning-models-for-health-monitoring-applications
#14
Shyamal Patel, Ryan S McGinnis, Ikaro Silva, Steve DiCristofaro, Nikhil Mahadevan, Elise Jortberg, Jaime Franco, Albert Martin, Joseph Lust, Milan Raj, Bryan McGrane, Paolo DePetrillo, A J Aranyosi, Melissa Ceruolo, Jesus Pindado, Roozbeh Ghaffari, Shyamal Patel, Ryan S McGinnis, Ikaro Silva, Steve DiCristofaro, Nikhil Mahadevan, Elise Jortberg, Jaime Franco, Albert Martin, Joseph Lust, Milan Raj, Bryan McGrane, Paolo DePetrillo, A J Aranyosi, Melissa Ceruolo, Jesus Pindado, Roozbeh Ghaffari
Wearable sensors have the potential to enable clinical-grade ambulatory health monitoring outside the clinic. Technological advances have enabled development of devices that can measure vital signs with great precision and significant progress has been made towards extracting clinically meaningful information from these devices in research studies. However, translating measurement accuracies achieved in the controlled settings such as the lab and clinic to unconstrained environments such as the home remains a challenge...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227738/tuning-electrical-stimulation-for-thalamic-visual-prosthesis-an-autoencoder-based-approach
#15
Amr Jawwad, Hossam H Abolfotuh, Bassem Abdullah, Hani M K Mahdi, Seif Eldawlatly, Amr Jawwad, Hossam H Abolfotuh, Bassem Abdullah, Hani M K Mahdi, Seif Eldawlatly, Amr Jawwad, Hani M K Mahdi, Bassem Abdullah, Seif Eldawlatly, Hossam H Abolfotuh
Visual prosthesis holds hope of vision restoration for millions with retinal degenerative diseases. Machine learning techniques such as artificial neural networks could help in improving prosthetic devices as they could learn how the brain encodes information and imitate that code. This paper introduces an autoencoder-based approach for tuning thalamic visual prostheses. The objective of the proposed approach is to estimate electrical stimuli that are equivalent to a given natural visual stimulus, in a way such that they both elicit responses that are as similar as possible when introduced to a Lateral Geniculate Nucleus (LGN) population...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227732/a-hybrid-rule-and-machine-learning-based-generic-alerting-platform-for-smart-environments
#16
Joseph Rafferty, Jonathan Synnott, Chris Nugent, Joseph Rafferty, Jonathan Synnott, Chris Nugent, Jonathan Synnott, Joseph Rafferty, Chris Nugent
Existing smart environment based alert solutions have adopted a relatively complex and tailored approach to supporting individuals. These solutions have involved sensor based monitoring, activity recognition and assistance provisioning. Traditionally they have suffered from a number of issues, rooted in scalability and performance, associated with complex activity recognition processes. This paper introduces a generic approach to realizing an alerting platform within a smart environment. The core concept of this approach is presented and placed within the context of related work...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227720/can-we-make-a-carpet-smart-enough-to-detect-falls
#17
Fadi Muheidat, Harry W Tyrer, Fadi Muheidat, Harry W Tyrer, Harry W Tyrer, Fadi Muheidat
In this paper, we have enhanced smart carpet, which is a floor based personnel detector system, to detect falls using a faster but low cost processor. Our hardware front end reads 128 sensors, with sensors output a voltage due to a person walking or falling on the carpet. The processor is Jetson TK1, which provides more computing power than before. We generated a dataset with volunteers who walked and fell to test our algorithms. Data obtained allowed examining data frames (a frame is a single scan of the carpet sensors) read from the data acquisition system...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227711/detection-of-fetal-kicks-using-body-worn-accelerometers-during-pregnancy-trade-offs-between-sensors-number-and-positioning
#18
Marco Altini, Patrick Mullan, Michiel Rooijakkers, Stefan Gradl, Julien Penders, Nele Geusens, Lars Grieten, Bjoern Eskofier, Marco Altini, Patrick Mullan, Michiel Rooijakkers, Stefan Gradl, Julien Penders, Nele Geusens, Lars Grieten, Bjoern Eskofier, Julien Penders, Michiel Rooijakkers, Nele Geusens, Stefan Gradl, Patrick Mullan, Lars Grieten, Bjoern Eskofier, Marco Altini
Monitoring fetal wellbeing is key in modern obstetrics. While fetal movement is routinely used as a proxy to fetal wellbeing, accurate, noninvasive, long-term monitoring of fetal movement is challenging. A few accelerometer-based systems have been developed in the past few years, to tackle common issues in ultrasound measurement and enable remote, self-administrated monitoring of fetal movement during pregnancy. However, many questions remain unanswered to date on the optimal setup in terms of body-worn accelerometers as well as signal processing and machine learning techniques used to detect fetal movement...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227698/predicting-brain-stimulation-treatment-outcomes-of-depressed-patients-through-the-classification-of-eeg-oscillations
#19
Alaa M Al-Kaysi, Ahmed Al-Ani, Colleen K Loo, Michael Breakspear, Tjeerd W Boonstra, Alaa M Al-Kaysi, Ahmed Al-Ani, Colleen K Loo, Michael Breakspear, Tjeerd W Boonstra, Michael Breakspear, Ahmed Al-Ani, Tjeerd W Boonstra, Colleen K Loo
Major depressive disorder (MDD) is a mental disorder that is characterized by negative thoughts, mood and behavior. Transcranial direct current stimulation (tDCS) has recently emerged as a promising brain-stimulation treatment for MDD. A standard tDCS treatment involves numerous sessions that run over a few weeks, however, not all participants respond to this type of treatment. This study aims to predict which patients improve in mood and cognition in response to tDCS treatment by analyzing electroencephalography (EEG) of MDD patients that was collected at the start of tDCS treatment...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227680/a-mobile-platform-for-automated-screening-of-asthma-and-chronic-obstructive-pulmonary-disease
#20
Daniel B Chamberlain, Rahul Kodgule, Richard Ribon Fletcher, Daniel B Chamberlain, Rahul Kodgule, Richard Ribon Fletcher, Daniel B Chamberlain, Richard Ribon Fletcher, Rahul Kodgule
Chronic Obstructive Pulmonary Disease (COPD) and asthma each represent a large proportion of the global disease burden; COPD is the third leading cause of death worldwide and asthma is one of the most prevalent chronic diseases, afflicting over 300 million people. Much of this burden is concentrated in the developing world, where patients lack access to physicians trained in the diagnosis of pulmonary disease. As a result, these patients experience high rates of underdiagnosis and misdiagnosis. To address this need, we present a mobile platform capable of screening for Asthma and COPD...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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