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https://www.readbyqxmd.com/read/28227996/a-novel-approach-for-chewing-detection-based-on-a-wearable-ppg-sensor
#1
Vasileios Papapanagiotou, Christos Diou, Lingchuan Zhou, Janet van den Boer, Monica Mars, Anastasios Delopoulos, Vasileios Papapanagiotou, Christos Diou, Lingchuan Zhou, Janet van den Boer, Monica Mars, Anastasios Delopoulos, Janet van den Boer, Vasileios Papapanagiotou, Lingchuan Zhou, Anastasios Delopoulos, Monica Mars, Christos Diou
Monitoring of human eating behaviour has been attracting interest over the last few years, as a means to a healthy lifestyle, but also due to its association with serious health conditions, such as eating disorders and obesity. Use of self-reports and other non-automated means of monitoring have been found to be unreliable, compared to the use of wearable sensors. Various modalities have been reported, such as acoustic signal from ear-worn microphones, or signal from wearable strain sensors. In this work, we introduce a new sensor for the task of chewing detection, based on a novel photoplethysmography (PPG) sensor placed on the outer earlobe to perform the task...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227975/an-adaptive-deep-learning-approach-for-ppg-based-identification
#2
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/28227964/cross-entropy-optimization-for-neuromodulation
#3
Harleen K Brar, Yunpeng Pan, Babak Mahmoudi, Evangelos A Theodorou, Harleen K Brar, Yunpeng Pan, Babak Mahmoudi, Evangelos A Theodorou, Yunpeng Pan, Harleen K Brar, Babak Mahmoudi, Evangelos A Theodorou
This study presents a reinforcement learning approach for the optimization of the proportional-integral gains of the feedback controller represented in a computational model of epilepsy. The chaotic oscillator model provides a feedback control systems view of the dynamics of an epileptic brain with an internal feedback controller representative of the natural seizure suppression mechanism within the brain circuitry. Normal and pathological brain activity is simulated in this model by adjusting the feedback gain values of the internal controller...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227960/automated-volumetry-for-unilateral-hippocampal-sclerosis-detection-in-patients-with-temporal-lobe-epilepsy
#4
Cristina Martins, Nadia Moreira da Silva, Guilherme Silva, Verena E Rozanski, Joao Paulo Silva Cunha, Cristina Martins, Nadia Moreira da Silva, Guilherme Silva, Verena E Rozanski, Joao Paulo Silva Cunha, Joao Paulo Silva Cunha, Guilherme Silva, Verena E Rozanski, Nadia Moreira da Silva, Cristina Martins
Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy (TLE) and can be identified in magnetic resonance imaging as hippocampal atrophy and subsequent volume loss. Detecting this kind of abnormalities through simple radiological assessment could be difficult, even for experienced radiologists. For that reason, hippocampal volumetry is generally used to support this kind of diagnosis. Manual volumetry is the traditional approach but it is time consuming and requires the physician to be familiar with neuroimaging software tools...
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
#5
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/28227943/an-unsupervised-approach-to-detecting-and-isolating-athletic-movements
#6
Terry Taewoong Um, Dana Kulic, Terry Taewoong Um, Dana Kulic, Dana Kulic
To enable automatic analysis of athletic movement, the first task is to recognize the athletic movements to be analyzed from a continuous motion data stream. Automated detection of athletic movement and the isolation of the recruited body parts would enable the analysis of sporting movements for improving sports performance and preventing possible injuries. In this paper, an unsupervised method for detecting and isolating athletic movements is proposed. Given motion capture data, the method automatically identifies when athletic movements are being performed and the body parts involved using the concepts of the manipulability and kinematic dimensionality reduction...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227935/heart-sound-segmentation-using-fractal-decomposition
#7
Rijil Thomas, Ling Lieng Hsi, Soh Cheong Boon, Erry Gunawan, Rijil Thomas, Ling Lieng Hsi, Soh Cheong Boon, Erry Gunawan
In order to assist cardiac diagnosis by phonocardiography, the automated identification of fundamental heart sounds for heart beat segmentation in a cardiac cycle plays a significant role in signal processing. Recent advancements in signal processing have also shown the potential of multifractality in biomedical applications. Hence, in this paper, the multifractal property of heart sounds is utilized to identify first and second heart sounds. The root mean square (rms) fluctuation used to obtain multifractal/singularity spectrum is used to decompose the heart sound into its own fractally-important components in time domain along with simultaneous Gaussianity test to filter out fundamental components...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227924/a-hybrid-hardware-and-software-approach-for-cancelling-stimulus-artifacts-during-same-electrode-neural-stimulation-and-recording
#8
Stanislav Culaclii, Brian Kim, Yi-Kai Lo, Wentai Liu, Stanislav Culaclii, Brian Kim, Yi-Kai Lo, Wentai Liu, Brian Kim, Yi-Kai Lo, Wentai Liu, Stanislav Culaclii
Recovering neural responses from electrode recordings is fundamental for understanding the dynamics of neural networks. This effort is often obscured by stimulus artifacts in the recordings, which result from stimuli injected into the electrode-tissue interface. Stimulus artifacts, which can be orders of magnitude larger than the neural responses of interest, can mask short-latency evoked responses. Furthermore, simultaneous neural stimulation and recording on the same electrode generates artifacts with larger amplitudes compared to a separate electrode setup, which inevitably overwhelm the amplifier operation and cause unrecoverable neural signal loss...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227913/online-learning-of-gait-models-for-calculation-of-gait-parameters
#9
Jamie L S Waugh, Anton Trinh, Ryan R Mohammed, William E McIlroy, Dana Kulic, Jamie L S Waugh, Anton Trinh, Ryan R Mohammed, William E McIlroy, Dana Kulic, Jamie L S Waugh, Dana Kulic, Anton Trinh, William E McIlroy, Ryan R Mohammed
This paper proposes a novel approach for gait analysis from wearable sensing, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter and intra-personal variability by creating an individualized model. Once the algorithm has converged to the input signal, key gait events can be identified relative to the estimated gait phase; these events can then be used to calculate gait parameters...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227886/development-of-an-open-source-cosimulation-method-of-the-knee
#10
Anne Schmitz, Davide Piovesan, Anne Schmitz, Davide Piovesan, Anne Schmitz, Davide Piovesan
Rigid body dynamics and soft tissue loads are solved simultaneously in a cosimulation framework to couple musculoskeletal dynamics and tissue mechanics. The goal of this work was to implement a validated, open-source cosimulation framework of the knee to determine how this coupling affects computed cartilage loads. The kinematic knee joint of a generic whole body model in the open-source software OpenSim was replaced by a previously developed discrete element knee model that consisted of a six degree of freedom (dof) tibiofemoral joint and one dof patellofemoral joint...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227880/toward-personalized-and-context-aware-prompting-for-smartphone-based-intervention
#11
Ramin Fallahzadeh, Samaneh Aminikhanghahi, Ashley Nichole Gibson, Diane J Cook, Ramin Fallahzadeh, Samaneh Aminikhanghahi, Ashley Nichole Gibson, Diane J Cook, Ashley Nichole Gibson, Samaneh Aminikhanghahi, Ramin Fallahzadeh, Diane J Cook
Intervention strategies can help individuals with cognitive impairment to increase adherence to instructions, independence, and activity engagement and reduce errors on everyday instrumental activities of daily living (IADLs) and caregiver burden. However, to be effective, intervention prompts should be given at a time that does not interrupt other important user activities and is more convenient. In this paper, we propose an intelligent personalized intervention system for smartphones. In our approach, we use context and activity awareness to time prompts when they will most likely be viewed and used...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227872/detection-of-different-types-of-noise-in-lung-sounds
#12
A Leal, R Couceiro, I Chouvarda, N Maglaveras, J Henriques, R Paiva, P Carvalho, C Teixeira, A Leal, R Couceiro, I Chouvarda, N Maglaveras, J Henriques, R Paiva, P Carvalho, C Teixeira, R Couceiro, P Carvalho, C Teixeira, A Leal, R Paiva, J Henriques, N Maglaveras, I Chouvarda
Lung sound signal processing has proven to be a great improvement to the traditional acoustic interpretation of lung sounds. However, that analysis can be seriously hindered by the presence of different types of noise originated in the acquisition environment or caused by physiological processes. Consequently, the diagnostic accuracy of pulmonary diseases can be severely affected, especially if the implementation of telemonitoring systems is considered. The present study is focused on the implementation of an algorithm able to identify noisy periods, either voluntarily (vocalizations, chest movement and background voices) or involuntarily produced during acquisitions of lung sounds...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227870/identifying-disease-network-perturbations-through-regression-on-gene-expression-and-pathway-topology-analysis
#13
Georgios N Dimitrakopoulos, Panos Balomenos, Aristidis G Vrahatis, Kyriakos Sgarbas, Anastasios Bezerianos, Georgios N Dimitrakopoulos, Panos Balomenos, Aristidis G Vrahatis, Kyriakos Sgarbas, Anastasios Bezerianos, Kyriakos Sgarbas, Panos Balomenos, Georgios N Dimitrakopoulos, Anastasios Bezerianos, Aristidis G Vrahatis
In Systems Biology, network-based approaches have been extensively used to effectively study complex diseases. An important challenge is the detection of network perturbations which disrupt regular biological functions as a result of a disease. In this regard, we introduce a network based pathway analysis method which isolates casual interactions with significant regulatory roles within diseased-perturbed pathways. Specifically, we use gene expression data with Random Forest regression models to assess the interactivity strengths of genes within disease-perturbed networks, using KEGG pathway maps as a source of prior-knowledge pertaining to pathway topology...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227864/automatic-segmentation-of-multimodal-brain-tumor-images-based-on-classification-of-super-voxels
#14
M Kadkhodaei, S Samavi, N Karimi, H Mohaghegh, S M R Soroushmehr, K Ward, A All, K Najarian, M Kadkhodaei, S Samavi, N Karimi, H Mohaghegh, S M R Soroushmehr, K Ward, A All, K Najarian, K Ward, S M R Soroushmehr, A All, S Samavi, M Kadkhodaei, H Mohaghegh, K Najarian, N Karimi
Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227858/compensation-of-pulsation-artifacts-during-optical-imaging-with-and-without-cranial-chamber
#15
Alejandro Romero-Santiago, Philipp Flotho, Karsten Schwerdtfeger, Jacek Szczygielski, Matthias Hulser, Lars Haab, Daniel J Strauss, Alejandro Romero-Santiago, Philipp Flotho, Karsten Schwerdtfeger, Jacek Szczygielski, Matthias Hulser, Lars Haab, Daniel J Strauss, Daniel J Strauss, Alejandro Romero-Santiago, Jacek Szczygielski, Karsten Schwerdtfeger, Philipp Flotho, Lars Haab, Matthias Hulser
Functional Optical Imaging (OI) through the opened skull forms a group of Neuroimaging techniques characterized by a high temporal and spatial resolution on a meso-to macroscopic scale. State of the art OI experiments are generally difficult to execute, with a very timely surgical preparation preceding the experiment, that requires a skilled surgeon to mount a sealed imaging chamber onto the skull. The chamber reduces brain pulsation artifacts and swelling of the brain through movement restriction. In this work, we present preliminary results of a novel approach that does not rely on the usage of an imaging chamber with the goal to facilitate heavily the surgical animal preparation and to allow straightforward joint Electroencephalography - Optical Imaging recordings in the future...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227846/time-frequency-joint-coding-method-for-boosting-information-transfer-rate-in-an-ssvep-based-bci-system
#16
Ke Lin, Yijun Wang, Xiaorong Gao, Ke Lin, Yijun Wang, Xiaorong Gao, Yijun Wang, Xiaorong Gao, Ke Lin
Steady-State Visual Evoked Potential (SSVEP) based Brain-Computer Interface (BCI) system is an important BCI modality. It has advantages such as ease of use, little training and high Information Transfer Rate (ITR). Traditional SSVEP based BCI systems are based on the Frequency Division Multiple Access (FDMA) approach in telecommunications. Recently, Time Division Multiple Access (TDMA) was also introduced to SSVEP based BCI to enhance the system performance. This study designed a new time-frequency joint coding method to utilize the information coding from both time and frequency domains...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227845/high-performance-wearable-two-channel-hybrid-bci-system-with-eye-closure-assist
#17
Yubing Jiang, Hyeonseok Lee, Gang Li, Wan-Young Chung, Yubing Jiang, Hyeonseok Lee, Gang Li, Wan-Young Chung, Wan-Young Chung, Gang Li, Hyeonseok Lee, Yubing Jiang
Generally, eye closure (EC) and eye opening (EO)-based alpha blocking has widely recognized advantages, such as being easy to use, requiring little user training, while motor imagery (MI) is difficult for some users to have concrete feelings. This study presents a hybrid brain-computer interface (BCI) combining MI and EC strategies - such an approach aims to overcome some disadvantages of MI-based BCI, improve the performance and universality of the BCI. The EC/EO is employed to control the machine to switch in different states including forward, stop, changing direction motions, while the MI is used to control the machine to turn left or right for 90° by imagining the hands grasp motions when the system is switched into "changing direction" state...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227833/a-comparative-study-of-approaches-to-compute-the-field-distribution-of-deep-brain-stimulation-in-the-hemiparkinson-rat-model
#18
Andrea Bohme, Ursula van Rienen, Andrea Bohme, Ursula van Rienen, Ursula Van Rienen, Andrea Bohme
Computational modeling of the stimulating field distribution during Deep Brain Stimulation provides an opportunity to advance our knowledge of this neurosurgical therapy for Parkinson's disease. There exist several approaches to model the target region for Deep Brain Stimulation in Hemi-parkinson Rats with volume conductor models. We have described and compared the normalized mapping approach as well as the modeling with three-dimensional structures, which include curvilinear coordinates to assure an anatomically realistic conductivity tensor orientation...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227830/an-adaptive-model-approach-for-quantitative-wrist-rigidity-evaluation-during-deep-brain-stimulation-surgery
#19
Sofia Assis, Pedro Costa, Maria Jose Rosas, Rui Vaz, Joao Paulo Silva Cunha, Sofia Assis, Pedro Costa, Maria Jose Rosas, Rui Vaz, Joao Paulo Silva Cunha, Maria Jose Rosas, Joao Paulo Silva Cunha, Sofia Assis, Rui Vaz, Pedro Costa
Intraoperative evaluation of the efficacy of Deep Brain Stimulation includes evaluation of the effect on rigidity. A subjective semi-quantitative scale is used, dependent on the examiner perception and experience. A system was proposed previously, aiming to tackle this subjectivity, using quantitative data and providing real-time feedback of the computed rigidity reduction, hence supporting the physician decision. This system comprised of a gyroscope-based motion sensor in a textile band, placed in the patients hand, which communicated its measurements to a laptop...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227820/evaluating-respiratory-muscle-activity-using-a-wireless-sensor-platform
#20
Luis Estrada, Abel Torres, Leonardo Sarlabous, Raimon Jane, Luis Estrada, Abel Torres, Leonardo Sarlabous, Raimon Jane, Raimon Jane, Luis Estrada, Leonardo Sarlabous, Abel Torres
Wireless sensors are an emerging technology that allows to assist physicians in the monitoring of patients health status. This approach can be used for the non-invasive recording of the electrical respiratory muscle activity of the diaphragm (EMGdi). In this work, we acquired the EMGdi signal of a healthy subject performing an inspiratory load test. To this end, the EMGdi activity was captured from a single channel of electromyography using a wireless platform which was compared with the EMGdi and the inspiratory mouth pressure (Pmouth) recorded with a conventional lab equipment...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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