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IEEE Transactions on Bio-medical Engineering

Xi Cai, Guang Han, Xin Song, Jinkuan Wang
OBJECTIVE: Single-camera-based gait monitoring is unobtrusive, inexpensive and easy-to-use to monitor daily gait of seniors in their homes. However, most studies require subjects to walk perpendicularly to camera's optical axis or along some specified routes, which limits its application in elderly home monitoring. To build unconstrained monitoring environments, we propose a method to measure step length symmetry ratio (a useful gait parameter representing gait symmetry without significant relationship with age) from unconstrained straight walking using a single camera, without strict restrictions on walking directions or routes...
January 16, 2017: IEEE Transactions on Bio-medical Engineering
Temiloluwa Prioleau, Elliot Moore, Maysam Ghovanloo
The threat of obesity, diabetes, anorexia and bulimia in our society today has motivated extensive research on dietary monitoring. Standard self-report methods such as 24-hour recall and food frequency questionnaires are expensive, burdensome and unrealiable to handle the growing health crisis. Long-term activity monitoring in daily living is a promising approach to provide individuals with quantitative feedback that can encourage healthier habits. Although several studies have attempted automating dietary monitoring using wearable, hand-held, smartobject, and environmental systems, it remains an open research problem...
January 16, 2017: IEEE Transactions on Bio-medical Engineering
Chiara Toffanin, Roberto Visentin, Mirko Messori, Federico Di Palma, Lalo Magni, Claudio Cobelli
: Contemporary and future outpatient long-term artificial pancreas (AP) studies need to cope with the well-known large intra- and inter-day glucose variability occurring in type 1 diabetic (T1D) subjects. Here we propose an adaptive Model Predictive Control (MPC) strategy to account for it and test it in silico. METHODS: A Run-to-Run (R2R) approach adapts the subcutaneous basal insulin delivery during the night and the carbohydrate-to-insulin ratio (CR) during the day, based on some performance indices calculated from subcutaneous continuous glucose sensor data...
January 11, 2017: IEEE Transactions on Bio-medical Engineering
Abhijit Bhattacharyya, Ram Bilas Pachori
OBJECTIVE: This paper investigates the multivariate oscillatory nature of electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure detection. METHODS: The empirical wavelet transform (EWT) has been explored for the multivariate signals in order to determine the joint instantaneous amplitudes and frequencies in signal adaptive frequency scales. The proposed multivariate extension of EWT has been studied on multivariate multi-component synthetic signal, as well as on multivariate EEG signals of CHB-MIT scalp EEG database...
January 9, 2017: IEEE Transactions on Bio-medical Engineering
Hongming Xu, Cheng Lu, Richard Berendt, Naresh Jha, Mrinal Mandal
In the diagnosis of various cancers by analyzing histological images, automatic nuclear segmentation is an important step. However, nuclear segmentation is a difficult problem because of overlapping nuclei, inhomogeneous staining, and presence of noisy pixels and other tissue components. In this paper, we present an automatic technique for nuclear segmentation in skin histological images. The proposed technique first applies a bank of generalized Laplacian of Gaussian (gLoG) kernels to detect nuclear seeds...
January 9, 2017: IEEE Transactions on Bio-medical Engineering
Johan Wahlstrom, Isaac Skog, Peter Handel, Farzad Khosrow-Khavar, Kouhyar Tavakolian, Phyllis K Stein, Arye Nehorai
We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogrambased heart rate and heart rate variability estimation Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and 9 [ms], respectively...
January 9, 2017: IEEE Transactions on Bio-medical Engineering
Rui Zhang, Qihong Wang, Kai Li, Shenghong He, Si Qin, Zhenghui Feng, Yang Chen, Pingxia Song, Tingyan Yang, Yuandong Zhang, Zhuliang Yu, Yaohua Hu, Ming Shao, Yuanqing Li
: This study proposes an event-related potential (ERP) BCI-based environmental control system that integrates household electrical appliances, a nursing bed, and an intelligent wheelchair to provide daily assistance to paralyzed patients with severe spinal cord injuries (SCIs). METHODS: An asynchronous mode is used to switch the environmental control system on or off or to select a device (e.g., a TV) for achieving selfpaced control. In the asynchronous mode, we introduce several pseudo-keys and a verification mechanism to effectively reduce the false operation rate...
January 9, 2017: IEEE Transactions on Bio-medical Engineering
Narges Ahmidi, Lingling Tao, Shahin Sefati, Yixin Gao, Colin Lea, Benjamin Bejar, Luca Zappella, Sanjeev Khudanpur, Rene Vidal, Gregory D Hager
OBJECTIVE: State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging. METHODS: In this paper, we address two major problems for surgical data analysis: (1) lack of uniform shared datasets and benchmarks and (2) lack of consistent validation processes...
January 4, 2017: IEEE Transactions on Bio-medical Engineering
Ferdinando Auricchio, Anna Ferrara, Ettore Lanzarone, Simone Morganti, Pasquale Totaro
GOAL: Ascending aorta aneurysms represent a severe life-threatening condition associated with asymptomatic risk of rupture. Prediction of aneurysm evolution and rupture is one of the hottest investigation topics in cardiovascular science, and the decision on when and whether to surgically operate is still an open question. We propose an approach for estimating the patient-specific ultimate mechanical properties and stress-stretch characteristics based on non-invasive data. METHODS: As for the characteristics, we consider a non-linear constitutive model of the aortic wall and assume patient-specific model coefficients...
December 29, 2016: IEEE Transactions on Bio-medical Engineering
Saurabh Dargar, Ali Akyildiz, Suvranu De
In this paper we report the development of a technique to characterize layer-specific nonlinear material properties of soft tissue in situ with the potential for in vivo testing. A Soft Tissue Elastography Robotic Arm (STiERA) system comprising of a robotically manipulated 30 MHz high-resolution ultrasound probe, a custom designed compression head and load cells has been developed to perform compression ultrasound imaging on the target tissue and measure reaction forces. A multi-layer finite element model is iteratively optimized to identify the material coefficients of each layer...
December 23, 2016: IEEE Transactions on Bio-medical Engineering
Szu-Wei Fu, Pei-Chun Li, Ying-Hui Lai, Cheng-Chien Yang, Li-Chun Hsieh, Yu Tsao
OBJECTIVE: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: (1) the amount of training data may be limited (because speaking for a long time is usually difficult for post-operative patients); (2) rapid conversion is desirable (for better communication)...
December 23, 2016: IEEE Transactions on Bio-medical Engineering
Sina Miran, Patrick Purdon, Emery Brown, Behtash Babadi
OBJECTIVE: Characterizing the spectral properties of neuronal responses is an important problem in computational neuroscience, as it provides insight into the spectral organization of the underlying functional neural processes. Although spectral analysis techniques are widely used in the analysis of noninvasive neural recordings such as EEG, their application to spiking data is limited due to the binary and non-linear nature of neuronal spiking. In this paper, we address the problem of estimating the power spectral density of the neural covariate driving the spiking statistics of a neuronal population from binary observations...
December 22, 2016: IEEE Transactions on Bio-medical Engineering
Sinan Hersek, Hakan Toreyin, Caitlin N Teague, Mindy L Millard-Stafford, Hyeon-Ki Jeong, Miheer M Bavare, Paul Wolkoff, Michael N Sawka, Omer T Inan
: We designed and validated a portable electrical bioimpedance (EBI) system to quantify knee joint health. METHODS: Five separate experiments were performed to demonstrate the: (1) ability of the EBI system to assess knee injury and recovery; (2) inter-day variability of knee EBI measurements; (3) sensitivity of the system to small changes in interstitial fluid volume; (4) reducing the error of EBI measurements using acceleration signals; (5) use of the system with dry electrodes integrated to a wearable knee wrap...
December 22, 2016: IEEE Transactions on Bio-medical Engineering
Yinfeng Fang, Dalin Zhou, Kairu Li, Honghai Liu
It is evident that user training significantly affects performance of pattern-recognition based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent EMG patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualised online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction...
December 21, 2016: IEEE Transactions on Bio-medical Engineering
Flurin Pfiffner, Lukas Prochazka, Dominik Peus, Ivo Dobrev, Adrian Dalbert, Jae Hoon Sim, Rahel Kesterke, Joris Walraevens, Francesca Paris, Christof Roosli, Dominik Obrist, A M Huber
GOAL: Intracochlear sound pressure (ICSP) measurements are limited by the small dimensions of the human inner ear and the requirements imposed by the liquid medium. A robust intracochlear acoustic receiver (ICAR) for repeated use with a simple data acquisition system that provides the required high sensitivity and small dimensions does not yet exist. The work described in this report aims to fill this gap and presents a new MEMS condenser microphone (CMIC) based ICAR concept suitable for ICSP measurements in human temporal bones...
December 16, 2016: IEEE Transactions on Bio-medical Engineering
Yukimi Tanaka, Takuya Isomura, Kenta Shimba, Kiyoshi Kotani, Yasuhiko Jimbo
OBJECTIVE: Adult neurogenesis in the hippocampus facilitates cognitive functions such as pattern separation in mammals. However, it remains unclear how newborn neurons mediate changes in neural networks to enhance pattern separation ability. Here, we developed an in vitro model of adult neurogenesis using rat hippocampal cultures in order to investigate whether newborn neurons can be directly incorporated into neural networks related to pattern separation to produce functional improvements...
December 14, 2016: IEEE Transactions on Bio-medical Engineering
Saman Sargolzaei, Hassan Elahi, Alan Sokoloff, Maysam Ghovanloo
We have developed an unobtrusive magnetic-acoustic fluid intake monitoring (MAFIM) system using a conventional stainless-steel roller-ball nipple to measure licking and drinking behavior in animals. Movements of a small permanent magnetic tracer attached to stainless-steel roller balls that operate as a tongue-actuated valve are sensed by a pair of 3-axial magneto-meters, and transformed into a time series indicating the status of the ball (up or down), using a Gaussian mixture model based data driven classifier...
December 12, 2016: IEEE Transactions on Bio-medical Engineering
Clement Baumgarten, Yulong Zhao, Paul Sauleau, Cecile Malrain, Pierre Jannin, Claire Haegelen
OBJECTIVE: Subthalamic nucleus deep brain stimulation (STN DBS) is limited by the occurrence of pyramidal tract side effect (PTSE) induced by electrical activation of the pyramidal tract. Predictive models are needed to assist the surgeon during the electrode trajectory pre-planning. The objective of the study was to compare two methods of PTSE prediction based on clinical assessment of PTSE induced by STN DBS in patients with Parkinson's disease. METHODS: Two clinicians assessed PTSE postoperatively in 20 patients implanted for at least 3 months in the STN...
December 9, 2016: IEEE Transactions on Bio-medical Engineering
Yangjin Kim, Hyejin Jeon, Hans Othmer
Glioblastoma multiforme (GBM) is one of the deadliest human cancers and is characterized by tumor cells that hijack immune system cells in a deadly symbiotic relationship. Microglia and glioma-infiltrating-macrophages (GIMs), which in principle should mount an immune response to the tumor, are subverted by tumor cells to facilitate growth in several ways. In this study we seek to understand the interactions between the tumor cells and the microglia that enhance tumor growth, and for this purpose we develop a mathematical and computational model that involves reaction-diffusion equations for the important components in the interaction...
December 8, 2016: IEEE Transactions on Bio-medical Engineering
Shih-Yun Lin, Ying-Chih Lai, Chi-Chun Hsia, Pei-Fang Su, Chih-Han Chang
OBJECTIVE: This study aimed to verify and compare the accuracy of energy expenditure (EE) prediction models using shoe-based motion detectors with embedded accelerometers. METHODS: Three physical activity (PA) data sets (unclassified, recognition, and intensity segmentation) were used to develop three prediction models. A multiple classification flow and these models were used to estimate EE. The "unclassified" data set was defined as the data without PA recognition, the "recognition" as the data classified with PA recognition, and the "intensity segmentation" as the data with intensity segmentation...
December 7, 2016: IEEE Transactions on Bio-medical Engineering
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