This paper provides a systematic review of current techniques for quantitative gait analysis and proposes key metrics for for qualifying gait features extracted from wearable sensors. It aims to highlight key advances in this rapidly evolving research field and outline potential future directions for both research and clinical applications
This special issue of the IEEE JOURNAL ON BIOMEDICAL AND HEALTH INFORMATICS (JBHI) presents full articles of the work presented at the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), which was held in Milano, Italy, from August 25–29.
This special issue includes four papers that expand on work presented at the 12th BSN Conference, held in Cambridge, MA, USA, in June 2015, which included 85 oral and poster presentations. All four papers went through the rigorous review process of the IEEE Journal of Biomedical and Health Informatics (J-BHI).
The aim of this special section is to present an overview of recent advances in sensing technologies, monitoring of patients,
security and privacy of data transfer, provision of collaborative environments, data gathering and analysis from various sources, and predictive models, which all finally target the best strategy for patient monitoring and treatment.
In this work, we investigate the feasibility of predicting abnormal spirometry using measurements of the partial flow-volume curve generated during a voluntary cough. A variety of features were extracted from the cough airflow signals and fed to a support vector machine (SVM) classifier. Airflow features and SVM parameters were selected using a genetic algorithm, and accuracy assessed using repeated double cross-validation. This technique is easily performed and does not require expensive equipment. Based on these characteristics, and the results of this study, it has potential for becoming especially useful for mass screening or for testing subjects who are not able to perform conventional pulmonary function testing.
Dynamic Bayesian networks (DBNs) are temporal probabilistic graphical models that model temporal events and their causal and temporal dependencies. Temporal abstraction (TA) is a knowledge-based process that abstracts raw temporal data into higher level interval-based concepts. In this paper, we present an extended DBN model that integrates TA methods with DBNs applied for prognosis of the risk for coronary heart disease. More specifically, we demonstrate the derivation of TAs from data, which are used for building the network structure. We use machine learning algorithms to learn the parameters of the model through data. We apply the extended model to a longitudinal medical dataset and compare its performance to the performance of a DBN implemented without TAs. The results we obtain demonstrate the predictive accuracy of our model and the effectiveness of our proposed approach.
In this paper, we studied a dataset composed of 159 anatomical MR images pooled from three different scanners, including 75 ASD patients and 84 healthy controls. We quantitatively assessed the effects of the age, pathology and scanner factors on cortical thickness measurements. We observe that the effect size associated to the scanner factor is larger than the effect of pathology in almost every cortical region. Moreover, while the effect of age is consistent across scanners, the interaction between the age and scanner factors is important and significant in some specific cortical areas. Our results allow for a better understanding of the frequent discrepancies observed in the literature of ASD. We conclude that maximizing the consistency of image characteristics across scanners is mandatory for multi-site quantitative MRI studies in the context of neuro-developmental pathologies such as ASD.
A novel modeling approach of tumor growth and response to radiotherapy is presented and tested on cervical cancer patient data. A patient-specific mathematical model is developed to predict the evolution of cancer volume at a macroscopic scale, during fractionated external radiotherapy. The model provides estimates of the re-growth of the active portion of the tumor along with their impairment due to irradiation using the linear-quadratic model. At the same time, it accounts for the necrotic portion dynamics by means of an exponential decay to mimic the dead-cell reabsorption.
In this paper, we propose a framework for automatically segmenting the lumen boundary in IV-OCT with minimum time‐complexity. The framework uses OCT imaging physics based graph representation of signals and random walks image segmentation approaches. First, each OCT frame is modelled as a 4-connected graph and edge weights are assigned incorporating OCT signal attenuation physics models. Second, optical backscattering maxima is tracked along each A-scan of OCT and is subsequently refined using global gray‐level statistics and used for initializing seeds. This automates the seed selection process thus avoiding manual interaction. Finally, lumen boundary is segmented using the random walks image segmentation using the initialized seeds.
A new framework based on variational coupled level set has been developed for the extraction of new born skull including fontanels and sutures from CT images. The proposed method utilizes hard tissue contrast in CT image, prior information of head shape integrated in level sets initialization, and a predefined constraint to impose surface reconstruction properties. The proposed method was evaluated using eighteen neonatal CT images. The segmentation results achieved by the suggested method have been compared with manual segmentations.