Online Decoding of Hidden Markov Models for Gait Event Detection Using Foot-Mounted Gyroscopes
Robustness, Specificity and Reliability of an In-ear Pulse Oximetric Sensor in Surgical Patients
A Multiscale Optimization Approach to Detect Exudates in the Macula
Supervised Hierarchical Bayesian Model-Based Electomyographic Control and Analysis
We present an approach to the online implementation of a gait event detector based on machine learning algorithms. An Android smartphone was used as the interface with the sensing node. Gait events were detected using a uniaxial gyro that measured the foot instep angular velocity in the sagittal plane to feed a four-state left–right hidden Markov model (HMM). The four gait events were foot strike, flat foot (FF), heel off (HO), and toe off. The accuracy ranged, on average, from 45 ms (early detection, FF) to 35 ms (late detection, HO); the latency of detection was less than 100 ms for all gait events but the HO, where the probability that it was greater than 100 ms was 25%…
A novel in-ear pulse oximetric sensor was presented in a prior work which is deemed to be independent from perfusion fluctuations due to its proximity to the trunk. Having demonstrated the feasibility of in-ear SpO2measurement with reliable specificity in a laboratory setting, we now report results from a study on in-ear SpO2 in a clinical setting. For this, trials were performed in 29 adult patients undergoing surgery. SpO2 shows good accordance with SaO2, a high level of comparability with the reference pulse oximeters, and was significantly improved by introducing a new algorithm for artifact reduction.
This work presents a computer-aided detection algorithm to identify exudates near the fovea, and thus improve the classification of CSME. Our method is based on a generalized optimization scheme of image decompositions. We combine information from different frequency scales through iterative thresholding bound selection to extract possible candidates. For each candidate region, we extract color, shape, and texture features that are used as inputs to a partial least squares (PLS) classifier. Our classification system achieves an area under the ROC curve (AUC) of 0.96 for detection of exudates. Given the optimization process and flexibility of the implementation, this methodology could be extended to the detection of different types of lesions that occurs in other eye diseases.
This work suggests a supervised hierarchical Bayesian model for surface electromyography (sEMG) signal based motion classification and its strategy analysis. To validate the model, nine-class classification using four sEMG sensors on the limb motions is tested. The model performances are evaluated with relatively high and low activation levels, generalized classification across subjects and online classification. The model based on LNSs to capture various motions is assessed with respect to activation levels, individual subjects and transition during online classification. The proposed model can reflect various muscular activation patterns across subjects as well as represent subject-specific characteristics of muscular activities…
About This Journal
IEEE Journal of Biomedical and Health Informatics (J-BHI) publishes original papers describing recent advances in the field of biomedical and health informatics where information and communication technologies intersect with health, healthcare, life sciences and biomedicine. Papers must contain original content in theoretical analysis, methods, technical development, and/or novel clinical applications of information systems.
Retitled from the IEEE Transactions on Information Technology in Biomedicine (T-ITB) in 2013, the J-BHI is one of the leading journals in computer science and information systems with a strong interdisciplinary focus and biomedical and health application emphasis. Topics covered by J-BHI include, but are not limited to: acquisition, transmission, storage, retrieval, management, processing and analysis of biomedical and health information; applications of information and communication technologies to the practice of healthcare, personal well-being, preventive care and early diagnosis of diseases, and discovery of new therapies and patient specific treatment protocols; and integration of electronic medical and health records, methods of longitudinal data analysis, data mining and knowledge discovery tools.
Manuscripts may deal with these applications and their integration, such as clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body senor networks, informatics in biological and physiological systems, personalized and pervasive health technologies (telemedicine, u-, p-, m- and e-Health) for public health, home healthcare and wellness management. Topics related to integration include interoperability, protocol-based patient care, evidence-based medicine, and methods of secure patient data.
Papers published by J-BHI are typically organised under section headings of Bioinformatics, Imaging Informatics, Sensor Informatics, Medical Informatics, and Public Health Informatics. These are complemented by managed special issues/sections covering topics that are of strategic importance to the journal, coordinated by guest editors who are leading experts in these fields. We particularly encourage large cohort studies with clearly demonstrated clinical translational values supplemented by online data sets or algorithms that can be shared by the research community.