Featured Article

Epsilon-Tube Filtering: Reduction of High-Amplitude Motion Artifacts from Impedance Plethysmography Signal

Epsilon-Tube Filtering: Reduction of High-Amplitude Motion Artifacts from Impedance Plethysmography Signal

This paper proposes a novel motion artifact reduction method that adopts Vapnik’s idea of epsilon-tube to restrict the amplitude of the filtered signal. Additionally, it uses Stockwell Transform to maximize the regularity of the filtered signal within the tube. The results indicate that epsilon-tube filtering has significantly higher accuracy compared with the currently existing motion artifact reduction methods. The combination of a portable Impedance plethysmography (IP) device and the proposed epsilon-tube filter will allow the physicians to more accurately and adequately monitor patient respiratory activity within the home setting and without the use of facemasks or nasal cannulas, thereby not imposing any restrictions on patient’s airways.

See All Highlights

Vein Visualization Using a Smart Phone with Multispectral Wiener Estimation for Point-of-Care Applications

Vein Visualization Using a Smart Phone with Multispectral Wiener Estimation for Point-of-Care Applications

In this article, a new vein visualization method based on multispectral Wiener estimation is proposed and its real-time implementation on a smart phone is presented. To evaluate the performance of the proposed method, an experiment was conducted using a color calibration chart and an average root mean square error of 12.0% was obtained. In addition, from an in vivo subcutaneous vein imaging experiment, the veins at various sites were successfully localized using the reconstructed multispectral images and these results were confirmed by ultrasound B-mode and color Doppler images.

See All Highlights

Integrative Clustering by Non-Negative Matrix Factorization Can Reveal Coherent Functional Groups from Gene Profile Data

Integrative Clustering by Non-Negative Matrix Factorization Can Reveal Coherent Functional Groups from Gene Profile Data

In this paper we propose a technique that develops separate gene clusters and fuses them by means of non-negative matrix factorization. Gene clusters are inferred from gene networks that are built from each of available data sources by applying various estimates of gene profile similarity. We use gene profile data on the budding yeast S. cerevisiae to demonstrate that this approach can successfully integrate heterogeneous data sets and yield high-quality clusters that could otherwise not be inferred by simply merging the gene profiles prior to clustering.

See All Highlights

Two-Phase Greedy Pursuit Algorithm for Automatic Detection and Characterization of Transient Calcium Signaling

Two-Phase Greedy Pursuit Algorithm for Automatic Detection and Characterization of Transient Calcium Signaling

This paper presents a novel two-phase greedy pursuit (TPGP) algorithm for automatic detection and characterization of calcium sparks. In Phase I, a coarse-grained search is conducted across the whole image to identify the predominant sparks. In Phase II, adaptive basis function model is developed for the fine-grained representation of detected sparks. Experimental results show that TPGP algorithms yield better performances than previous hard-thresholding approaches in terms of both sensitivities and positive predicted values. The present research provides the community a more robust tool for the automatic detection and characterization of transient calcium signaling.

See All Highlights

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.

EMB PubMed



IEEE J-BHI Upcoming Special Issues and Supported Conferences