IEEE Open Journal of

Engineering in Medicine and Biology

The IEEE Open Journal of Engineering in Medicine and Biology covers the development and application of engineering concepts and methods to biology, medicine and health sciences to provide effective solutions to biological, medical and healthcare problems.

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Editor-in-Chief

Paolo Bonato

Paolo Bonato, Ph.D.

Paolo Bonato, Ph.D., serves as Director of the Motion Analysis Laboratory at Spaulding Rehabilitation Hospital, Boston MA. He is an Associate Professor in the Department of Physical Medicine and Rehabilitation at Harvard Medical School, an Adjunct Professor of Biomedical Engineering at the MGH Institute of Health Professions, an Associate Faculty Member at the Wyss Institute for Biologically Inspired Engineering, and an Adjunct Associate Professor at Boston University College of Health & Rehabilitation Sciences. He has held Adjunct Faculty positions at MIT, the University of Ireland Galway, and the University of Melbourne. His research work is focused on the development of rehabilitation technologies with special emphasis on wearable technology and robotics.

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Highlights

Updates

The IEEE Open Journal of Engineering in Medicine and Biology

Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals

Goal: To achieve high-quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies.

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Artificial Intelligence for Detecting COVID-19 with the Aid of Human Cough, Breathing and Speech Signals: Scoping Review 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Artificial Intelligence for Detecting COVID-19 with the Aid of Human Cough, Breathing and Speech Signals: Scoping Review
Author(s): Mouzzam Husain, Andrew Simpkin, Claire Gibbons, Tanya Talkar, Daniel M. Low, Paolo Bonato, Satrajit Ghosh, Thomas Quatieri, Derek O'Keeffe

Background: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data for preliminary screening may help alleviate these issues.

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Bayesian Inference-Based Gaussian Mixture Models With Optimal Components Estimation Towards Large-Scale Synthetic Data Generation for In Silico Clinical Trials 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Bayesian Inference-Based Gaussian Mixture Models With Optimal Components Estimation Towards Large-Scale Synthetic Data Generation for In Silico Clinical Trials

Goal: To develop a computationally efficient and unbiased synthetic data generator for large-scale in silico clinical trials (CTs).

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Machine Learning-Based Prediction of Impulse Control Disorders in Parkinson’s Disease From Clinical and Genetic Data 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Machine Learning-Based Prediction of Impulse Control Disorders in Parkinson’s Disease From Clinical and Genetic Data

Goal: Impulse control disorders (ICDs) are frequent non-motor symptoms occurring during the course of Parkinson’s disease (PD). The objective of this study was to estimate the predictability of the future occurrence of these disorders using longitudinal data, the first study using cross-validation and replication in an independent cohort.

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Assessment of CAR-T Cell-Mediated Cytotoxicity in 3D Microfluidic Cancer Co-Culture Models for Combination Therapy 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Assessment of CAR-T Cell-Mediated Cytotoxicity in 3D Microfluidic Cancer Co-Culture Models for Combination Therapy

Chimeric antigen receptor (CAR)-T cell therapy is efficacious against many haematological malignancies, but challenges remain when using this cellular immunotherapy for treating solid tumours.

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SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification

The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated well with the help of EEG signals.

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