IEEE Transactions on Biomedical Engineering

Featured Articles
Evaluation of a Wireless Tongue Tracking System on the Identification of Phoneme Landmarks
Nordine Sebkhi, Nina Monique Santus, Arpan Bhavsar, Shayan Siahpoushan, Omer Inan
Visualizing tongue movement in real-time has the potential to improve therapy outcome for millions of people worldwide living with a speech sound disorder because the positioning of the tongue is crucial in the production of many phonemes to be intelligible. Our team has developed a wearable 3D tongue tracking system based on a wireless magnetic localization method. To evaluate its tracking accuracy, 2,500 tongue trajectories were recorded from 10 subjects uttering 25 phonemes. The results show that our system is capable of tracking tongue motion with positional errors in the order of few millimeters (median: 3.9 mm, Q3: 5.8 mm)... Read more
Posted on 31 MAR 2021
Featured Articles
Fit to Burst: Toward Noninvasive Estimation of Achilles Tendon Load Using Burst Vibrations
Nicholas B. Bolus, Hyeon Ki Jeong, Bradley M. Blaho, Mohsen Safaei, Aaron J. Young, Omer T. Inan
In this study, we present a novel method of noninvasively estimating mechanical load in the Achilles tendon using burst vibrations. These vibrations, produced by a small vibration motor on the skin superficial to the tendon, are sensed by a skin-mounted accelerometer, which measures the tendon’s response to burst excitation under varying tensile load. Characteristic changes in the burst response profile as a function of tendon tension are observed and used as inputs to an ML model, which yields accurate (R2 = 0.85) estimates of ankle loading during gait. Preliminary results of a fully wearable ankle load monitor are also presented... Read more
Posted on 15 JAN 2021
Featured Articles
A Machine Learning Enabled Wireless Intracranial Brain Deformation Sensing System
Sayemul Islam, Vinit Shah, Sai Teja Reddy Gidde, Parsaoran Hutapea, Seung Hyun Song, Joseph Picone, Albert Kim
A leading cause of traumatic brain injury (TBI) is intracranial brain deformation from mechanical impact. This deformation is viscoelastic and differs from a traditional rigid transformation. Here, we present a machine learning enabled wireless sensing system, which can predict the trajectory of intracranial brain deformation by interpreting the magnetic sensor outputs created by the change in position of the implanted soft magnet. Both in vitro and in vivo experimental results showed an overall accuracy of over 92%, suggesting that this sensing scheme can be an effective tool for studying TBI due to in situ and real-time brain deformation prediction... Read more
Posted on 30 NOV 2020
Articles
Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients using Primary Care Electronic Health Records
Gavin Tsang, Shang-Ming Zhou, Xianghua Xie
A growing elderly population suffering from incurable, chronic conditions such as dementia present a continual strain on medical services due to mental impairment paired with high comorbidity resulting in increased hospitalization risk. The identification of at risk individuals allows for... Read more
Posted on 25 NOV 2020
Articles
Early detection of Acute Chest Syndrome through electronic recording and analysis of auscultatory percussion
Bekah Allen, Robert Molokie, Thomas J. Royston
Acute chest syndrome (ACS) is the leading cause of death among people with sickle cell disease. ACS is clinically defined and diagnosed by the presence of a new pulmonary infiltrate on chest imaging with accompanying fever and respiratory symptoms like... Read more
Posted on 6 OCT 2020
Articles
SARS-CoV-2 Detection from Voice
Gadi Pinkas, Yarden Karny, Aviad Malachi, Galia Barkai, Gideon Bachar, Vered Aharonson
Abstract: Automated voice-based detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could facilitate the screening for COVID19. A dataset of cellular phone recordings from 88 subjects was recently collected. The dataset included vocal utterances, speech and coughs that were self-recorded... Read more
Posted on 25 SEP 2020
Articles
Wearable Device-Independent Next Day Activity and Next Night Sleep Prediction for Rehabilitation Populations
Allison Fellger, Gina Sprint, Douglas Weeks, Elena Crooks, Diane J. Cook
Wearable sensor-based devices are increasingly applied in free-living and clinical settings to collect fine-grained, objective data about activity and sleep behavior. The manufacturers of these devices provide proprietary software that labels the sensor data at specified time intervals with activity... Read more
Posted on 14 AUG 2020
Featured Articles
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Andreas S. Panayides, Amir Amini, Nenad Filipovic, Ashish Sharma, Sotirios Tsaftaris, Alistair Young, David J. Foran, Nhan Do, Spyretta Golemati, Tahsin Kurc, Kun Huang, Konstantina S. Nikita, Ben P. Veasey, Michalis Zervakis, Joel H. Saltz, Constantinos S. Pattichis
This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity... Read more
Posted on 1 JUL 2020
Featured Articles
Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach
Guangyu Jia, Hak-Keung Lam, Shichao Ma, Zhaohui Yang, Yujia Xu, Bo Xiao
Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces the challenges of unsatisfied classification accuracy, insufficient generalization ability, lack... Read more
Posted on 30 JUN 2020
Featured Articles
Real-time Radiofrequency Ablation Lesion Depth Estimation Using Multi-Frequency Impedance with a Deep Neural Network and Tree-based Ensemble
Emre Besler, Y. Curtis Wang, Alan V. Sahakian
A combination of different machine learning algorithms and a hardware setup that consists of an embedded system and a 3D-printed electrode device is used to monitor the progress of radiofrequency ablation depth on a perfused breast tissue model. The device at the center of the tissue model both applied the alternating current and collected the tissue impedance data at multiple frequencies, which is fed into tree-based ensemble (TE) models and a deep neural network (DNN). Their predictions showed a mean difference against physical measurements of 0.5 mm for the DNN and 0.7 mm for the TEs... Read more
Posted on 30 JUN 2020