IEEE Journal of Translational Engineering in Health and Medicine

Detecting Cataract Using Smartphones
Objective: Cataract, which is the clouding of the crystalline lens, is the most prevalent eye disease accounting for 51% of all eye diseases in the U.S. Cataract is a progressive disease, and its early detection is critical for preventing blindness.... Read more
Privacy-Preserving Deep Speaker Separation for Smartphone-Based Passive Speech Assessment
Goal: Smartphones can be used to passively assess and monitor patients’ speech impairments caused by ailments such as Parkinson’s disease, Traumatic Brain Injury (TBI), Post-Traumatic Stress Disorder (PTSD) and neurodegenerative diseases such as Alzheimer’s disease and dementia. However, passive audio... Read more
Identifying hand use and hand roles after stroke using egocentric video
Objective: Upper limb (UL) impairment impacts quality of life, but is common after stroke. UL function evaluated in the clinic may not reflect use in activities of daily living (ADLs) after stroke, and current approaches for assessment at home rely... Read more
Featured Articles
Prediction of Freezing of Gait in Parkinson’s Disease Using Statistical Inference and Lower–Limb Acceleration Data
The freezing of gait (FoG) is a common type of motor dysfunction in advanced Parkinson’s disease (PD) associated with falls. Over the last decade, a significant amount of studies has been focused on detecting FoG episodes in clinical and home... Read more
Deep Learning Classification of Systemic Sclerosis Skin using the MobileNetV2 Model
Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management plans. Machine learning algorithms, especially deep learning, have been found... Read more
MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then... Read more
A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data
Background: Diagnosing epileptic seizures using electroencephalogram (EEG) in combination with deep learning computational methods has received much attention in recent years. However, to date, deep learning techniques in seizure detection have not been effectively harnessed due to sub-optimal classifier design... Read more
LoCoMo-Net: A Low-Complex Deep Learning Framework for sEMG Based Hand Movement Recognition for Prosthetic Control
Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named ‘Low-Complex Movement recognition-Net’ (LoCoMo-Net)... Read more
Published Articles
Accurate Fiducial Point Detection Using Haar Wavelet for Beat-by-Beat Blood Pressure Estimation
Pulse Arrival Time (PAT) derived from Electrocardiogram (ECG) and Photoplethysmogram (PPG) for cuff-less Blood Pressure (BP) measurement has been a contemporary and widely accepted technique. However, the features extracted for it are conventionally from an isolated pulse of ECG and... Read more
Articles, Published Articles
Non-contact Early Warning of Shaking Palsy
        Abstract Objective: Parkinsonian gait is a defining feature of Shaking Palsy (SP) and it has one of the worse impact on human healthy life than other SP symptoms. The objective of this work is to propose a Parkinsonian gait detection system... Read more