feature extraction

Improving Dysarthric Speech Segmentation with Emulated and Synthetic Augmentation
Improving Dysarthric Speech Segmentation with Emulated and Synthetic Augmentation 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Background: Acoustic features extracted from speech can help with the diagnosis of neurological diseases and monitoring of symptoms over time. Temporal segmentation of audio signals into individual words is an… read more

Acoustic and Text Features Analysis for Adult ADHD Screening: A Data-Driven Approach Utilizing DIVA Interview
Acoustic and Text Features Analysis for Adult ADHD Screening: A Data-Driven Approach Utilizing DIVA Interview 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder commonly seen in childhood that leads to behavioural changes in social development and communication patterns, often continues into undiagnosed adulthood due… read more

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients
Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Background: Over a decade, tissues dissected adjacent to primary tumors have been considered “normal” or healthy samples (NATs). However, NATs have recently been discovered to be distinct from both tumorous… read more

Automated Diagnosis of COVID-19 using Deep Features and Parameter Free BAT Optimization
Author(s)3: Taranjit Kaur, Tapan K. Gandhi, Bijaya K. Panigrahi
Automated Diagnosis of COVID-19 using Deep Features and Parameter Free BAT Optimization 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

Background: Timely and precise identification of COVID-19 is an arduous task owing to the scarcity and inefficiency of the medical test kits. This has resulted in medical professionals turning towards…

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Detecting Cataract Using Smartphones
Author(s)3: Behnam Askarian, Peter Ho, Jo Woon Chong
Detecting Cataract Using Smartphones 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

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…

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Identifying hand use and hand roles after stroke using egocentric video
Author(s)3: Meng-Fen Tsai, Rosalie H. Wang, José Zariffa
Identifying hand use and hand roles after stroke using egocentric video 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

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)…

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MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
Author(s)3: Jing Zhang, Deng Liang, Aiping Liu, Min Gao, Xiang Chen, Xu Zhang, Xun Chenb
MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

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…

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A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data
Author(s)3: Md. Rashed-Al-Mahfuz, Mohammad Ali Moni, Shahadat Uddin, Salem A. Alyami, Mattew A. Summers, Valsamma Eapen
A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

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…

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LoCoMo-Net: A Low-Complex Deep Learning Framework for sEMG Based Hand Movement Recognition for Prosthetic Control
Author(s)3: Arvind Gautam, Madhuri Panwar, Archana Wankhede, Sridhar Arjunan, Ganesh Naik, Amit Charyya, Dinesh Kumar
LoCoMo-Net: A Low-Complex Deep Learning Framework for sEMG Based Hand Movement Recognition for Prosthetic Control 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

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…

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