RDLINet: A novel lightweight inception network for respiratory disease classification using lung sounds
Respiratory diseases are the world's third leading cause of mortality. Early detection is
critical in dealing with respiratory diseases, as it improves the effectiveness of intervention …
critical in dealing with respiratory diseases, as it improves the effectiveness of intervention …
Correlation-filter-based channel and feature selection framework for hybrid EEG-fNIRS BCI applications
The proposed study is based on a feature and channel selection strategy that uses
correlation filters for brain–computer interface (BCI) applications using …
correlation filters for brain–computer interface (BCI) applications using …
Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography-Based Cognitive Workload Detection
This article summarizes a systematic review of the electroencephalography (EEG)-based
cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate …
cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate …
State-of-the-art mental tasks classification based on electroencephalograms: a review
Electroencephalograms (EEGs) play an important role in analyzing different mental tasks
and neurological disorders. Hence, they are a critical component for designing various …
and neurological disorders. Hence, they are a critical component for designing various …
Subject-Wise Cognitive Load Detection Using Time–Frequency EEG and Bi-LSTM
J Yedukondalu, D Sharma, LD Sharma - Arabian Journal for Science and …, 2024 - Springer
Cognitive load detection using electroencephalogram (EEG) signals is a technique
employed to understand and measure the mental workload or cognitive demands placed on …
employed to understand and measure the mental workload or cognitive demands placed on …
[PDF][PDF] AsthmaSCELNet: A lightweight supervised contrastive embedding learning framework for asthma classification using lung sounds
Asthma is one of the most prevalent respiratory disorders, which can be identified by
different modalities such as speech, wheezing of lung sounds (LSs), spirometric measures …
different modalities such as speech, wheezing of lung sounds (LSs), spirometric measures …
A Unified Deep Learning Framework for Smartphone-Enabled ADHD Detection
Attention-deficit hyperactivity disorder (ADHD) is a persistent condition with repeated issues,
such as difficulty maintaining attention, impetuous behavior, and hyperactivity. It severely …
such as difficulty maintaining attention, impetuous behavior, and hyperactivity. It severely …
[HTML][HTML] EEG stress classification based on Doppler spectral features for ensemble 1D-CNN with LCL activation function
The paper proposes an induced stress classification algorithm that uses features from the
Doppler spectrum. In this approach, a reference signal source is used to obtain the …
Doppler spectrum. In this approach, a reference signal source is used to obtain the …
Pulmo-TS2ONN: A Novel Triple Scale Self Operational Neural Network for Pulmonary Disorder Detection Using Respiratory Sounds
Pulmonary disorders (PDs) are one of the substantial hazards to human life, which can be
diagnosed by a variety of clinical modalities, including peak flowmeter and spirometry …
diagnosed by a variety of clinical modalities, including peak flowmeter and spirometry …
DMAeEDNet: Dense Multiplicative Attention Enhanced Encoder Decoder Network for Ultrasound-Based Automated Breast Lesion Segmentation
Automated and precise segmentation of breast lesions can facilitate early diagnosis of
breast cancer. Recent research studies employ deep learning for automatic segmentation of …
breast cancer. Recent research studies employ deep learning for automatic segmentation of …