AIMC Topic: Young Adult

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DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model.

Medical & biological engineering & computing
Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introd...

Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder.

Translational psychiatry
Suicide is a growing public health problem around the world. The most important risk factor for suicide is underlying psychiatric illness, especially depression. Detailed classification of suicide in patients with depression can greatly enhance perso...

Identifying Key Training Load and Intensity Indicators in Ice Hockey Using Unsupervised Machine Learning.

Research quarterly for exercise and sport
To identify key training load (TL) and intensity indicators in ice hockey, practice, and game data were collected using a wearable 200-Hz accelerometer and heart rate (HR) recording throughout a four-week (29 days) competitive period (23 practice ses...

Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features.

European journal of applied physiology
PURPOSE: Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical ...

Derivative Method to Detect Sleep and Awake States through Heart Rate Variability Analysis Using Machine Learning Algorithms.

Sensors (Basel, Switzerland)
Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is po...

Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm.

PloS one
This research aims to establish a practical stress detection framework by integrating physiological indicators and deep learning techniques. Utilizing a virtual reality (VR) interview paradigm mirroring real-world scenarios, our focus is on classifyi...

Evaluation of Serum Visfatin as a Biomarker of Lupus Nephritis in Egyptian Patients with Systemic Lupus Erythematosus.

Saudi journal of kidney diseases and transplantation : an official publication of the Saudi Center for Organ Transplantation, Saudi Arabia
One of the most significant consequences of systemic lupus erythematosus (SLE) is lupus nephritis (LN). Visfatin, an adipokine that is significantly expressed in visceral fat and is a marker of endothelial dysfunction in chronic kidney disease, has m...

Deep Learning-based Hierarchical Brain Segmentation with Preliminary Analysis of the Repeatability and Reproducibility.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: We developed new deep learning-based hierarchical brain segmentation (DLHBS) method that can segment T1-weighted MR images (T1WI) into 107 brain subregions and calculate the volume of each subregion. This study aimed to evaluate the repeatab...

A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network.

Neuroscience bulletin
Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional neural networks (CNNs) effectively extract these features but face limitations like overfitting due to small datasets. To address this issue, we propose a light...

Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals.

Physical and engineering sciences in medicine
Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML) algorithm based on the Extreme Learning Machine (ELM) was developed for identifying motor actions usi...