AIMC Topic: Heart Rate
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A machine-learning approach for stress detection using wearable sensors in free-living environments.
Computers in biology and medicine
Jul 18, 2024
Stress is a psychological condition resulting from the body's response to challenging situations, which can negatively impact physical and mental health if experienced over prolonged periods. Early detection of stress is crucial to prevent chronic he...
Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.
Journal of cardiovascular translational research
Jul 17, 2024
Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this ...
Ventricular Pressure
Swine, Miniature
Heart Sounds
Phonocardiography
Disease Models, Animal
Predictive Value of Tests
Signal Processing, Computer-Assisted
Heart Rate
Machine Learning
Ventricular Function, Left
Feasibility Studies
Oximetry
Animals
Heart Failure
Stroke Volume
Swine
Electrocardiography
A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification.
Medical engineering & physics
Jul 17, 2024
As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG) abnormalities is becoming increasingly essential. Scholars have developed numerous methods of arrh...
Delineation of 12-Lead ECG Representative Beats Using Convolutional Encoder-Decoders with Residual and Recurrent Connections.
Sensors (Basel, Switzerland)
Jul 17, 2024
The aim of this study is to address the challenge of 12-lead ECG delineation by different encoder-decoder architectures of deep neural networks (DNNs). This study compares four concepts for encoder-decoders based on a fully convolutional architecture...
Enhancing ECG Heartbeat classification with feature fusion neural networks and dynamic minority-biased batch weighting loss function.
Physiological measurement
Jul 10, 2024
This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by signif...
A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT.
Scientific reports
Jul 8, 2024
The goal of this research is to create an ensemble deep learning model for Internet of Things (IoT) applications that specifically target remote patient monitoring (RPM) by integrating long short-term memory (LSTM) networks and convolutional neural n...
Identifying Key Training Load and Intensity Indicators in Ice Hockey Using Unsupervised Machine Learning.
Research quarterly for exercise and sport
Jul 3, 2024
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...
Derivative Method to Detect Sleep and Awake States through Heart Rate Variability Analysis Using Machine Learning Algorithms.
Sensors (Basel, Switzerland)
Jul 3, 2024
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...
A Residual U-Net Neural Network for Seismocardiogram Denoising and Analysis During Physical Activity.
IEEE journal of biomedical and health informatics
Jul 2, 2024
Seismocardiogram (SCG) signals are noninvasively obtained cardiomechanical signals containing important features for cardiovascular health monitoring. However, these signals are prone to contamination by motion noise, which can significantly impact a...
Multilayer Perceptron-Based Wearable Exercise-Related Heart Rate Variability Predicts Anxiety and Depression in College Students.
Sensors (Basel, Switzerland)
Jun 28, 2024
(1) Background: This study aims to investigate the correlation between heart rate variability (HRV) during exercise and recovery periods and the levels of anxiety and depression among college students. Additionally, the study assesses the accuracy of...
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