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Heart Rate

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ECG Heartbeat Classification Based on an Improved ResNet-18 Model.

Computational and mathematical methods in medicine
Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique r...

Interpretable heartbeat classification using local model-agnostic explanations on ECGs.

Computers in biology and medicine
Treatment and prevention of cardiovascular diseases often rely on Electrocardiogram (ECG) interpretation. Dependent on the physician's variability, ECG interpretation is subjective and prone to errors. Machine learning models are often developed and ...

Robotic Locomotor Training Leads to Cardiovascular Changes in Individuals With Incomplete Spinal Cord Injury Over a 24-Week Rehabilitation Period: A Randomized Controlled Pilot Study.

Archives of physical medicine and rehabilitation
OBJECTIVE: To describe the effect of robotic locomotor training (RLT) and activity-based training (ABT) on cardiovascular indices during various physiological positions in individuals with spinal cord injury.

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals.

Sensors (Basel, Switzerland)
Mental stress can lead to traffic accidents by reducing a driver's concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers' stress in advance to prevent dangerous situations increased. Thus, we propose...

Genetic-fuzzy logic model for a non-invasive measurement of a stroke volume.

Computer methods and programs in biomedicine
BACKGROUND: Despite the importance of stroke volume readings in understanding the work of the cardiovascular system in patients, its routine daily measurement outside of a hospital in the absence of special equipment presents a problem for a comprehe...

Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states.

BMC anesthesiology
BACKGROUND: Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under ce...

Human and Human-Interfaced AI Interactions: Modulation of Human Male Autonomic Nervous System via Pupil Mimicry.

Sensors (Basel, Switzerland)
Pupillary alterations in virtual humans induce neurophysiological responses within an observer. Technological advances have enabled rapid developments in artificial intelligence (AI), from verbal systems, to visual AI interfaces with the ability to e...

Deep learning framework for subject-independent emotion detection using wireless signals.

PloS one
Emotion states recognition using wireless signals is an emerging area of research that has an impact on neuroscientific studies of human behaviour and well-being monitoring. Currently, standoff emotion detection is mostly reliant on the analysis of f...