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Heart Rate

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Mental Stress Detection Using a Wearable In-Ear Plethysmography.

Biosensors
This study presents an ear-mounted photoplethysmography (PPG) system that is designed to detect mental stress. Mental stress is a prevalent condition that can negatively impact an individual's health and well-being. Early detection and treatment of m...

Convolutional Neural Network for Individual Identification Using Phase Space Reconstruction of Electrocardiogram.

Sensors (Basel, Switzerland)
Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convoluti...

Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network.

Sensors (Basel, Switzerland)
Oxygen uptake (V˙O2) is an important metric in any exercise test including walking and running. It can be measured using portable spirometers or metabolic analyzers. Those devices are, however, not suitable for constant use by consumers due to their ...

Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models.

Sensors (Basel, Switzerland)
Stress is an increasingly prevalent mental health condition across the world. In Europe, for example, stress is considered one of the most common health problems, and over USD 300 billion are spent on stress treatments annually. Therefore, monitoring...

An interpretable machine learning approach to multimodal stress detection in a simulated office environment.

Journal of biomedical informatics
BACKGROUND AND OBJECTIVE: Work-related stress affects a large part of today's workforce and is known to have detrimental effects on physical and mental health. Continuous and unobtrusive stress detection may help prevent and reduce stress by providin...

A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge.

Sensors (Basel, Switzerland)
Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requ...

A concept for emotion recognition systems for children with profound intellectual and multiple disabilities based on artificial intelligence using physiological and motion signals.

Disability and rehabilitation. Assistive technology
PURPOSE: This study proposes a concept for emotion recognition systems for children with profound intellectual and multiple disabilities (PIMD) based on artificial intelligence (AI) using physiological and motion signals.

X-iPPGNet: A novel one stage deep learning architecture based on depthwise separable convolutions for video-based pulse rate estimation.

Computers in biology and medicine
Pulse rate (PR) is one of the most important markers for assessing a person's health. With the increasing demand for long-term health monitoring, much attention is being paid to contactless PR estimation using imaging photoplethysmography (iPPG). Thi...

Two phases based training method for designing codewords for a set of perceptrons with each perceptron having multi-pulse type activation function.

Network (Bristol, England)
This paper proposes a two phases-based training method to design the codewords to map the cluster indices of the input feature vectors to the outputs of the new perceptrons with the multi-pulse type activation functions. Our proposed method is applie...

Diagnosis of arrhythmias with few abnormal ECG samples using metric-based meta learning.

Computers in biology and medicine
A major challenge in artificial intelligence based ECG diagnosis lies that it is difficult to obtain sufficient annotated training samples for each rhythm type, especially for rare diseases, which makes many approaches fail to achieve the desired per...