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Heart Rate

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Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence.

Seminars in vascular surgery
Artificial intelligence (AI)-based technologies have garnered interest across a range of disciplines in the past several years, with an even more recent interest in various health care fields, including Vascular Surgery. AI offers a unique ability to...

Classification of health deterioration by geometric invariants.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Prediction of patient deterioration is essential in medical care, and its automation may reduce the risk of patient death. The precise monitoring of a patient's medical state requires devices placed on the body, which may c...

AI-Driven sleep staging from actigraphy and heart rate.

PloS one
Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). H...

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching.

Sensors (Basel, Switzerland)
Cardiac monitoring can be performed by means of an accelerometer attached to a subject's chest, which produces the Seismocardiography (SCG) signal. Detection of SCG heartbeats is commonly carried out by taking advantage of a simultaneous electrocardi...

Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification.

Sensors (Basel, Switzerland)
Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the heal...

Automated inter-patient arrhythmia classification with dual attention neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Arrhythmia classification based on electrocardiograms (ECG) can enhance clinical diagnostic efficiency. However, due to the significant differences in the number of different categories of heartbeats, the performance of cla...

Cardiac arrest prediction in smokers using enhanced Artificial Bee Colony algorithm with stacked autoencoder model.

Computer methods in biomechanics and biomedical engineering
In the recent times, the cardiac arrest is a severe heart disease, which results in millions of annual casualties. In this article, the heart rate variability (HRV) parameters are used for predicting cardiac arrest in smokers based on the deep learni...

Experimental Exploration of Multilevel Human Pain Assessment Using Blood Volume Pulse (BVP) Signals.

Sensors (Basel, Switzerland)
Critically ill patients often lack cognitive or communicative functions, making it challenging to assess their pain levels using self-reporting mechanisms. There is an urgent need for an accurate system that can assess pain levels without relying on ...

Coupling analysis of heart rate variability and cortical arousal using a deep learning algorithm.

PloS one
Frequent cortical arousal is associated with cardiovascular dysfunction among people with sleep-disordered breathing. Changes in heart rate variability (HRV) can represent pathological conditions associated with autonomic nervous system dysfunction. ...

Novel AI-based HRV analysis (NAIHA) in healthcare automation and related applications.

Journal of electrocardiology
BACKGROUND: Heart rate variability (HRV) analysis computed on R-R interval series of ECG records with heavy burden of ectopic beats or non-sinus rhythm can significantly distort HRV parameters and hence clinically ineligible for HRV analysis. Yet, ex...