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Heart Rate

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KPCA-WRF-prediction of heart rate using deep feature fusion and machine learning classification with tuned weighted hyper-parameter.

Network (Bristol, England)
The rapid advancement of technology such as stream processing technologies, deep-learning approaches, and artificial intelligence plays a prominent and vital role, to detect heart rate using a prediction model. However, the existing methods could not...

Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm.

BMC medical informatics and decision making
BACKGROUND: Signal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step for retrieving complete information and extracting signal characteristics for each lead in cardiology clinical practice. However, it is arduous to manuall...

Deep Learning-Based Motion Correction in Projection Domain for Coronary Computed Tomography Angiography: A Clinical Evaluation.

Journal of computer assisted tomography
OBJECTIVE: This study aimed to evaluate the clinical performance of a deep learning-based motion correction algorithm (MCA) in projection domain for coronary computed tomography angiography (CCTA).

Standardized motion detection and real time heart rate monitoring of aerobics training based on convolution neural network.

Preventive medicine
In order to make the teaching and training of aerobics more standardized, it is necessary to use scientific means to detect and monitor the movement standardization in teaching and training and the change of human heart rate in the training process, ...

AI-designed NMR spectroscopy RF pulses for fast acquisition at high and ultra-high magnetic fields.

Nature communications
Nuclear magnetic resonance (NMR) spectroscopy is a powerful high-resolution tool for characterizing biomacromolecular structure, dynamics, and interactions. However, the lengthy longitudinal relaxation of the nuclear spins significantly extends the t...

Beat-wise segmentation of electrocardiogram using adaptive windowing and deep neural network.

Scientific reports
Timely detection of anomalies and automatic interpretation of an electrocardiogram (ECG) play a crucial role in many healthcare applications, such as patient monitoring and post treatments. Beat-wise segmentation is one of the essential steps in ensu...

A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data.

IEEE transactions on neural networks and learning systems
Fiber-optic distributed acoustic sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterizati...

An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction.

Physical and engineering sciences in medicine
This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The model performs pre-processing of the heartbeat signal by automatically and efficiently extracting tim...

Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs.

Scientific reports
Unrecognized deterioration of COVID-19 patients can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical images o...

Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time.

Sensors (Basel, Switzerland)
With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care and reducing healthcare costs. The Internet of Things (IoT) has recently drawn much interest as a potential remote health ...