AI Medical Compendium Topic:
Signal Processing, Computer-Assisted

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Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization.

Heart rhythm
BACKGROUND: Diagnosing long QT syndrome (LQTS) remains challenging because of a considerable overlap in QT interval between patients with LQTS and healthy subjects. Characterizing T-wave morphology might improve LQTS diagnosis.

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor.

Journal of healthcare engineering
Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human ...

A Multimodal Wearable System for Continuous and Real-Time Breathing Pattern Monitoring During Daily Activity.

IEEE journal of biomedical and health informatics
OBJECTIVE: This study aims to understand breathing patterns during daily activities by developing a wearable respiratory and activity monitoring (WRAM) system.

A novel method of motor imagery classification using eeg signal.

Artificial intelligence in medicine
A subject of extensive research interest in the Brain Computer Interfaces (BCIs) niche is motor imagery (MI), where users imagine limb movements to control the system. This interest is owed to the immense potential for its applicability in gaming, ne...

A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition.

Sensors (Basel, Switzerland)
Speech is the most significant mode of communication among human beings and a potential method for human-computer interaction (HCI) by using a microphone sensor. Quantifiable emotion recognition using these sensors from speech signals is an emerging ...

Sparse Ensemble Machine Learning to Improve Robustness of Long-Term Decoding in iBMIs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the en...

Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.

Physiological measurement
OBJECTIVE: Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracking. Its simplicity and cost-effectiveness has enabled a variety of biomedical applications, such as ...

Accurate Deep Learning-Based Sleep Staging in a Clinical Population With Suspected Obstructive Sleep Apnea.

IEEE journal of biomedical and health informatics
The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overc...

Synchronization of Hindmarsh Rose Neurons.

Neural networks : the official journal of the International Neural Network Society
Modeling and implementation of biological neurons are key to the fundamental understanding of neural network architectures in the brain and its cognitive behavior. Synchronization of neuronal models play a significant role in neural signal processing...

BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model.

Medical hypotheses
A brain tumor is a mass that grows unevenly in the brain and directly affects human life. This mass occurs spontaneously because of the tissues surrounding the brain or the skull. Surgical methods are generally preferred for the treatment of the brai...