AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Signal Processing, Computer-Assisted

Showing 431 to 440 of 1837 articles

Clear Filters

EMG-based prediction of step direction for a better control of lower limb wearable devices.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Lower-limb wearable devices can significantly improve the quality of life of subjects suffering from debilitating conditions, such as amputations, neurodegenerative disorders, and stroke-related impairments. Current control...

ConvLSNet: A lightweight architecture based on ConvLSTM model for the classification of pulmonary conditions using multichannel lung sound recordings.

Artificial intelligence in medicine
Characterization of lung sounds (LS) is indispensable for diagnosing respiratory pathology. Although conventional neural networks (NNs) have been widely employed for the automatic diagnosis of lung sounds, deep neural networks can potentially be more...

Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients.

Biomedical engineering online
BACKGROUND: Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strateg...

Intelligent deep model based on convolutional neural network's and multi-layer perceptron to classify cardiac abnormality in diabetic patients.

Physical and engineering sciences in medicine
The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding in the identification of various cardiac diseases. Commonly, the interpretation of ECGs necessitates specialized knowledge. However, this paper explor...

Multivariate modeling and prediction of cerebral physiology in acute traumatic neural injury: A scoping review.

Computers in biology and medicine
Traumatic brain injury (TBI) poses a significant global public health challenge necessitating a profound understanding of cerebral physiology. The dynamic nature of TBI demands sophisticated methodologies for modeling and predicting cerebral signals ...

EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets.

Biomedical engineering online
OBJECTIVE: Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data s...

ECGVEDNET: A Variational Encoder-Decoder Network for ECG Delineation in Morphology Variant ECGs.

IEEE transactions on bio-medical engineering
Electrocardiogram (ECG) delineation to identify the fiducial points of ECG segments, plays an important role in cardiovascular diagnosis and care. Whilst deep delineation frameworks have been deployed within the literature, several factors still hind...

The Deep-Match Framework: R-Peak Detection in Ear-ECG.

IEEE transactions on bio-medical engineering
The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential difference related to heart activity by electrodes which are embedded within earphones. However, the significant increase in wearability and comfort enab...

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks.

Sensors (Basel, Switzerland)
Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pres...

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures.

Sensors (Basel, Switzerland)
This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain-computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a signif...