AI Medical Compendium Topic

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Signal Processing, Computer-Assisted

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Enhanced convolutional neural network accelerators with memory optimization for routing applications.

PloS one
Currently, Convolutional Neural Networks (CNN) accelerators find application in various digital domains, each highlighting memory utilization as a significant concern leading to system degradation. In response, our present work focuses on optimizing ...

SMANet: A Model Combining SincNet, Multi-Branch Spatial-Temporal CNN, and Attention Mechanism for Motor Imagery BCI.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an ...

Deep learning models for segmenting phonocardiogram signals: a comparative study.

PloS one
Cardiac auscultation requires the mechanical vibrations occurring on the body's surface, which carries a range of sound frequencies. These sounds are generated by the movement and pulsation of different cardiac structures as they facilitate blood cir...

A WaveNet-based model for predicting the electroglottographic signal from the acoustic voice signal.

The Journal of the Acoustical Society of America
The electroglottographic (EGG) signal offers a non-invasive approach to analyze phonation. It is known, if not obvious, that the onset of vocal fold contacting has a substantial effect on how the vocal folds vibrate and on the quality of the voice. G...

A deep learning framework leveraging spatiotemporal feature fusion for electrophysiological source imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Electrophysiological source imaging (ESI) is a challenging technique for noninvasively measuring brain activity, which involves solving a highly ill-posed inverse problem. Traditional methods attempt to address this challen...

Portable ECG and PCG wireless acquisition system and multiscale CNN feature fusion Bi-LSTM network for coronary artery disease diagnosis.

Computers in biology and medicine
Coronary artery disease (CAD) is a major cause of mortality, especially among aging populations, making timely and accurate diagnosis essential. In this work, a portable wireless device powered by artificial intelligence for CAD detection is proposed...

Automatic cough detection via a multi-sensor smart garment using machine learning.

Computers in biology and medicine
Coughing behavior is associated with conditions such as sleep apnea, asthma, and chronic obstructive pulmonary disorder and can severely affect quality of life in those affected. In this context, coughing quantification is often important, but routin...

Multi-scale convolutional transformer network for motor imagery brain-computer interface.

Scientific reports
Brain-computer interface (BCI) systems allow users to communicate with external devices by translating neural signals into real-time commands. Convolutional neural networks (CNNs) have been effectively utilized for decoding motor imagery electroencep...

A multi-scale convolutional LSTM-dense network for robust cardiac arrhythmia classification from ECG signals.

Computers in biology and medicine
Cardiac arrhythmias are irregular heart rhythms that, if undetected, can lead to severe cardiovascular conditions. Detecting these anomalies early through electrocardiogram (ECG) signal analysis is critical for preventive healthcare and effective tre...

Towards interpretable sleep stage classification with a multi-stream fusion network.

BMC medical informatics and decision making
Sleep stage classification is a significant measure in assessing sleep quality and diagnosing sleep disorders. Many researchers have investigated automatic sleep stage classification methods and achieved promising results. However, these methods igno...