AIMC Topic: Deep Learning

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Computational design of soluble and functional membrane protein analogues.

Nature
De novo design of complex protein folds using solely computational means remains a substantial challenge. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topolog...

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...

Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation.

IEEE transactions on bio-medical engineering
OBJECTIVE: Recent deep learning techniques hold promise to enable IMU-driven kinetic assessment; however, they require large extents of ground reaction force (GRF) data to serve as labels for supervised model training. We thus propose using existing ...

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...

Improved sports image classification using deep neural network and novel tuna swarm optimization.

Scientific reports
Sports image classification is a complex undertaking that necessitates the utilization of precise and robust techniques to differentiate between various sports activities. This study introduces a novel approach that combines the deep neural network (...

SleepFC: Feature Pyramid and Cross-Scale Context Learning for Sleep Staging.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Automated sleep staging is essential to assess sleep quality and treat sleep disorders, so the issue of electroencephalography (EEG)-based sleep staging has gained extensive research interests. However, the following difficulties exist in this issue:...

Super-resolution Deep Learning Reconstruction for 3D Brain MR Imaging: Improvement of Cranial Nerve Depiction and Interobserver Agreement in Evaluations of Neurovascular Conflict.

Academic radiology
RATIONALE AND OBJECTIVES: To determine if super-resolution deep learning reconstruction (SR-DLR) improves the depiction of cranial nerves and interobserver agreement when assessing neurovascular conflict in 3D fast asymmetric spin echo (3D FASE) brai...

Improved vascular depiction and image quality through deep learning reconstruction of CT hepatic arteriography during transcatheter arterial chemoembolization.

Japanese journal of radiology
PURPOSE: To evaluate the effect of deep learning reconstruction (DLR) on vascular depiction, tumor enhancement, and image quality of computed tomography hepatic arteriography (CTHA) images acquired during transcatheter arterial chemoembolization (TAC...