AIMC Topic: Signal Processing, Computer-Assisted

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A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram.

IEEE transactions on bio-medical engineering
OBJECTIVE: Gastric slow wave abnormalities have been associated with gastric motility disorders. Invasive studies in humans have described normal and abnormal propagation of the slow wave. This study aims to disambiguate the abnormally functioning wa...

Deep convolutional neural network for classification of sleep stages from single-channel EEG signals.

Journal of neuroscience methods
Using a smart method for automatic diagnosis in medical applications, such as sleep stage classification is considered as one of the important challenges of the last few years which can replace the time-consuming process of visual inspection done by ...

Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The recognition of many sleep related pathologies highly relies on an accurate classification of sleep stages. Clinically, sleep stages are usually labelled by sleep experts through visually inspecting the whole-night polyso...

Slow cortical potential signal classification using concave-convex feature.

Journal of neuroscience methods
BACKGROUND: The classification of the slow cortical potential (SCP) signals plays a key role in a variety of research areas, including disease diagnostics, human-machine interaction, and education. The widely used classification methods, which combin...

Autoencoding of long-term scalp electroencephalogram to detect epileptic seizure for diagnosis support system.

Computers in biology and medicine
INTRODUCTION: Epileptologists could benefit from a diagnosis support system that automatically detects seizures because visual inspection of long-term electroencephalograms (EEGs) is extremely time-consuming. However, the diversity of seizures among ...

Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network.

Sensors (Basel, Switzerland)
The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG classification based on faster regions with a convolutio...

Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification.

Computational intelligence and neuroscience
Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is ...

In situ tissue classification during laser ablation using acoustic signals.

Journal of biophotonics
We suggest a novel method to classify the type of tissue that is being ablated, using the recorded acoustic sound waves during pulsed ultraviolet laser ablation. The motivation of the current research is tissue classification during vascular interven...

I-Vector-Based Patient Adaptation of Deep Neural Networks for Automatic Heartbeat Classification.

IEEE journal of biomedical and health informatics
Automatic classification of electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A big challenge in automatic ECG classification is the variation in the waveforms and characteristics of ECG signals among different patients. ...

A novel IRBF-RVM model for diagnosis of atrial fibrillation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learn...