AIMC Topic: Signal Processing, Computer-Assisted

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Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

International journal of neural systems
ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately p...

A Sparse Model-Inspired Deep Thresholding Network for Exponential Signal Reconstruction-Application in Fast Biological Spectroscopy.

IEEE transactions on neural networks and learning systems
The nonuniform sampling (NUS) is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partially sampled exponentials is highly expected in general signal processing and many...

Enhancing the performance of premature ventricular contraction detection in unseen datasets through deep learning with denoise and contrast attention module.

Computers in biology and medicine
Premature ventricular contraction (PVC) is a common and harmless cardiac arrhythmia that can be asymptomatic or cause palpitations and chest pain in rare instances. However, frequent PVCs can lead to more serious arrhythmias, such as atrial fibrillat...

Supervised learning algorithm for analysis of communication signals in the weakly electric fish Apteronotus leptorhynchus.

Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology
Signal analysis plays a preeminent role in neuroethological research. Traditionally, signal identification has been based on pre-defined signal (sub-)types, thus being subject to the investigator's bias. To address this deficiency, we have developed ...

A Deep Learning-Based Automated Framework for Subpeak Designation on Intracranial Pressure Signals.

Sensors (Basel, Switzerland)
The intracranial pressure (ICP) signal, as monitored on patients in intensive care units, contains pulses of cardiac origin, where P1 and P2 subpeaks can often be observed. When calculable, the ratio of their relative amplitudes is an indicator of th...

Automatic identification of schizophrenia employing EEG records analyzed with deep learning algorithms.

Schizophrenia research
Electroencephalography is a method of detecting and analyzing electrical activity in the brain. This electrical activity can be recorded and processed to aid in the clinical diagnosis of mental disorders. In this study, a novel system for classifying...

The influence of EEG channels and features significance on automatic detection of epileptic waves in MECT.

Computer methods in biomechanics and biomedical engineering
Modified Electric Convulsive Therapy (MECT) is an efficacious physical therapy in treating mental disorders. The occurrence of epilepsy is a crucial benchmark for evaluating therapeutic effectiveness. However, the medical field still lacks relevant r...

A novel method for modeling effective connections between brain regions based on EEG signals and graph neural networks for motor imagery detection.

Computer methods in biomechanics and biomedical engineering
Classified as biomedical signal processing, cerebral signal processing plays a key role in human-computer interaction (HCI) and medical diagnosis. The motor imagery (MI) problem is an important research area in this field. Accurate solutions to this ...

Domain Agnostic Post-Processing for QRS Detection Using Recurrent Neural Network.

IEEE journal of biomedical and health informatics
Deep-learning-based QRS-detection algorithms often require essential post-processing to refine the output prediction-stream for R-peak localisation. The post-processing involves basic signal-processing tasks including the removal of random noise in t...

Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm.

BMC medical informatics and decision making
BACKGROUND: Signal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step for retrieving complete information and extracting signal characteristics for each lead in cardiology clinical practice. However, it is arduous to manuall...