AIMC Topic: Signal Processing, Computer-Assisted

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Detection and classification of adult epilepsy using hybrid deep learning approach.

Scientific reports Oct 16, 2023
The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinc...
Seizures Signal Processing, Computer-Assisted Electroencephalography Epilepsy Neural Networks, Computer Algorithms Humans Deep Learning Child Brain
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Unraveling the complexities of pathological voice through saliency analysis.

Computers in biology and medicine Oct 14, 2023
The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is very important. Conventional diagnosis of these voice pathologies can be invasive and costly. Voice ...
Humans Female Adult Male Neural Networks, Computer Databases, Factual Voice Disorders Signal Processing, Computer-Assisted Voice
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Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

International journal of neural systems Oct 13, 2023
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...
Epilepsy Seizures Signal Processing, Computer-Assisted Brain Humans Deep Learning Algorithms Electroencephalography
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A Sparse Model-Inspired Deep Thresholding Network for Exponential Signal Reconstruction-Application in Fast Biological Spectroscopy.

IEEE transactions on neural networks and learning systems Oct 5, 2023
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...
Neural Networks, Computer Image Processing, Computer-Assisted Signal Processing, Computer-Assisted Spectrum Analysis Algorithms Tomography, X-Ray Computed
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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 Oct 4, 2023
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...
Ventricular Premature Complexes Electrocardiography Signal Processing, Computer-Assisted Databases, Factual Deep Learning Humans Neural Networks, Computer
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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 Sep 13, 2023
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 ...
Gymnotiformes Algorithms Animals Animal Communication Electric Organ Electric Fish Signal Processing, Computer-Assisted Supervised Machine Learning
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A Deep Learning-Based Automated Framework for Subpeak Designation on Intracranial Pressure Signals.

Sensors (Basel, Switzerland) Sep 12, 2023
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...
Deep Learning Signal Processing, Computer-Assisted Neural Networks, Computer Humans Algorithms Intracranial Pressure
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Automatic identification of schizophrenia employing EEG records analyzed with deep learning algorithms.

Schizophrenia research Sep 8, 2023
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...
Signal Processing, Computer-Assisted Schizophrenia Bayes Theorem Electroencephalography Humans Deep Learning Support Vector Machine Algorithms
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The influence of EEG channels and features significance on automatic detection of epileptic waves in MECT.

Computer methods in biomechanics and biomedical engineering Sep 5, 2023
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...
Support Vector Machine Electroencephalography Female Signal Processing, Computer-Assisted Epilepsy Automation Humans Male
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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 Aug 7, 2023
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 ...
Signal Processing, Computer-Assisted Brain Imagination Humans Electroencephalography Neural Networks, Computer
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