AIMC Topic: Signal Processing, Computer-Assisted
Clear Filters Showing 951 to 960 of 1999 articles
Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings.
Scientific reports
May 26, 2020
High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening and aspiration detection, as it does not involve the use of harmful ionizing radiation approaches. Automatic extraction of swallowing events in cervi...
Detection of Depression and Scaling of Severity Using Six Channel EEG Data.
Journal of medical systems
May 21, 2020
Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong diagnosis of depression is absence of any laboratory test for detection as well as severity scaling o...
Intelligent Fault-Prediction Assisted Self-Healing for Embryonic Hardware.
IEEE transactions on biomedical circuits and systems
May 19, 2020
This paper proposes novel methods for making embryonic bio-inspired hardware efficient against faults through self-healing, fault prediction, and fault-prediction assisted self-healing. The proposed self-healing recovers a faulty embryonic cell throu...
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.
NeuroImage
May 18, 2020
Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, m...
EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research.
Computational and mathematical methods in medicine
May 16, 2020
In recent years, with the development of brain science and biomedical engineering, as well as the rapid development of electroencephalogram (EEG) signal analysis methods, using EEG signals to monitor human health has become a very popular research fi...
Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.
Journal of the American Heart Association
May 14, 2020
BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized interpretation has not been able to reach physician-level accuracy in detecting (acute) cardiac abnorm...
EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system.
Medical & biological engineering & computing
May 11, 2020
Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed a new transfer learning algorithm to classify motor imagery EEG data. By analyzing the power spectru...
Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.
Journal of medical systems
May 10, 2020
Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this patholog...
Predicting polysomnographic severity thresholds in children using machine learning.
Pediatric research
May 9, 2020
BACKGROUND: Approximately 500,000 children undergo tonsillectomy and adenoidectomy (T&A) annually for treatment of obstructive sleep disordered breathing (oSDB). Although polysomnography is beneficial for preoperative risk stratification in these chi...
Risk
Actigraphy
Hypersensitivity
Polysomnography
Oximetry
Adenoidectomy
Monitoring, Physiologic
Signal Processing, Computer-Assisted
Decision Making
Sleep Apnea Syndromes
Tonsillectomy
Sleep Apnea, Obstructive
Anthropometry
Humans
Machine Learning
Algorithms
Female
Asthma
Male
Surveys and Questionnaires
Child
Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning.
Computational and mathematical methods in medicine
May 8, 2020
Electroencephalography (EEG) plays an import role in monitoring the brain activities of patients with epilepsy and has been extensively used to diagnose epilepsy. Clinically reading tens or even hundreds of hours of EEG recordings is very time consum...
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