AIMC Topic: Signal Processing, Computer-Assisted

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Application of Statistical Analysis and Machine Learning to Identify Infants' Abnormal Suckling Behavior.

IEEE journal of translational engineering in health and medicine Apr 17, 2024
OBJECTIVE: Identify infants with abnormal suckling behavior from simple non-nutritive suckling devices.
Infant Female Signal Processing, Computer-Assisted Infant, Newborn Breast Feeding Male Humans Sucking Behavior Machine Learning
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Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Apr 16, 2024
OBJECTIVES: We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which featu...
Diagnosis, Computer-Assisted Time Factors Diagnosis, Differential Predictive Value of Tests Male Middle Aged Case-Control Studies Brain Waves Ischemic Stroke Electroencephalography Signal Processing, Computer-Assisted Brain Retrospective Studies Female Aged, 80 and over Adult Reproducibility of Results Aged Humans Machine Learning Databases, Factual
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Apnoea detection using ECG signal based on machine learning classifiers and its performances.

Journal of medical engineering & technology Apr 16, 2024
Sleep apnoea is a common disorder affecting sleep quality by obstructing the respiratory airway. This disorder can also be correlated to certain diseases like stroke, depression, neurocognitive disorder, non-communicable disease, etc. We implemented ...
Middle Aged Electrocardiography Female Support Vector Machine Signal Processing, Computer-Assisted Humans Adult Sleep Apnea Syndromes Machine Learning Male Algorithms
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A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring.

Journal of clinical monitoring and computing Apr 15, 2024
Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deteri...
Humans Machine Learning Algorithms Analgesics, Opioid Artifacts Oximetry Capnography Prospective Studies Female Male Reproducibility of Results Monitoring, Physiologic Middle Aged Signal Processing, Computer-Assisted
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Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs.

Sensors (Basel, Switzerland) Apr 15, 2024
Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasiv...
Signal Processing, Computer-Assisted Heart Rate Vital Signs Ballistocardiography Male Deep Learning Biosensing Techniques Monitoring, Physiologic Humans Female Aged
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Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets.

Sensors (Basel, Switzerland) Apr 12, 2024
ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. ...
Neural Networks, Computer Algorithms Humans Arrhythmias, Cardiac Deep Learning Heart Rate Electrocardiography Signal Processing, Computer-Assisted
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MA-MIL: Sampling point-level abnormal ECG location method via weakly supervised learning.

Computer methods and programs in biomedicine Apr 9, 2024
BACKGROUND AND OBJECTIVE: Current automatic electrocardiogram (ECG) diagnostic systems could provide classification outcomes but often lack explanations for these results. This limitation hampers their application in clinical diagnoses. Previous supe...
Electrocardiography Signal Processing, Computer-Assisted Supervised Machine Learning Heart Rate Humans Algorithms Databases, Factual
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Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning.

Computers in biology and medicine Apr 9, 2024
Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL methods usually use a stationary distance, such as Euclidean distanc...
Emotions Machine Learning Electroencephalography Algorithms Signal Processing, Computer-Assisted Humans Brain Brain-Computer Interfaces Female Male
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An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network.

Computers in biology and medicine Apr 9, 2024
Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) si...
Male Parkinson Disease Electroencephalography Humans Signal Processing, Computer-Assisted Neural Networks, Computer Brain
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Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes.

Sensors (Basel, Switzerland) Apr 9, 2024
Classification-based myoelectric control has attracted significant interest in recent years, leading to prosthetic hands with advanced functionality, such as multi-grip hands. Thus far, high classification accuracies have been achieved by increasing ...
Electrodes Algorithms Female Young Adult Signal Processing, Computer-Assisted Humans Support Vector Machine Hand Machine Learning Discriminant Analysis Electromyography Adult Male
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