AIMC Topic: Signal Processing, Computer-Assisted

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Fed-MStacking: Heterogeneous Federated Learning With Stacking Misaligned Labels for Abnormal Heart Sound Detection.

IEEE journal of biomedical and health informatics Sep 5, 2024
Ubiquitous sensing has been widely applied in smart healthcare, providing an opportunity for intelligent heart sound auscultation. However, smart devices contain sensitive information, raising user privacy concerns. To this end, federated learning (F...
Heart Sounds Humans Adult Machine Learning Signal Processing, Computer-Assisted Algorithms Heart Auscultation
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Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data.

IEEE journal of biomedical and health informatics Sep 5, 2024
The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires accurate and timely ICU outcome predictions for alleviating the economic and healthcare burdens imposed...
Vital Signs Stochastic Processes Humans Machine Learning Deep Learning Female Algorithms Male Signal Processing, Computer-Assisted Aged Prognosis Critical Care Middle Aged Adult Intensive Care Units
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A Generalisable Heartbeat Classifier Leveraging Self-Supervised Learning for ECG Analysis During Magnetic Resonance Imaging.

IEEE journal of biomedical and health informatics Sep 5, 2024
Electrocardiogram (ECG) is acquired during Magnetic Resonance Imaging (MRI) to monitor patients and synchronize image acquisition with the heart motion. ECG signals are highly distorted during MRI due to the complex electromagnetic environment. Autom...
Humans Algorithms Female Young Adult Signal Processing, Computer-Assisted Supervised Machine Learning Heart Rate Deep Learning Electrocardiography Magnetic Resonance Imaging Adult Male Middle Aged
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SeqAFNet: A Beat-Wise Sequential Neural Network for Atrial Fibrillation Classification in Adhesive Patch-Type Electrocardiographs.

IEEE journal of biomedical and health informatics Sep 5, 2024
Due to their convenience, adhesive patch-type electrocardiographs are commonly used for arrhythmia screening. This study aimed to develop a reliable method that can improve the classification performance of atrial fibrillation (AF) using these device...
Signal Processing, Computer-Assisted Humans Atrial Fibrillation Neural Networks, Computer Deep Learning Algorithms Electrocardiography
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Classification of Three Anesthesia Stages Based on Near-Infrared Spectroscopy Signals.

IEEE journal of biomedical and health informatics Sep 5, 2024
Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynami...
Anesthesia Monitoring, Intraoperative Support Vector Machine Signal Processing, Computer-Assisted Female Humans Male Young Adult Middle Aged Adult Spectroscopy, Near-Infrared
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RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG.

IEEE journal of biomedical and health informatics Sep 5, 2024
INTRODUCTION: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morph...
Humans Algorithms Atrial Fibrillation Deep Learning Electrocardiography Signal Processing, Computer-Assisted
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HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals.

IEEE journal of biomedical and health informatics Sep 5, 2024
Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehen...
Humans Neural Networks, Computer Signal Processing, Computer-Assisted Brain Depression Electroencephalography
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PSEENet: A Pseudo-Siamese Neural Network Incorporating Electroencephalography and Electrooculography Characteristics for Heterogeneous Sleep Staging.

IEEE journal of biomedical and health informatics Sep 5, 2024
Sleep staging plays a critical role in evaluating the quality of sleep. Currently, most studies are either suffering from dramatic performance drops when coping with varying input modalities or unable to handle heterogeneous signals. To handle hetero...
Sleep Stages Electroencephalography Algorithms Signal Processing, Computer-Assisted Female Humans Electrooculography Young Adult Neural Networks, Computer Adult Male Middle Aged
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EEGDepressionNet: A Novel Self Attention-Based Gated DenseNet With Hybrid Heuristic Adopted Mental Depression Detection Model Using EEG Signals.

IEEE journal of biomedical and health informatics Sep 5, 2024
World Health Organization (WHO) has identified depression as a significant contributor to global disability, creating a complex thread in both public and private health. Electroencephalogram (EEG) can accurately reveal the working condition of the hu...
Humans Neural Networks, Computer Electroencephalography Signal Processing, Computer-Assisted Heuristics Algorithms Deep Learning Depression
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Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification.

Computer methods and programs in biomedicine Sep 3, 2024
BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) analysis is crucial in diagnosing cardiovascular diseases (CVDs). It is important to consider both temporal and spatial features in ECG analysis to improve automated CVDs diagnosis. Significant progre...
Deep Learning Electrocardiography Signal Processing, Computer-Assisted Algorithms Humans Databases, Factual Neural Networks, Computer Cardiovascular Diseases
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