AIMC Topic: Signal Processing, Computer-Assisted

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A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.

Journal of medical engineering & technology Feb 14, 2025
Developing a robust and effective technique is crucial for interpreting a user's brainwave signals accurately in the realm of biomedical signal processing. The variability and uncertainty present in EEG patterns over time, compounded by noise, pose n...
Electroencephalography Deep Learning Humans Signal Processing, Computer-Assisted Fuzzy Logic Algorithms Brain-Computer Interfaces Bayes Theorem
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An arrhythmia classification using a deep learning and optimisation-based methodology.

Journal of medical engineering & technology Feb 14, 2025
The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interference. The preprocessed signals ar...
Wavelet Analysis Humans Electrocardiography Algorithms Signal Processing, Computer-Assisted Deep Learning Arrhythmias, Cardiac
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Multitask learning approach for PPG applications: Case studies on signal quality assessment and physiological parameters estimation.

Computers in biology and medicine Feb 12, 2025
Wearable technology has expanded the applications of photoplethysmography (PPG) in remote health monitoring, enabling real-time measurement of various physiological parameters, such as heart rate (HR), heart rate variability (HRV), and respiration ra...
Wearable Electronic Devices Photoplethysmography Signal Processing, Computer-Assisted Heart Rate Adult Respiratory Rate Humans Male Female Deep Learning
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Deep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices.

PloS one Feb 12, 2025
Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachycardia) or too slow (bradycardia), irregular heartbeats (like atrial fibrillation-AF, ventricular fibrillation-VF), or the initiation of heartbeats in ...
Wearable Electronic Devices Photoplethysmography Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Atrial Fibrillation Heart Rate Humans Neural Networks, Computer
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Hybrid attention-CNN model for classification of gait abnormalities using EMG scalogram images.

Journal of medical engineering & technology Feb 12, 2025
This research aimed to develop an algorithm for classifying scalogram images generated from electromyography data of patients with Rheumatoid Arthritis and Prolapsed Intervertebral Disc. Electromyography is valuable for assessing muscle function and ...
Gait Signal Processing, Computer-Assisted Electromyography Humans Neural Networks, Computer Male Middle Aged Arthritis, Rheumatoid Algorithms Female Adult
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A novel method for assessing cycling movement status: an exploratory study integrating deep learning and signal processing technologies.

BMC medical informatics and decision making Feb 11, 2025
This study proposes a deep learning-based motion assessment method that integrates the pose estimation algorithm (Keypoint RCNN) with signal processing techniques, demonstrating its reliability and effectiveness.The reliability and validity of this m...
Male Deep Learning Signal Processing, Computer-Assisted Accelerometry Movement Bicycling Reproducibility of Results Young Adult Adult Algorithms Female Humans
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Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition.

Scientific reports Feb 11, 2025
The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking th...
Deep Learning Electrocardiography Signal Processing, Computer-Assisted Humans Algorithms Animals Astacoidea Arrhythmias, Cardiac Finite Element Analysis Models, Cardiovascular
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Generative adversarial networks with fully connected layers to denoise PPG signals.

Physiological measurement Feb 11, 2025
The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/...
Humans Neural Networks, Computer Signal-To-Noise Ratio Photoplethysmography Generative Adversarial Networks Signal Processing, Computer-Assisted Heart Rate
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BrainForest: Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classification Processor.

IEEE transactions on biomedical circuits and systems Feb 11, 2025
Personalized brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Medical implants that deliver therapeutic stimulation in response to detected seizures have already been deployed for the t...
Seizures Neural Networks, Computer Machine Learning Humans Signal Processing, Computer-Assisted Brain Models, Neurological Electroencephalography Brain-Computer Interfaces Algorithms
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Low-Power and Low-Cost AI Processor With Distributed-Aggregated Classification Architecture for Wearable Epilepsy Seizure Detection.

IEEE transactions on biomedical circuits and systems Feb 11, 2025
Wearable devices with continuous monitoring capabilities are critical for the daily detection of epileptic seizures, as they provide users with accurate and comprehensible analytical results. However, current AI classifiers rely on a two-stage recogn...
Humans Neural Networks, Computer Artificial Intelligence Epilepsy Seizures Algorithms Signal Processing, Computer-Assisted Wearable Electronic Devices Electroencephalography
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