AIMC Topic: Signal Processing, Computer-Assisted

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Domain Agnostic Post-Processing for QRS Detection Using Recurrent Neural Network.

IEEE journal of biomedical and health informatics Aug 7, 2023
Deep-learning-based QRS-detection algorithms often require essential post-processing to refine the output prediction-stream for R-peak localisation. The post-processing involves basic signal-processing tasks including the removal of random noise in t...
Algorithms Signal Processing, Computer-Assisted Neural Networks, Computer
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Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm.

BMC medical informatics and decision making Jul 28, 2023
BACKGROUND: Signal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step for retrieving complete information and extracting signal characteristics for each lead in cardiology clinical practice. However, it is arduous to manuall...
Electrocardiography Algorithms Signal Processing, Computer-Assisted Humans Deep Learning Heart Rate Neural Networks, Computer
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Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System.

Sensors (Basel, Switzerland) Jul 26, 2023
Due to the tremendous growth of the Internet of Things (IoT), sensing technologies, and wearables, the quality of medical services has been enhanced, and it has shifted from standard medical-based health services to real time. Commonly, the sensors c...
Deep Learning Electrocardiography Computers Signal Processing, Computer-Assisted Humans Algorithms Cardiovascular Diseases Arrhythmias, Cardiac
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A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG.

Scientific reports Jul 19, 2023
Arrhythmia detection from ECG is an important area of computational ECG analysis. However, although a large number of public ECG recordings are available, most research uses only few datasets, making it difficult to estimate the generalizability of t...
Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Algorithms Databases, Factual Neural Networks, Computer Humans
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Sex-related patterns in the electroencephalogram and their relevance in machine learning classifiers.

Human brain mapping Jul 17, 2023
Deep learning is increasingly being proposed for detecting neurological and psychiatric diseases from electroencephalogram (EEG) data but the method is prone to inadvertently incorporate biases from training data and exploit illegitimate patterns. Th...
Female Male Neural Networks, Computer Machine Learning Artifacts Electroencephalography Signal Processing, Computer-Assisted Humans
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DeepSeg: Deep Segmental Denoising Neural Network for Seismic Data.

IEEE transactions on neural networks and learning systems Jul 6, 2023
Noise attenuation is a crucial phase in seismic signal processing. Enhancing the signal-to-noise ratio (SNR) of registered seismic signals improves subsequent processing and, eventually, data analysis and interpretation. In this work, a novel noise r...
Electroencephalography Signal Processing, Computer-Assisted Image Processing, Computer-Assisted Magnetic Resonance Imaging Signal-To-Noise Ratio Neural Networks, Computer
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CS-based multi-task learning network for arrhythmia reconstruction and classification using ECG signals.

Physiological measurement Jul 5, 2023
. Although deep learning-based current methods have achieved impressive results in electrocardiograph (ECG) arrhythmia classification issues, they rely on using the original data to identify arrhythmia categories. However, a large amount of data gene...
Humans Signal Processing, Computer-Assisted Arrhythmias, Cardiac Pressure Algorithms Electrocardiography Neural Networks, Computer
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Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning.

Sensors (Basel, Switzerland) Jun 30, 2023
This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is us...
Uncertainty Models, Statistical Machine Learning Optical Devices Signal Processing, Computer-Assisted
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A neuromorphic physiological signal processing system based on VO memristor for next-generation human-machine interface.

Nature communications Jun 21, 2023
Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. However, the explosion of physiological signal data presents challenges for ...
Neural Networks, Computer Seizures Arrhythmias, Cardiac Epilepsy Humans Signal Processing, Computer-Assisted
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Deep-Learning-Based Classification of Digitally Modulated Signals Using Capsule Networks and Cyclic Cumulants.

Sensors (Basel, Switzerland) Jun 20, 2023
This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated ...
Signal Processing, Computer-Assisted Neural Networks, Computer Deep Learning
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