AIMC Topic: Signal Processing, Computer-Assisted

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ECG signal feature extraction trends in methods and applications.

Biomedical engineering online Mar 8, 2023
Signal analysis is a domain which is an amalgamation of different processes coming together to form robust pipelines for the automation of data analysis. When applied to the medical world, physiological signals are used. It is becoming increasingly c...
Artificial Intelligence Electrocardiography Signal Processing, Computer-Assisted Machine Learning Algorithms
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Edge computing on TPU for brain implant signal analysis.

Neural networks : the official journal of the International Neural Network Society Feb 28, 2023
The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision off...
Signal Processing, Computer-Assisted Neurons Action Potentials Artificial Intelligence Algorithms Brain
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Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.

Medical & biological engineering & computing Feb 23, 2023
Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG s...
Electroencephalography Signal Processing, Computer-Assisted Algorithms Support Vector Machine Imagination Brain-Computer Interfaces
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Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals.

Computers in biology and medicine Feb 8, 2023
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critical to timely medical treatment to save patients' lives. Routine use of the electrocardiogram (ECG) is the most common method for physicians to assess t...
Neural Networks, Computer Heart Diseases Algorithms Signal Processing, Computer-Assisted Deep Learning Humans Arrhythmias, Cardiac Electrocardiography
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Molecular Computation for Molecular Classification.

Advanced biology Jan 29, 2023
DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or ins...
Neural Networks, Computer DNA Computers, Molecular Signal Processing, Computer-Assisted Logic
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A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge.

Sensors (Basel, Switzerland) Jan 26, 2023
Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requ...
Heart Rate Electrocardiography Neural Networks, Computer Humans Signal Processing, Computer-Assisted Heart Ventricles Algorithms Arrhythmias, Cardiac
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Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning.

Sensors (Basel, Switzerland) Jan 15, 2023
Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly p...
Machine Learning Signal Processing, Computer-Assisted
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Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.

Clinical EEG and neuroscience Jan 5, 2023
Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with...
Algorithms Imagination Brain Humans Discriminant Analysis Signal Processing, Computer-Assisted Neural Networks, Computer Electroencephalography Brain-Computer Interfaces
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Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity.

Scientific reports Jan 5, 2023
Neuroscientific analyses balance between capturing the brain's complexity and expressing that complexity in meaningful and understandable ways. Here we present a novel approach that fully characterizes neural network activity and does so by uniquely ...
Feedback Neural Networks, Computer Signal Processing, Computer-Assisted
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Dual-Modal Information Bottleneck Network for Seizure Detection.

International journal of neural systems Jan 5, 2023
In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimens...
Signal Processing, Computer-Assisted Electroencephalography Seizures Deep Learning Humans Neural Networks, Computer
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