Wavelet Analysis

The Effectiveness of Narrowing the Window size for LD & HD EMG Channels based on Novel Deep Learning Wavelet Scattering Transform Feature Extraction Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2022

The use of the Electromyogram (EMG) signals as a source of control to command externally powered prostheses is often challenged by the signal complexity and non-stationary behavior. Mainly, two factors affect classification accuracy: selecting the op...

Artificial Limbs Wavelet Analysis Deep Learning Electromyography Algorithms

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A Deep Learning Scheme for Detecting Atrial Fibrillation Based on Fusion of Raw and Discrete Wavelet Transformed ECG Features.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2022

Atrial fibrillation is the most common sustained cardiac arrhythmia and the electrocardiogram (ECG) is a powerful non-invasive tool for its clinical diagnosis. Automatic AF detection remains a very challenging task due to the high inter-patient varia...

Humans Electrocardiography Atrial Fibrillation Wavelet Analysis Diagnosis, Computer-Assisted Deep Learning

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[Epilepsy detection and analysis method for specific patient based on data augmentation and deep learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Apr 25, 2022

In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it is difficult to collect data from epileptic seizure, and it is easy to cause over fitting phenomenon ...

Humans Epilepsy Seizures Wavelet Analysis Signal Processing, Computer-Assisted Child Electroencephalography Algorithms Deep Learning

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Segmentation-free Heart Pathology Detection Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021

Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination. The ability to diagnose a patient based on their heart sounds is a rather difficult skill to ma...

Humans Neural Networks, Computer Heart Auscultation Heart Sounds Deep Learning Wavelet Analysis

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[An anesthesia depth computing method study based on wavelet transform and artificial neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Oct 25, 2021

General anesthesia is an essential part of surgery to ensure the safety of patients. Electroencephalogram (EEG) has been widely used in anesthesia depth monitoring for abundant information and the ability of reflecting the brain activity. The paper p...

Electroencephalography Wavelet Analysis Humans Algorithms Anesthesia, General Neural Networks, Computer

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Towards Interpretable Machine Learning in EEG Analysis.

Studies in health technology and informatics Sep 21, 2021

In this paper a machine learning model for automatic detection of abnormalities in electroencephalography (EEG) is dissected into parts, so that the influence of each part on the classification accuracy score can be examined. The most successful setu...

Wavelet Analysis Electroencephalography Signal Processing, Computer-Assisted Algorithms Support Vector Machine Neural Networks, Computer Machine Learning

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Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020

Stress analysis and assessment of affective states of mind using ECG as a physiological signal is a burning research topic in biomedical signal processing. However, existing literature provides only binary assessment of stress, while multiple levels ...

Signal Processing, Computer-Assisted Neural Networks, Computer Wavelet Analysis Electrocardiography

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Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020

Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effective for specific, short windows of time during this evolution of injury. Clinically, we often do not ...

Fuzzy Logic Electroencephalography Humans Sheep Infant, Newborn Biomarkers Pregnancy Neural Networks, Computer Wavelet Analysis Animals Female

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Deep Convolutional Neural Network and Reverse Biorthogonal Wavelet Scalograms for Automatic Identification of High Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020

Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at risk infants at birth. Using pre-clinical data from our fetal sheep models, we have shown that micro...

Sheep Wavelet Analysis Electroencephalography Neural Networks, Computer Hypoxia Pregnancy Animals Female Fuzzy Logic

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Wavelet Spectral Deep-training of Convolutional Neural Networks for Accurate Identification of High-Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020

Early diagnosis and prognosis of babies with signs of hypoxic-ischemic encephalopathy (HIE) is currently limited and requires reliable prognostic biomarkers to identify at risk infants. Using our pre-clinical fetal sheep models, we have demonstrated ...

Hypoxia Animals Neural Networks, Computer Sheep Fuzzy Logic Wavelet Analysis Humans Pregnancy Electroencephalography Female Infant, Newborn

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AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

The Effectiveness of Narrowing the Window size for LD & HD EMG Channels based on Novel Deep Learning Wavelet Scattering Transform Feature Extraction Approach.

A Deep Learning Scheme for Detecting Atrial Fibrillation Based on Fusion of Raw and Discrete Wavelet Transformed ECG Features.

[Epilepsy detection and analysis method for specific patient based on data augmentation and deep learning].

Segmentation-free Heart Pathology Detection Using Deep Learning.

[An anesthesia depth computing method study based on wavelet transform and artificial neural network].

Towards Interpretable Machine Learning in EEG Analysis.

Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal.

Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

Deep Convolutional Neural Network and Reverse Biorthogonal Wavelet Scalograms for Automatic Identification of High Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG.

Wavelet Spectral Deep-training of Convolutional Neural Networks for Accurate Identification of High-Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

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