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20192022
most citedAn End-to-End Deep Learning Approach for Epileptic Seizure Prediction

84 citations · 133 across the 6 of their papers we have counts for

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5 papers · 1 filter

eess.SP20221 cited

Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond

Di Wu, Jie Yang, Mohamad Sawan

Machine learning on electromyography (EMG) has recently achieved remarkable success on a variety of tasks, while such success relies heavily on the assumption that the training and…

eess.SP20221 cited

A Compact Online-Learning Spiking Neuromorphic Biosignal Processor

Chaoming Fang, Ziyang Shen, Fengshi Tian +2

Real-time biosignal processing on wearable devices has attracted worldwide attention for its potential in healthcare applications. However, the requirement of low-area, low-power a…

eess.SP20221 cited

An Event-Driven Compressive Neuromorphic System for Cardiac Arrhythmia Detection

Jinbo Chen, Fengshi Tian, Jie Yang +1

Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks. Conventional wearable systems, ho…

eess.SP202246 cited

Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures

Yankun Xu, Jie Yang, Mohamad Sawan

Epilepsy is a chronic neurological disorder affecting 1\% of people worldwide, deep learning (DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for…

eess.SP202184 cited

An End-to-End Deep Learning Approach for Epileptic Seizure Prediction

Yankun Xu, Jie Yang, Shiqi Zhao +2

An accurate seizure prediction system enables early warnings before seizure onset of epileptic patients. It is extremely important for drug-refractory patients. Conventional seizur…