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Applications of artificial intelligence in automatic detection of epileptic seizures using EEG signals: A review
Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a
tedious and time-consuming task that may take several years of manual training due to its …
tedious and time-consuming task that may take several years of manual training due to its …
Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
A wireless and battery-less implant for multimodal closed-loop neuromodulation in small animals
Fully implantable wireless systems for the recording and modulation of neural circuits that do
not require physical tethers or batteries allow for studies that demand the use of …
not require physical tethers or batteries allow for studies that demand the use of …
[HTML][HTML] Machine-learning-based diagnostics of EEG pathology
Abstract Machine learning (ML) methods have the potential to automate clinical EEG
analysis. They can be categorized into feature-based (with handcrafted features), and end-to …
analysis. They can be categorized into feature-based (with handcrafted features), and end-to …
Applied machine learning and artificial intelligence in rheumatology
Abstract Machine learning as a field of artificial intelligence is increasingly applied in
medicine to assist patients and physicians. Growing datasets provide a sound basis with …
medicine to assist patients and physicians. Growing datasets provide a sound basis with …
[HTML][HTML] A recent investigation on detection and classification of epileptic seizure techniques using EEG signal
The benefits of early detection and classification of epileptic seizures in analysis, monitoring
and diagnosis for the realization and actualization of computer-aided devices and recent …
and diagnosis for the realization and actualization of computer-aided devices and recent …
Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting
Objective Seizure forecasting may provide patients with timely warnings to adapt their daily
activities and help clinicians deliver more objective, personalized treatments. Although …
activities and help clinicians deliver more objective, personalized treatments. Although …
From seizure detection to smart and fully embedded seizure prediction engine: A review
Recent review papers have investigated seizure prediction, creating the possibility of
preempting epileptic seizures. Correct seizure prediction can significantly improve the …
preempting epileptic seizures. Correct seizure prediction can significantly improve the …
Edge deep learning for neural implants: a case study of seizure detection and prediction
Objective. Implanted devices providing real-time neural activity classification and control are
increasingly used to treat neurological disorders, such as epilepsy and Parkinson's disease …
increasingly used to treat neurological disorders, such as epilepsy and Parkinson's disease …
LightSeizureNet: A lightweight deep learning model for real-time epileptic seizure detection
The monitoring of epilepsy patients in non-hospital environment is highly desirable, where
ultra-low power wearable seizure detection devices are essential in such a system. The …
ultra-low power wearable seizure detection devices are essential in such a system. The …