Ambulatory seizure detection

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While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neur ...

Objective: Markup of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG markup is a time-consuming, subjective, and the specialized task where the human r ...

A novel low-complexity method of detecting epileptic seizures from intracranial encephalography (iEEG) signals is presented. In the proposed algorithm, coastline, energy and nonlinear energy features of iEEG signals are extracted in a patient-specific two- ...

Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epileptic seizure detection. Unfortunately, due to differences in data preparati ...

Reliably detecting focal seizures without secondary generalization during daily life activities, chronically, using convenient portable or wearable devices, would offer patients with active epilepsy a number of potential benefits, such as providing more re ...

In this thesis, I focus on monitoring of patients suffering from cardiovascular and neurological diseases through the use of wearable devices. The main diseases considered in this thesis are atrial fibrillation (AF), myocardial infarction (MI), and epileps ...

Epilepsy is a chronic neurological disorder affecting more than 65 million people worldwide and manifested by recurrent unprovoked seizures. The unpredictability of seizures not only degrades the quality of life of the patients, but it can also be life-thr ...

Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can ...

Deep convolutional neural networks have recently emerged as a state-of-the art tool in detection of seizures. Such models offer the ability to extract complex nonlinear representations of an electroencephalogram (EEG) signal which can improve accuracy over ...

A closed-loop system aiming at epilepsy control is proposed in this paper, in which electrical stimulation is triggered upon a decision aggragating different biological signatures of the seizure such as changes in the heart rate, blood flow in the brain, a ...

Ambulatory seizure detection

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Interpreting deep learning models for epileptic seizure detection on EEG signals

Automatic detection of generalized paroxysmal fast activity in interictal EEG using time-frequency analysis

Two-stage Hardware-Friendly Epileptic Seizure Detection Method with a Dynamic Feature Selection

Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection

Noninvasive detection of focal seizures in ambulatory patients

Monitoring of Cardiovascular and Neurological Diseases Using Wearable Devices

EpilepsyGAN: Synthetic Epileptic Brain Activities with Privacy Preservation

Robust Epileptic Seizure Detection on Wearable Systems with Reduced False-Alarm Rate

Noise-Resilient and Interpretable Epileptic Seizure Detection

Implantable IoT System for Closed-Loop Epilepsy Control based on Electrical Neuromodulation