Tailoring SVM Inference for Resource-Efficient ECG-Based Epilepsy Monitors

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Purpose of review: To review recent advances in the field of seizure detection in ambulatory patients with epilepsy. Recent findings: Recent studies have shown that wrist or arm wearable sensors, using 3D-accelerometry, electrodermal activity or photopleth ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

Implanted medical devices (IMDs) have been widely developed to support the monitoring and recording of biological data inside the body or brain. Wirelessly powered IMDs, a subset of implantable electronics, have been proposed to eliminate the limitations r ...

Hyperdimensional (HD) computing is a novel approach to machine learning inspired by neuroscience, which uses vectors in a hyper-dimensional space to represent data and models. This approach has gained significant interest in recent years with applications ...

Epilepsy is one of the most prevalent brain disorders that disrupts the lives of millions worldwide. For patients with drug-resistant seizures, there exist implantable devices capable of monitoring neural activity, promptly triggering neurostimulation to r ...

Epilepsy is a common chronic neurological disorder that causes recurring seizures and affects more than 50 million people worldwide. Implantable medical devices (IMDs) are regarded as effective tools to cure patients who suffer from refractory epilepsy. Se ...

Wearable and unobtrusive monitoring and prediction of epileptic seizures has the potential to significantly increase the life quality of patients, but is still an unreached goal due to challenges of real-time detection and wearable devices design. Hyperdim ...

Long-term monitoring of patients with epilepsy presents a challenging problem from the engineering perspective of real-time detection and wearable devices design. It requires new solutions that allow continuous unobstructed monitoring and reliable detectio ...

Early and accurate detection of epileptic seizures is an extremely important therapeutic goal due to the severity of complications it can prevent. To this end, a low-power machine learning-based seizure detection implemented on an FPGA is proposed in this ...

Objective Long-term automatic detection of focal seizures remains one of the major challenges in epilepsy due to the unacceptably high number of false alarms from state-of-the-art methods. Our aim was to investigate to what extent a new patient-specific ap ...

The epileptic brain is the result of a sequence of events transforming normal neuronal populations into hyperexcitable networks supporting recurrent seizure generation. These modifications are known to induce fundamental alterations of circuit function and ...

Objective: Long-term monitoring of people with epilepsy based on electroencephalography (EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for personalised epilepsy treatment. More specifically, in outpatient settings, ...

Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information ...

An accurate epileptic seizure detector using intracranial electroencephalography (iEEG) recordings, suitable for low-power wearable/implantable applications, is presented. Eleven time-domain features with low hardware complexity are employed in the feature ...

Epilepsy affects more than 40 million people worldwide, constituting one of the most debilitating disorders of the Central Nervous System (CNS). It results from an imbalance in the electrical activity of neurons, which is primarily mediated by calcium ions ...

Recent years have seen growing interest in leveraging deep learning models for monitoring epilepsy patients based on electroencephalographic (EEG) signals. However, these approaches often exhibit poor generalization when applied outside of the setting in w ...

Wearable Health Companions allow the unobtrusive monitoring of patients affected by chronic conditions. In particular, by acquiring and interpreting bio-signals, they enable the detection of acute episodes in cardiac and neurological ailments. Nevertheless ...

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 ...

Epilepsy is one of the most prevalent paroxystic neurological disorders. It is characterized by the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant epilepsy, thus their seizures cannot be controlled by medication. Automati ...

Low-power wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints concerning time and location, on one hand, and fulfilling long-term tracking, on the other hand. In the case of epileptic seizures, as t ...

Tailoring SVM Inference for Resource-Efficient ECG-Based Epilepsy Monitors

Chattez avec Graph Search

Ambulatory seizure detection

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Hyperdimensional computing for biosignal monitoring: Applications for epilepsy detection

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Epileptic Seizure Detection With Patient-Specific Feature and Channel Selection for Low-power Applications

Recent Developments on the Use of Nanomaterials for the Treatment of Epilepsy

M2D2: Maximum-Mean-Discrepancy Decoder for Temporal Localization of Epileptic Brain Activities

INCLASS: Incremental Classification Strategy for Self-Aware Epileptic Seizure Detection

Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

Self-Aware Anomaly-Detection for Epilepsy Monitoring on Low-Power Wearable Electrocardiographic Devices