Abstract The vast majority of studies that process and analyze neural signals are conducted on cloud computing resources, which is often necessary for the demanding requirements deep network workloads. However, applications such as epileptic seizure detection stand to benefit from edge devices can securely sensitive medical data in a real-time personalised manner. In this work, we propose novel neuromorphic approach using surrogate gradient-based spiking (SNN), consists ConvLSTM unit. We...

Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring telehealth. One such unmet challenge is rapid accurate epileptic seizure annotation. An automatic approach can provide an alternative way label seizures epilepsy deliver a substitute for inaccurate patient self-reports. Multimodal fusion believed avenue improve performance of AI systems identification. We...

This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by self-supervised learning. Time-series are inherently autocorrelated. By a detection model to generate weak labels on the fly, which concurrently used as targets train time-shifted input stream, this autocorrelation can effectively be harnessed reduce burden of manual labelling. is critical in medical patient monitoring, it enables development...

Abstract Epilepsy is a common neurological disorder that sub-stantially deteriorates patients’ safety and quality of life. Electroencephalogram (EEG) has been the golden-standard technique for diagnosing this brain played an essential role in epilepsy monitoring disease management. It extremely laborious challenging, if not practical, physicians expert humans to annotate all recorded signals, particularly long-term monitoring. The annotation process often involves identifying signal segments...

Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds potential to greatly improve quality life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury death in addition several clinical benefits it may provide patient care hospitals. The challenge forecasting lies within seemingly unpredictable transitions brain dynamics into ictal state. main body computational research on...

Abstract Background Electroencephalogram (EEG) monitoring and objective seizure identification is an essential clinical investigation for some patients with epilepsy. Accurate annotation done through a time-consuming process by EEG specialists. Computer-assisted systems detection currently lack extensive utility due to retrospective, patient-specific, and/or irreproducible studies that result in low sensitivity or high false positives tests. We aim significantly reduce the time resources on...

Abstract Epilepsy is one of the most common severe neurological disorders worldwide. The International League Against (ILAE) define epilepsy as a brain disorder that generates (1) two unprovoked seizures more than 24 hrs apart, or (2) seizure with at least 60% risk recurrence over next ten years. Complete remission has been defined years free last five medication free. This requires cost-effective ambulatory ultra-long term out-patient monitoring solution. practice self-reporting inaccurate....

The majority of studies for automatic epileptic seizure (ictal) detection are based on electroencephalogram (EEG) data, but electrocardiogram (ECG) presents a simpler and more wearable alternative long-term ambulatory monitoring. To assess the performance EEG ECG signals, AI systems offer promising way forward developing high performing models in securing both reasonable sensitivity specificity. There crucial needs these to be developed with clinical relevance inference generalization. In...

Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds potential to greatly improve quality life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury death in addition a long list clinical benefits it may provide patient care hospitals. The challenge forecasting lies within seemingly unpredictable transitions brain dynamics into ictal state. main body computational research...

ABSTRACT A high performance event detection system is all you need for some predictive studies. Here, we present AURA: an daptive forecasting model trained with U nlabeled, R eal-time data using internally generated pproximate labels on-the-fly. By harnessing the correlated nature of time-series data, a pair and prediction models are coupled together such that generates automatically, which then used to train model. AURA relies on several simple principles assumptions: (i)...

Abstract Epilepsy is a prevalent condition characterised by recurrent, unpredictable seizures. The diagnosis of epilepsy surface electroencephalography (EEG), time-consuming and uncomfortable process for patients. seizures using EEG over brief monitoring period has variable success, dependent on patient tolerance seizure frequency. Further, the availability hospital resources, hardware software specifications inherently limit capacity to perform long-term data collection whilst maintaining...

Electroencephalogram (EEG) monitoring and objective seizure identification is an essential clinical investigation for some patients with epilepsy. Accurate annotation done through a time-consuming process by EEG specialists. Computer-assisted systems detection currently lack extensive utility due to retrospective, patient-specific, and/or irreproducible studies that result in low sensitivity or high false positives tests. We aim significantly reduce the time resources on data demonstrating...