Detection and prediction of seizures using a wrist-based wearable platform

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Seizure is a neurological disorder that occurs due to sudden electrical activity in the brain. A number of physiological signals can be used to detect seizure. This paper describes a wrist-based platform that uses a combination of physiological signals and other parameters such as irregular, uncoordinated motion for the detection of seizure. The proposed device is capable of constant evolution by updating of its algorithms, leading to a device capable of predicting seizures before their actual onset.

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... For instance, not all the platforms store the data stream; on the contrary, the majority of them process the data as it comes in order to generate the alarms and delete them afterwards: these solutions rely on previous research stages that would have been performed to obtain the deployed models [6,8,9,[34][35][36][37][38]. On the other hand, there are solutions where the gathered data is stored for data analytics [39,40] or even for future use [41][42][43][44]. Finally, complete CC solutions have been also tackled in the literature, including not only the data storage and visualization but also the modelling and classification of the current state of affairs [7,31,32,[45][46][47][48]. ...
... ECG for detecting epileptic seizures while sleeping has been reported in [39], while EEG hats have been also effectively used in seizure detection [41,50]; however their ergonomic characteristics make them difficult to use. Further studies in this context of epileptic seizure detection include BSN sending information to a computer in controlled environments [42,43]; ACM, temperature, and skin humidity data gathered to computers [40]; ACM and HR [5]; or the use of thresholds as a detection method when measuring the HR linked to a Smartphone [38]. ...
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Epilepsy is a chronic neurological disorder with several different types of seizures, some of them characterized by involuntary recurrent convulsions, which have a great impact on the everyday life of the patients. Several solutions have been proposed in the literature to detect this type of seizures and to monitor the patient; however, these approaches lack in ergonomic issues and in the suitable integration with the health system. This research makes an in-depth analysis of the main factors that an epileptic detection and monitoring tool should accomplish. Furthermore, we introduce the architecture for a specific epilepsy detection and monitoring platform, fulfilling these factors. Special attention has been given to the part of the system the patient should wear, providing details of this part of the platform. Finally, a partial implementation has been deployed and several tests have been proposed and carried out in order to make some design decisions.
... Seizure is a condition that triggers a sudden surge of electrical activity in the brain due to complex chemical changes that occur in nerve cells [1]. Its condition can be seen with naked eye as repetitive convulsions, muscle spasms, loss of consciousness, abnormal sensations, emotions or (motor) behaviour. ...
... Sometimes the cause for the seizure is not clear or there is no cause. Most common causes are fever, brain injury, and abnormal levels of sodium or glucose in the blood, brain infection including meningitis, brain tumour, head injury, heart disease, stroke, malignant hypertension, and withdrawal from alcohol [1]. ...
... There have been some positive developments in seizure prediction using data from wearable devices, although current studies remain limited with regard to accuracy and clinical validity. 98,99 Epilepsy seizure diaries have also shown promise as a source of diverse clinical insights, 45 including as a measure of seizure risk. 100 Despite well-documented inaccuracies and limitations inherent to seizure diaries, 101 self-reported events remain the standard data source for medical practice and clinical trials in epilepsy. ...
Epilepsy is a unique neurologic condition characterized by recurrent seizures, where causes, underlying biomarkers, triggers, and patterns differ across individuals. The unpredictability of seizures can heighten fear and anxiety in people with epilepsy, making it difficult to take part in day‐to‐day activities. Epilepsy researchers have prioritized developing seizure prediction algorithms to combat episodic seizures for decades, but the utility and effectiveness of prediction algorithms has not been investigated thoroughly in clinical settings. In contrast, seizure forecasts, which theoretically provide the probability of a seizure at any time (as opposed to predicting the next seizure occurrence), may be more feasible. Many advances have been made over the past decade in the field of seizure forecasting, including improvements in algorithms as a result of machine learning and exploration of non–EEG‐based measures of seizure susceptibility, such as physiological biomarkers, behavioral changes, environmental drivers, and cyclic seizure patterns. For example, recent work investigating periodicities in individual seizure patterns has determined that more than 90% of people have circadian rhythms in their seizures, and many also experience multiday, weekly, or longer cycles. Other potential indicators of seizure susceptibility include stress levels, heart rate, and sleep quality, all of which have the potential to be captured noninvasively over long time scales. There are many possible applications of a seizure‐forecasting device, including improving quality of life for people with epilepsy, guiding treatment plans and medication titration, optimizing presurgical monitoring, and focusing scientific research. To realize this potential, it is vital to better understand the user requirements of a seizure‐forecasting device, continue to advance forecasting algorithms, and design clear guidelines for prospective clinical trials of seizure forecasting.
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