An IoT Platform for Epilepsy Monitoring and Supervising

Vergara, Paula M.; Cal, Enrique de la; Villar, José R.; González, Víctor; Sedano, Javier

doi:10.1155/2017/6043069

Cited by 53 publications

(36 citation statements)

References 58 publications

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“…The result was stated that the performance was good by using wearable EEG. [9] This research achieved an in-depth analysis of the main factors and provides the solutions for tonic-clonic epileptic detection and monitoring. [5] Instead of accelerometer, the use of other combination of sensors like heart rate, oxygen Instead of accelerometer, the use of other combination of sensors like heart rate, oxygen saturation, or muscle twitches/signals were provided good accuracy of result.…”

Section: Literature Review On Iot Based Seizure Detection Work Inmentioning

confidence: 99%

Epilepsy Seizure Detection and Prediction Based on DeviceHive

Nanthini*,

Kavitha,

Sivabalaselvamani

et al. 2019

IJRTE

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— Unpredictable nature of epilepsy, patients only has more need of awareness about precautions and how to handle the occurrence. Epilepsy belongs to have a recurrent seizures tendency throughout the life. Seizure may happen due to number of reasons like tumor, head injury, pregnancy time, genetic etc. It can be curable with proper diagnosis, incurable but controllable with lifelong medication and remaining are uncontrollable that leads to death. Recording of alert symptoms like auras, prodromes and precipitant factors are helped to self-alert the patient, create positive impact on quality of life and increase the efficacy of treatments. The need of enhancing early seizure detection and developing wearable monitoring product with low cost is used to create fear free environment among the affected people. In this connection, my proposed work reviewed on existing and currently available IOT based seizure detection and alert systems feasibility.

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Section: Literature Review On Iot Based Seizure Detection Work Inmentioning

confidence: 99%

Epilepsy Seizure Detection and Prediction Based on DeviceHive

Nanthini*,

Kavitha,

Sivabalaselvamani

et al. 2019

IJRTE

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“…When consider the world population there are about 65 million people approximately affected with seizure and it dramatically impact on the patient's quality of life, the health system budget, as well as on the professional development and social behaviour [2]. In order to treat patients, the data should be gathered in everyday life while, allowing the patient to freely do their desired activities.…”

Section: Introductionmentioning

confidence: 99%

“…In order to treat patients, the data should be gathered in everyday life while, allowing the patient to freely do their desired activities. Yet it is very difficult to adapt to analysis and procedures according to unconstrained world [2]. It is very important that the solution is easy to wear and unobtrusive since, better the wearable conditions, lessen the chances of finding a suitable excuse for not using the solution [2].…”

Section: Introductionmentioning

confidence: 99%

“…Yet it is very difficult to adapt to analysis and procedures according to unconstrained world [2]. It is very important that the solution is easy to wear and unobtrusive since, better the wearable conditions, lessen the chances of finding a suitable excuse for not using the solution [2]. Most of devices available on the market are mainly designed for the purpose of alarming caregivers, not for seizure recording where seizure logs could significantly improve the quality of medical care and more optimal treatment to the disease where contribute to a better quality of patient's life [3].…”

Section: Introductionmentioning

confidence: 99%

“…In order to keep track of medical records of patient, there is a database with patient's personal information. Whenever there is a possibility for a seizure, the system is capable of making an emergency notification to make attention while, sharing those data to the medical staff [2] [7]. The seizure detection mechanism and the notification process activate immediately.…”

Section: Introductionmentioning

confidence: 99%

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SeizeIT: SEIZURE victims are no longer leashed

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Ubeysingha

Imbulana

et al. 2019

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Seizure is considered to be one of the severe and most common type of neurological disorders. Despite the availability of numerous anti-seizure drugs, it is often difficult to control the disease completely and effectively. Lack of close supervision and failure in providing urgent medical care during and after seizure episodes, leads to serious injuries or even death. On the other hand, the use of wireless sensor networks in everyday applications have rapidly increased due to decreased technology costs and improved product reliability. Therefore, developing a wearable device to monitor seizure may complete the anamnesis, help medical staff in diagnosing and acute treatment while preventing seizure related accidents. There are number of seizure detection systems available in the market. Still their performance is far from perfect. This paper explores an application of biomedical wireless sensor networks, which attempts to monitor patients in a completely non-invasive and non-intrusive manner. It describes a wearable device together with seizure prediction and alerting system, which is designed to address some issues with seizure detection systems in the market. Its functional block diagram and operating modes are detailed. Possible application areas of the device are also discussed.

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An Autonomous Fallers Monitoring Kit: Release 0.0

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et al. 2020

Advances in Intelligent Systems and Computing

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Fall is one of the main causes of the reduction of the Quality of Live in Elderly, since it has a high impact in mortality rate as well as in the probability of different disabilities like Fractures, head injuries, etc, and mainly a loss of confidence. Thus, a monitoring instrument to follow elderly activity is required and the most common non-invasive and easy-to-use are based on wearable devices. This work presents our first prototype of an autonomous, low-cost and easy-to-use elderly activity monitoring kit suitable to be deployed in nursery houses. The prototype is composed of: i) our own data analytics web application deployed in a low cost server located in our laboratory (SERVER) and ii) a transparent plastic case with a set of 10 commercial smartbands (WD), their respective charging docks, one NUC (Next Unit of Computing) as data gateway, one Wi-Fi router and one LTE router. Concerning the case, we can state that each WD has built-in a 3DACC (3DA), a Gyroscope (GY) and a wrist Heart Rate sensor (HR). Besides each WD is running our own software instead the pre-built commercial one in order to achieve two issues: recording high frequency data (1Hz for HR and 10Hz for 3DA and GY) and optimize the battery life (18 hours with 10Hz continuous sensors recording). Finally, a first trial of the presented kit is being carried out in a Nursery House in Burgos (Spain), and the participants enrolled criteria have been designed by a gerontologist from the Diputación de Burgos (Spain).

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An IoT Platform for Epilepsy Monitoring and Supervising

Cited by 53 publications

References 58 publications

Epilepsy Seizure Detection and Prediction Based on DeviceHive

Epilepsy Seizure Detection and Prediction Based on DeviceHive

SeizeIT: SEIZURE victims are no longer leashed

An Autonomous Fallers Monitoring Kit: Release 0.0

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