MyEpiPal: Mobile Application for Managing, Monitoring and Predicting Epilepsy Patient

Marzuki, Nur Ayuni; Husain, Wahidah; Shahiri, Amirah Mohamed

doi:10.1007/978-3-319-49073-1_42

Cited by 6 publications

(7 citation statements)

References 5 publications

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“…A large amount of apps have been published more recently especially in the commercial sector for the detection and management of seizures using either the Smartphone sensors or external sensors, for example Epdetec [17] and Myepipal [18] and web logging which facilitates the way a patient records daily information concerning her/his epileptic events, medication, and news, My Epilepsy Diary [19] and Epidiary [20] . Another app attracting attention and recently reviewed in the press is the Alert App by Empatica.…”

Section: A Sensors For Epilepsymentioning

confidence: 99%

Facilitating Personalisation in Epilepsy with an IoT Approach

Mchale¹,

Pereira²

2021

IJACSA

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The premises made in this paper put the future of personalisation in epilepsy into focus, a focus that shifts from a one-size fits all to a focus on the core of the epilepsy patients' individual characteristics. The emerging approach of personalised healthcare is known to be facilitated by the Internet of Things (IoT) and sensor-based IoT devices are in popular demand for healthcare providers due to the constant need for patient monitoring. In epilepsy, the most common and complex patients to deal with correspond to those with multiple strands of epilepsy. These extremely varied kind of patients should be monitored precisely according to their identified key symptoms and specific characteristics then treatment tailored accordingly. Consequently, paradigms are needed to personalise this information. By focusing upon personalised parameters that make epilepsy patients distinct this paper proposes an IoT based Epilepsy monitoring model endorsing a more accurate and refined way of remotely monitoring the 'individual' patient.

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Section: A Sensors For Epilepsymentioning

confidence: 99%

Facilitating Personalisation in Epilepsy with an IoT Approach

Mchale¹,

Pereira²

2021

IJACSA

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“…(Ranganathan et al, 2015) proposed a seizure diary in which patient has to record the seizure details manually. (Marzuki et al, 2016) proposed 'MyEpiPal' in which patients can manually add many details related to Epilepsy. The application is using the inbuilt mobile accelerometer to detect motion during a seizure.…”

Section: Introductionmentioning

confidence: 99%

“…(Ranganathan et al, 2015) proposed a seizure diary for patients to record seizure activity manually. (Marzuki et al, 2016) developed an application, 'MyEpilPal'. It is a self-management and monitoring tool that can support the epilepsy patient and the caregiver.…”

mentioning

confidence: 99%

EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence

Gupta

Ranga

Agrawal

2022

ADCAIJ

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Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly involve the use of anti-seizure drugs for controlling seizures. But in 70% of cases, these drugs are not effective, and surgery is the only solution when the condition worsens. So patients need to take care of themselves while having a seizure and be safe. Wearable electroencephalogram (EEG) devices have come up with the development in medical science and technology. These devices help in the analysis of brain electrical activities. EEG helps in locating the affected cortical region. The most important is that it can predict any seizure in advance on-site. This has resulted in a sudden increase in demand for effective and efficient seizure prediction and diagnosis systems. A novel approach to epileptic seizure prediction and diagnosis system “EpilNet” is proposed in the present paper. It is a one-dimensional (1D) convolution neural network. EpilNet gives the testing accuracy of 79.13% for five classes, leading to a significant increase of about 6-7% compared to related works. The developed Web API helps in bringing EpilNet into practical use. Thus, it is an integrated system for both patients and doctors. The system will help patients prevent injury or accidents and increase the efficiency of the treatment process by doctors in the hospitals.

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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].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, plenty of apps have been published in the corresponding markets for the detection using either the Smartphone sensors or external sensors, EpDetec [51] and MyEpiPal [34], or for the web logging, facilitating the way a patient records daily information concerning her/his epileptic events, medication, and news, My Epilepsy Diary [52], EpiDiary [53], Epilepsy Society [37], and Epi & Me [54]. A reader who is interested should refer to [55] for a performance comparison of epilepsy related apps.…”

Section: Introductionmentioning

confidence: 99%

An IoT Platform for Epilepsy Monitoring and Supervising

et al. 2017

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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.

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MyEpiPal: Mobile Application for Managing, Monitoring and Predicting Epilepsy Patient

Cited by 6 publications

References 5 publications

Facilitating Personalisation in Epilepsy with an IoT Approach

Facilitating Personalisation in Epilepsy with an IoT Approach

EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence

An IoT Platform for Epilepsy Monitoring and Supervising

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