A Cloud-Based Seizure Alert System for Epileptic Patients That Uses Higher-Order Statistics

Sareen, Sanjay; Sood, Sandeep K.; Gupta, Sunil Kumar

doi:10.1109/mcse.2016.82

Cited by 21 publications

(11 citation statements)

References 18 publications

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“…Ergonomic issues also include factors like energy efficiency and long battery life: the lack of them forces frequent charging cycles of the devices and some other annoyances. Besides, some examples of devices without lacking in ergonomic issues are found in the main part of the solutions; they are efficient for their purpose, but their use is uncomfortable though: using sensing caps [7,8,31,32], wrongly sized WD [36,41], too many WDs [8], and so on.…”

Section: Remarkable Factorsmentioning

confidence: 99%

“…In EpiCare, a home care platform based on Mobile Cloud Computing to assist epilepsy diagnosis [7], portable EEG are used to detect the seizures in controlled environments. Sareen gathers data from EEC, using MCC and CC for storage and for sending notifications to the relatives and medical staff, including location information [31,32]. The main difference between Sareen and Bajwa is that the latter proposes CC only.…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Remarkable Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An IoT Platform for Epilepsy Monitoring and Supervising

et al. 2017

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“…Logistic regression, Random forests, and SVM. Sareen et al proposed a seizure alert system [6]. They collected data from body sensors via patients' mobile phones with Bluetooth technology.…”

Section: Literature Surveymentioning

confidence: 99%

“…Different CI techniques were trained to classify EEG signals as normal or abnormal [2], [6], [7], [13], [18], [19]. In this work, we used TreeBoost, Random Forests, and SVM classifiers.…”

Section: Computational Intelligence Techniquesmentioning

confidence: 99%

Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands

Bellegdi

Arafat

2017

ijamec

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This paper demonstrates the effectiveness of information fusion at the feature vectors level for automatic detection of epilepsy. Experiments used features ranging from separate EEG frequency band waves to combinations of band waves, in addition to signal energy. We used three classifiers with the feature vectors: TreeBoost, Random Forests, and support vector machines. We carried out experiments using a real life EEG signals data set that is available from the University of Bonn Hospital in Germany. This paper shows the effect of combining together signal energy with different EEG frequency band waves in order to classify epilepsy, and that this combination has computed 97.5% accuracy over using feature vectors with fewer band wave transformations (84-95.5% accuracy), using the TreeBoost algorithm and 10 folds cross validation. This combination computed 99% specificity and 95.5% sensitivity. Furthermore, the paper demonstrates and analyses the effectiveness of using ensemble based tree learning.

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Application Specific Sensor-Cloud: Architectural Model

Bhanumathi

Kalaivanan

2018

Computational Intelligence in Sensor Networks

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A Cloud-Based Seizure Alert System for Epileptic Patients That Uses Higher-Order Statistics

Cited by 21 publications

References 18 publications

An IoT Platform for Epilepsy Monitoring and Supervising

An IoT Platform for Epilepsy Monitoring and Supervising

Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands

Application Specific Sensor-Cloud: Architectural Model

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