A Robust and Fast Seizure Detector for IoT Edge

Sayeed, Abu; Mohanty, Saraju P.; Kougianos, Elias; Yanambaka, Venkata P.; Zaveri, Hitten P.

doi:10.1109/ises.2018.00042

Cited by 17 publications

(12 citation statements)

References 21 publications

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“…In [151], Devarajan & Ravi work on a fog computing based Parkinson detection system using a persons speech. Moreover, an edge computing system is presented in [152] which utilizes EEG signals to determine seizures in patients. [133] Homogeneous (Accelerometer) CNN [134] RNN (LSTM) [153] Fog Edge Heterogeneous (Accelerometer, Gyroscope, Magnetometer) CNN [137] Fog RF [138] Edge Heterogeneous (Accelerometer and Gyroscope) SVM [136] Patient health monitoring DT [139] Cloud Classification-Recommendation about diet etc.…”

Section: Smart Healthmentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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Section: Smart Healthmentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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“…4 is a means of continuous storage of EEG data streams from the patient, which can be useful for future patient-specific studies or further research on seizure detection and prediction as a whole. The edge-IoT epileptic seizure detector that was proposed in [34] only notified the physician, who is usually far away, about the subject's seizure state. However, a subject in a seizure crisis may need help immediately, if injury or death are to be prevented.…”

Section: Proposed Edge Computing Paradigm For Seizure Detectionmentioning

confidence: 99%

“…It was used in conjunction with linear discriminant analysis (LDA) for dimension reduction. Other machine learning algorithms which have also been used for seizure detection include the naive Bayes classifier [34], nearest neighbor ( NN) classifier [36,45], Artificial Neural Network (ANN) [18,27], Relevance Vector Machine (RVM) [48], Decision Tree [49] and Deep Neural Network [7]. The emphasis in most of these cases is performance in terms of accuracy or related metrics and not suitability for edge computation.…”

Section: Seizure State Classification Algorithmmentioning

confidence: 99%

Real-Time Automatic Seizure Detection Using Ordinary Kriging Method in an Edge-IoMT Computing Paradigm

et al. 2020

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Epilepsy is one of the leading neurological diseases in the world, affecting approximately 70 million of the world's population and often results in early mortality if not properly managed. The primary purpose of seizure detection is to reduce threat to life in the event of a seizure crisis. Previous efforts in the literature concentrate mostly on performance based on accuracy and other similar metrics. However, there is a short time lapse between the onset of a seizure attack and a potential injury that could claim the life of the patient. Therefore, there is the need for a more time-sensitive seizure detection model. We hereby propose a real-time seizure detection model in an edge computing paradigm using the ordinary kriging method, relying on the premise that the brain can be modeled as a three-dimensional spatial object, similar to a geographical panorama where kriging excels. Fractal dimensional features were extracted from patients' electroencephalogram (EEG) signals and then classified using the proposed ordinary kriging model. The proposed model achieves a training accuracy of 99.4% and a perfect sensitivity, specificity, precision and testing accuracy. Hardware implementation in an edge computing environment results in a mean detection latency of 0.85 s. To the best of the authors' knowledge, this is the first work that uses the kriging method for early detection of seizure.

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“…Due to increased population, traditional healthcare systems are not able to provide necessary services to everyone. Smart healthcare utilizes the limited resource in an efficient way to fulfill everyone's needs [30], [31]. The IoMT in smart healthcare is an integration of universal communication and connectivity where all the necessary components can be connected together [9], [6].…”

Section: The Proposed Novel Drug Delivery System In the Internet Of Mmentioning

confidence: 99%

“…The EEG data and dosage information are sent to open cloud storage, while the system concurrently receives dosage information prescribed by the physician. Both patients and medical professionals have access to the IoT cloud as well as the database using a REST API [30]. Fig.…”

Section: Consumer Electronics (Ce) Proof Of Concept Of the Proposed Cmentioning

confidence: 99%

eSeiz: An Edge-Device for Accurate Seizure Detection for Smart Healthcare

Sayeed

Mohanty

Kougianos

et al. 2019

IEEE Trans. Consumer Electron.

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Epilepsy is one of the most common neurological disorders affecting up to 1% of the world's population and approximately 2.5 million people in the United States. Seizures in more than 30% of epilepsy patients are refractory to anti-epileptic drugs. An important biomedical research effort is focused on the development of an energy efficient implantable device for the real-time control of seizures. In this paper we propose an Internet of Medical Things (IoMT) based automated seizure detection and drug delivery system (DDS) for the control of seizures. The proposed system will detect seizures and inject a fast acting anti-convulsant drug at the onset to suppress seizure progression. The drug injection is performed in two stages. Initially, the seizure detector detects the seizure from the electroencephalography (EEG) signal using a hyper-synchronous signal detection circuit and a signal rejection algorithm (SRA). In the second stage, the drug is released in the seizure onset area upon seizure detection. The design was validated using a system-level simulation and consumer electronics proof of concept. The proposed seizure detector reports a sensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitry leads to a considerable reduction of power consumption compared to previous approaches. The proposed approach can be generalized to other sensor modalities and the use of both wearable and implantable solutions, or a combination of the two.

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A Robust and Fast Seizure Detector for IoT Edge

Cited by 17 publications

References 21 publications

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Real-Time Automatic Seizure Detection Using Ordinary Kriging Method in an Edge-IoMT Computing Paradigm

eSeiz: An Edge-Device for Accurate Seizure Detection for Smart Healthcare

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