IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification

Singh, Karamjit; Malhotra, Jyoteesh

doi:10.1007/s12652-019-01613-7

Cited by 45 publications

(20 citation statements)

References 53 publications

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“…Furthermore, the algorithm has also been widely used for a wide variety of wetland classification, and mapping natural coastal salt marsh vegetation environments (Tian et al 2016;Van Beijma and Comber 2014;Corcoran et al 2013). Drawbacks of the method comprise a comparatively extended processing time and model intricacy, particularly when compared with the other methods (Singh et al 2019).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Muthu

Kirubha

2020

J Ambient Intell Human Comput

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Technology play a substantial function in exemplifying technical potentials that are unconsidered in the past to include intelligent behaviour. Computers in machine learning involve the modelling of intelligent behaviour with the least human mediation. Computational intelligence signifies a set of vital information processing methods for information managing and decision accomplishing. Major critical trials in technical exploration is the supervision of data obtained from various resources. Presently, Scanning Electron Microscopy (SEM) images are categorized as per user's measures, which greatly restrict misuse and reliability, owing for the need in definite classification algorithm. The foremost objective is to evaluate the exactness with each algorithm in terms of performance measures such as accuracy, precision, sensitivity, F-measure, kappa, computational time and confusion matrix. In this work, a recital assessment among three classification algorithms on SEM image dataset was conducted. Experimental results revealed that the Random forest algorithm gives the highest accuracy comparing to other two approach. Hence, the researchers would be able to perform automatic SEM image classification evading the necessity to categorize image and for providing a searchable database to identify a definite group of images.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Muthu

Kirubha

2020

J Ambient Intell Human Comput

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“…It is composed of a group of tree-structured classifiers that are independent and identically distributed random vectors, where each tree casts a single vote for the most popular category [ 46 ]. The algorithmic model applies the extensive approach of bootstrap aggregating, also called bagging, to the tree learners.…”

Section: Methodsmentioning

confidence: 99%

An Intelligent and Energy-Efficient Wireless Body Area Network to Control Coronavirus Outbreak

2021

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The coronaviruses are a deadly family of epidemic viruses that can spread from one individual to another very quickly, infecting masses. The literature on epidemics indicates that the early diagnosis of a coronavirus infection can lead to a reduction in mortality rates. To prevent coronavirus disease 2019 (COVID-19) from spreading, the regular identification and monitoring of infected patients are needed. In this regard, wireless body area networks (WBANs) can be used in conjunction with machine learning and the Internet of Things (IoT) to identify and monitor the human body for health-related information, which in turn can aid in the early diagnosis of diseases. This paper proposes a novel coronavirus-body area network (CoV-BAN) model based on IoT technology as a real-time health monitoring system for the detection of the early stages of coronavirus infection using a number of wearable biosensors to examine the health status of the patient. The proposed CoV-BAN model is tested with five machine learning-based classification methods, including random forest, logistic regression, Naive Bayes, support vector machine and multi-layer perceptron classifiers, to optimize the accuracy of the diagnosis of COVID-19. For the long-term sustainability of the sensor devices, the development of energy-efficient WBAN is critical. To address this issue, a long-range (LoRa)-based IoT program is used to receive biosensor signals from the patient and transmit them to the cloud directly for monitoring. The experimental results indicate that the proposed model using the random forest classifier outperforms models using the other classifiers, with an average accuracy of 88.6%. In addition, power consumption is reduced when LoRa technology is used as a relay node.

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“…The transformation of time-domain EEG signals into frequency domain emphasizes the epileptic spikes in spectral domain [41], which are useful for accurate and speedy prediction of epileptic seizures. Therefore, keeping in view the significance of time-frequency transformation for EEG signals, the present work makes use of fast Fourier transform algorithm (FFT) for converting multichannel time-domain EEG signals into frequency domain.…”

Section: Fast Fourier Transformmentioning

confidence: 99%

Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features

Singh

Malhotra

2022

Complex Intell. Syst.

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Epilepsy is a chronic nervous disorder, which disturbs the normal daily routine of an epileptic patient due to sudden seizure onset. In this era of smart healthcare, automated seizure prediction techniques could assist the patients, their family, and medical personnel to control and manage these seizures. This paper proposes a spectral feature-based two-layer LSTM network model for automatic prediction of epileptic seizures using long-term multichannel EEG signals. This model makes use of spectral power and mean spectrum amplitude features of delta, theta, alpha, beta, and gamma bands of 23-channel EEG spectrum for this task. Initially, the proposed single-layer and two-layer LSTM models have been evaluated for EEG segments having durations in the range of 5–50 s for 24 epileptic subjects, out of which EEG segments of 30 s duration are found to be useful for accurate seizure prediction using two-layer LSTM model. Afterwards, to validate the performance of this classifier, the spectral features of 30 s duration EEG segments are fed to random forest, decision tree, k-nearest neighbour, support vector machine, and naive Bayes classifiers, which are empowered with grid search-based parameter estimation. Finally, the iterative simulation results and comparison with recently published existing techniques firmly reveal that the proposed two-layer LSTM model with EEG spectral features is an effective technique for accurately predicting seizures in real time with an average classification accuracy of 98.14%, average sensitivity of 98.51%, and average specificity of 97.78%, thereby enabling the epileptic patients to have a better quality of life.

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IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification

Cited by 45 publications

References 53 publications

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

An Intelligent and Energy-Efficient Wireless Body Area Network to Control Coronavirus Outbreak

Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features

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