Locally Optimized Adaptive Directional Time–Frequency Distributions

Mohammadi, Mokhtar; Pouyan, Ali A.; Khan, Nabeel Ali; Abolghasemi, Vahid

doi:10.1007/s00034-018-0802-z

Cited by 35 publications

(25 citation statements)

References 44 publications

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“…Different domains, such as time, frequency, and timefrequency [8] domains, have been used to analyze EEG signals. Most of them rely on the discriminate features extracted from the signal in the analyzed domain.…”

Section: Related Workmentioning

confidence: 99%

“…Epilepsy is a chronic central nervous system disorder that makes life trouble for more than 50 million people over the world, as reported by the World Health Organization [1]- [5]. It characterizes by a rapid, unpredictable, and temporary change in the electrical activity of the brain able to affect human functionality at all age [6]- [8]. It may be a partial occur in the left or right part of the brain only or could affect both hemispheres of the brain.…”

Section: Introductionmentioning

confidence: 99%

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Epileptic Seizure Detection using Deep Learning Approach

Jaafar

Mohammadi

2019

UHD J SCI TECH

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An epileptic seizure is a sign of abnormal activity in the human brain. Electroencephalogram (EEG) is a standard tool that has been used vastly for detection of seizure activities. Many methods have been developed to help the neurophysiologists to detect the seizure activities with high accuracy. Most of them rely on the features extracted in the time, frequency, or time-frequency domains. The performance of the proposed methods is related to the performance of the features extracted from EEG recordings. Deep neural networks enable learning directly on the data without the domain knowledge needed to construct a feature set. This approach has been hugely successful in almost all machine learning applications. We propose a new framework that also learns directly from the data, without extracting a feature set. We proposed an original deep-learning-based method to classify EEG recordings. The EEG signal is segmented into 4 s segments and used to train the long- and short-term memory network. The trained model is used to discriminate the EEG seizure from the background. The Freiburg EEG dataset is used to assess the performance of the classifier. The 5-fold cross-validation is selected for evaluating the performance of the proposed method. About 97.75% of the accuracy is achieved.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Detection using Deep Learning Approach

Jaafar

Mohammadi

2019

UHD J SCI TECH

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“…In addition, the combination with energy-based transforms, such as Hough, Radon and Inverse Radon transform, has led to efficient IF detectors, designed for linear FM [22][23][24], polynomial FM [25], sinusoidal FM signals [26] and, recently, also for non linear FM MCS [27]. In case of monocomponent signals, as well as MCS with separable components, signal TF distributions (TFDs), for instance spectrogram, Smoothed Wigner Ville Distribution or Adaptive Directional TFD [28,29], reveal IFs as their maxima curve (ridge points). As a consequence, many approaches derive IFs from TFDs ridges [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

A pde-Based Analysis of the Spectrogram Image for Instantaneous Frequency Estimation

2021

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Instantaneous frequency (IF) is a fundamental feature in multicomponent signals analysis and its estimation is required in many practical applications. This goal can be successfully reached for well separated components, while it still is an open problem in case of interfering modes. Most of the methods addressing this issue are parametric, that is, they apply to a specific IF class. Alternative approaches consist of non-parametric time filtering-based procedures, which do not show robustness to destructive interference—the most critical scenario in crossing modes. In this paper, a method for IF curves estimation is proposed. The case of amplitude and frequency modulated two-component signals is addressed by introducing a spectrogram time-frequency evolution law, whose coefficients depend on signal IFs time derivatives, that is, the chirp rates. The problem is then turned into the resolution of a two-dimensional linear system which provides signal chirp rates; IF curves are then obtained by a simple integration. The method is non-parametric and it results quite robust to destructive interference. An estimate of the estimation error, as well as a numerical study concerning method sensitivity and robustness to noise are also provided in the paper.

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“…According to World Health Organization, Epilepsy is an ongoing central nervous system disorder affecting the life of over 50 million individuals around the world [3][4][5][6][7]. It is a fast, capricious, and temporary change in the electrical activity of the brain that influences functions of human beings of all age groups [8][9][10]. It might be a partial occurrence in the left or right hemisphere of the brain or could affect both of them.…”

Section: Introductionmentioning

confidence: 99%

Designing Efficient NoC-Based Neural Network Architectures for Identification of Epileptic Seizure

et al. 2021

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Artificial Neural Networks (ANNs) mirror the analytical functions of human neural networks. The performance of smart healthcare systems has been limited to the increasing size and intricacy of information. Several ANN architectures help in the analysis of EEG signals for the identification of epileptic seizures. However, real-time performance needs to be accurate and very quick. Consequently, it is important to design efficient ANN models without compromising the feasibility of hardware realization. Since, CPUs and GPUs are based on conventional bus-system architectures, processing large complex datasets decreases the efficiency, scalability and versatility of the systems. To counter the bottlenecks of the bus-based architectures, Network-on-Chip has been efficient for complex computations. In this paper, we develop NoC-based feed-forward neural network and convolutional neural network models for the identification of epileptic seizure by analysis of continuously monitored EEG signal. The trained neural network models are mapped onto the Network-on-Chip to increase the throughput, power efficiency, parallelism and scalability of the architecture. The performance of all models is thoroughly explored in terms of throughput, energy, latency and identification accuracy of an epileptic seizure. Keywords Network-on-Chip • Feed-forward neural network • CNN • Epileptic seizure • Classification • Accelerator This article is part of the topical collection "Hardware for AI, Machine Learning and Emerging Electronic Systems" guest edited by Himanshu Thapliyal, Saraju Mohanty and VS Kanchana Bhaaskaran.

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Locally Optimized Adaptive Directional Time–Frequency Distributions

Cited by 35 publications

References 44 publications

Epileptic Seizure Detection using Deep Learning Approach

Epileptic Seizure Detection using Deep Learning Approach

A pde-Based Analysis of the Spectrogram Image for Instantaneous Frequency Estimation

Designing Efficient NoC-Based Neural Network Architectures for Identification of Epileptic Seizure

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