Automated classification of EEG signals in brain tumor diagnostics

Karameh, Fadi N.; Dahleh, Munther A.

doi:10.1109/acc.2000.877006

Cited by 24 publications

(8 citation statements)

References 11 publications

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“…Figure 4 Shows the classification steps where the extracted feature values decides whether the tissue is tumor one or not [18]. This can be done by comparing the feature values which extracted already from the ground truth and the feature extracted from the test images.…”

Section: ) Classificationmentioning

confidence: 99%

Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor

et al. 2019

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Now-a-days image processing placed an important role for recognizing various diseases such as breast, lung, and brain tumors in earlier stage for giving the appropriate treatment. Presently, most cancer diagnosis worked according to the visual examination process with effectively. Human visual reviewing of infinitesimal biopsy pictures is exceptionally tedious, subjective, and conflicting due to between and intra-onlooker varieties. In this manner, the malignancy and it's compose will be distinguished in a beginning time for finish treatment and fix. This brain tumor classification system using machine learningbased back propagation neural networks (MLBPNN) causes pathologists to enhance the exactness and proficiency in location of threat and to limit the entomb onlooker variety. Moreover, the technique may assist doctors with analyzing the picture cell by utilizing order and bunching calculations by recoloring qualities of the phones. The different picture preparing steps required for disease location from biopsy pictures incorporate procurement, upgrade, and division; include extraction, picture portrayal, characterization, and basic leadership. In this paper, MLBPNN is analyzed with the help of infra-red sensor imaging technology. Then, the computational multifaceted nature of neural distinguishing proof incredibly diminished when the entire framework is deteriorated into a few subsystems. The features are extracted using fractal dimension algorithm and then the most significant features are selected using multi fractal detection technique to reduce the complexity. This imaging sensor is integrated via wireless infrared imaging sensor which is produced to transmit the tumor warm data to a specialist clinician to screen the wellbeing condition and for helpful control of ultrasound measurements level, especially if there should arise an occurrence of elderly patients living in remote zones.INDEX TERMS Wireless infrared imaging sensor, infra-red sensor, principal component analysis gray level covariance matrix, machine learning based neural networks.

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Section: ) Classificationmentioning

confidence: 99%

Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor

et al. 2019

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“…Advancement is quoted because generally, evolution is not considered to proceed in any intended direction (Dawkins, 2004). While the underlying lower layers are not as evident in recordings due to overshadowing by the higher layers, these lower levels may still occasionally dominate local and global oscillatory activity when the higher levels fail due to dysfunction as in epileptic seizures and brain tumors (Karameh and Dahleh, 2000) Higher layer activity may also be restrained to make way for more primitive homeostatic activity such as sleep activity necessary for reconciling experiences with the fundamental biological self (Knyazev, 2012).…”

Section: Layersmentioning

confidence: 99%

On the Hierarchical Organization of Oscillatory Assemblies: Layered Superimposition and a Global Bioelectric Framework

Jerath¹,

Beveridge²,

Jensen

2019

Front. Hum. Neurosci.

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Bioelectric oscillations occur throughout the nervous system of nearly all animals, revealed to play an important role in various aspects of cognitive activity such as information processing and feature binding. Modern research into this dynamic and intrinsic bioelectric activity of neural cells continues to raise questions regarding their role in consciousness and cognition. In this theoretical article, we assert a novel interpretation of the hierarchical nature of “brain waves” by identifying that the superposition of multiple oscillations varying in frequency corresponds to the superimposing of the contents of consciousness and cognition. In order to describe this isomorphism, we present a layered model of the global functional oscillations of various frequencies which act as a part of a unified metastable continuum described by the Operational Architectonics theory and suggested to be responsible for the emergence of the phenomenal mind. We detail the purposes, functions, and origins of each layer while proposing our main theory that the superimposition of these oscillatory layers mirrors the superimposition of the components of the integrated phenomenal experience as well as of cognition. In contrast to the traditional view that localizations of high and low-frequency activity are spatially distinct, many authors have suggested a hierarchical nature to oscillations. Our theoretical interpretation is founded in four layers which correlate not only in frequency but in evolutionary development. As other authors have done, we explore how these layers correlate to the phenomenology of human experience. Special importance is placed on the most basal layer of slow oscillations in coordinating and grouping all of the other layers. By detailing the isomorphism between the phenomenal and physiologic aspects of how lower frequency layers provide a foundation for higher frequency layers to be organized upon, we provide a further means to elucidate physiological and cognitive mechanisms of mind and for the well-researched outcomes of certain voluntary breathing patterns and meditative practices which modulate the mind and have therapeutic effects for psychiatric and other disorders.

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“…• Monitor alertness, coma and brain death [54,55] • Locate areas of damage following head injuries [56,57], stroke [58,59] or brain haemorrhage [60,61] • Detect Alzheimer's disease [62][63][64][65] and brain tumours [66,67] • Investigate sleep disorders [68,69] and epilepsy [70,71] • Monitor human brain development [72,73] • Measure the depth of anaesthesia [74] • Test the effect of drugs [75,76].…”

Section: Electroencephalogram (Eeg)mentioning

confidence: 99%

From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

Elgendi

Dauwels

Rebsamen

et al. 2014

Neural Computation, Neural Devices, and Neural Prosthesis

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Abstract. In neurofeedback, brain waves are transformed into sounds or music, graphics, and other representations, to provide real-time information on ongoing waves and patterns in the brain. Here we present various forms of neurofeedback, including sonification, sonification in combination with visualization, and at last, immersive neurofeedback, where auditory and visual feedback is provided in a multi-sided immersive environment in which participants are completely surrounded by virtual imagery and 3D sound. Neural feedback may potentially improve the user's (or patient's) ability to control brain activity, the diagnosis of medical conditions, and the rehabilitation of neurological or psychiatric disorders. Several psychological and medical studies have confirmed that virtual immersive activity is enjoyable, stimulating, and can have a healing effect. As an illustration, neurofeedback is generated from electroencephalograms (EEG) of Alzheimer's disease (AD) patients and healthy subjects. The auditory, visual, and immersive representations of Alzheimer's EEG differ substantially from healthy EEG, potentially yielding novel diagnostic tools. Moreover, such alternative representations of AD EEG are natural and intuitive, and hence easily accessible to laymen (AD patients and family members), and can provide insight into the abnormal brainwaves associated with AD.

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Automated classification of EEG signals in brain tumor diagnostics

Cited by 24 publications

References 11 publications

Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor

Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor

On the Hierarchical Organization of Oscillatory Assemblies: Layered Superimposition and a Global Bioelectric Framework

From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

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