By Hojjat Adeli
According to the authors’ groundbreaking study, computerized EEG-Based analysis of Neurological issues: Inventing the way forward for Neurology provides a learn ideology, a singular multi-paradigm method, and complex computational types for the automatic EEG-based analysis of neurological problems. it really is according to the inventive integration of 3 assorted computing applied sciences and problem-solving paradigms: neural networks, wavelets, and chaos conception. The e-book additionally comprises 3 introductory chapters that familiarize readers with those 3 specific paradigms. After huge learn and the invention of correct mathematical markers, the authors current a technique for epilepsy prognosis and seizure detection that provides a very good accuracy expense of ninety six percentage. They learn know-how that has the aptitude to affect and remodel neurology perform in an important method. They also comprise a few initial effects in the direction of EEG-based analysis of Alzheimer’s disorder. The technique offered within the ebook is mainly flexible and will be tailored and utilized for the prognosis of different mind issues. The senior writer is at the moment extending the recent expertise to analysis of ADHD and autism. A moment contribution made by means of the ebook is its presentation and development of Spiking Neural Networks because the seminal beginning of a extra sensible and believable 3rd iteration neural community.
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Extra info for Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology
However, the precision of this method is limited by the window size, which stays the same for all frequencies. 2 Wavelet Transform In the last two decades, many models have been developed that are based on the wavelet transform. Wavelets can be literally defined as small waves that have limited duration and zero average values. An example wavelet is compared to a sine wave in Fig. 3. They are mathematical functions capable of localizing a function or a set of data in both time and frequency. Wavelets can be stretched or compressed and used to analyze the signal at various levels of resolution.
4 Fisher Iris Classification Problem . . . . . . . . 334 . . . . . . . . . 6 Discussion and Concluding Remarks . . . . . . . 5 EEG Classification Problem 17 The Future 347 Bibliography 349 Index 383 Part I Basic Concepts 1 1 Introduction This book presents a novel approach for automated electroencephalogram (EEG)-based diagnosis of neurological disorders such as epilepsy based on the authors’ ground-breaking research in the past six years. It is divided into four parts.
4 Gradient Computation for Synapses Between a Neuron in the Input or Hidden Layer and a Neuron in the Hidden Layer . . . . . . . . . . . . . 1 Parameter Selection and Weight Initialization . . . . 2 Heuristic Rules for Multi-SpikeProp . . . . . . . 3 XOR Problem 332 . . . . . . . . . . . . . 4 Fisher Iris Classification Problem . . . . . . . . 334 . . . . . . . . . 6 Discussion and Concluding Remarks . . . . . . . 5 EEG Classification Problem 17 The Future 347 Bibliography 349 Index 383 Part I Basic Concepts 1 1 Introduction This book presents a novel approach for automated electroencephalogram (EEG)-based diagnosis of neurological disorders such as epilepsy based on the authors’ ground-breaking research in the past six years.
Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology by Hojjat Adeli