Titlebook: Biomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders; M. Murugappan,Yuvaraj Rajamanickam Book 2022 The Editor(s) (

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Hilbert Huang Transform (HHT) Analysis of Heart Rate Variability (HRV) in Recognition of Emotion inSD prior to their onset. As a result, developing an alarm system assists caregivers, doctors and therapists in preventing such aggressive behaviours before they occur. Only a few studies have studied the physiological markers in ASD children and adults. The Pan-Tompkins equation was used to determin

congenial

Detection of Tonic-Clonic Seizures Using Scalp EEG of Spectral Moments,s of motor, sensory, or psychic dysfunction. Tonic-clonic seizure (TCSZ), a type of electroclinical seizure, has a high risk of injury due to the convulsions. Video EEG is an essential tool, employed in the hospitals for the identification of those seizures. This is not practical outside the clinica

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A Novel Parametric Nonstationary Signal Model for EEG Signals and Its Application in Epileptic Seizification. The EEG signals are considered as signatures of neural activities and represent the electrical activity inside the brain. The EEG signals are considered predominantly nonlinear and nonstationary in nature; thus, obtaining meaningful inferences from the EEG signal has been a strenuous task

Fibroid

Biomedical Signal Analysis Using Entropy Measures: A Case Study of Motor Imaginary BCI in End Usersous entropy measures have been developed from both theoretical and experimental perspectives. Approximate entropy and Shannon entropy are basic entropy measures that have been successfully applied to various biomedical data. However, these measures have some drawbacks, such as sensitivity to data le

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aspersion

Catalogic Systematic Literature Review of Hardware-Accelerated Neurodiagnostic Systems,ed to automate the final diagnosis step but for the design of sensors, the preprocessing unit, and the processing unit as well. Today, it is an essential requirement that these CAD systems have low latency and low power consumption, which is not possible using a traditional software-based system tha

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Wearable Real-Time Epileptic Seizure Detection and Warning System,ent losing the capability of controlling his/her actions. Besides being prone to injuries, losing consciousness, and control of the body, patients have a higher risk of experiencing sudden unexpected death in epilepsy (SUDEP). Therefore, continuous multimodal monitoring using electrodermal activity