Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
A recent investigation on detection and classification of epileptic seizure techniques using EEG signal
The benefits of early detection and classification of epileptic seizures in analysis, monitoring
and diagnosis for the realization and actualization of computer-aided devices and recent …
and diagnosis for the realization and actualization of computer-aided devices and recent …
[BOOK][B] Time-frequency analysis techniques and their applications
RB Pachori - 2023 - taylorfrancis.com
Most of the real-life signals are non-stationary in nature. The examples of such signals
include biomedical signals, communication signals, speech, earthquake signals, vibration …
include biomedical signals, communication signals, speech, earthquake signals, vibration …
Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural networks
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …
EEG seizure detection: concepts, techniques, challenges, and future trends
A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity
becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of …
becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of …
Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features
Epilepsy is a brain disorder disease that affects people's quality of life.
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT
This project aimed to develop and evaluate a fast and fully-automated deep-learning
method applying convolutional neural networks with deep supervision (CNN-DS) for …
method applying convolutional neural networks with deep supervision (CNN-DS) for …
Detection of focal and non-focal electroencephalogram signals using fast Walsh-Hadamard transform and artificial neural network
The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the
epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the …
epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the …
ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects
A Agrawal, A Chauhan, MK Shetty, MD Gupta… - Computers in Biology …, 2022 - Elsevier
Objective Studies showed that many COVID-19 survivors develop sub-clinical to clinical
heart damage, even if subjects did not have underlying heart disease before COVID. Since …
heart damage, even if subjects did not have underlying heart disease before COVID. Since …
Exploiting feature selection and neural network techniques for identification of focal and nonfocal EEG signals in TQWT domain
For drug resistance patients, removal of a portion of the brain as a cause of epileptic
seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy …
seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy …