ReliefF based feature selection and Gradient Squirrel search Algorithm enabled Deep Maxout Network for detection of heart disease
S Balasubramaniam, CV Joe… - … Signal Processing and …, 2024 - Elsevier
Detecting heart disease is challenging in clinical settings, leading to an increase in mortality
rates. Current detection processes often rely on Electrocardiography (ECG) signal analysis …
rates. Current detection processes often rely on Electrocardiography (ECG) signal analysis …
A deep learning approach to automated sleep stages classification using Multi-Modal Signals
Sleep is one of the elements most vital to human life. However, the modern lifestyle
continues to push people to neglect this critical requirement. With a vast majority of people …
continues to push people to neglect this critical requirement. With a vast majority of people …
Multimodal multiclass machine learning model for automated sleep staging based on time series data
SK Satapathy, D Loganathan - SN Computer Science, 2022 - Springer
Quality sleep is one of the integral parts of the human body, which gives proper rest to the
body. It directly promotes and supports maintaining good health, both physically and …
body. It directly promotes and supports maintaining good health, both physically and …
Automated driver drowsiness detection from single-channel eeg signals using convolutional neural networks and transfer learning
A Ghadami, M Mohammadzadeh… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
One of the major causes of road accidents is drowsy driving. Identifying drivers' drowsiness
state is a crucial step in preventing accidents. The state of drowsiness of a driver can be …
state is a crucial step in preventing accidents. The state of drowsiness of a driver can be …
A design of bat-based optimized deep learning model for EEG signal analysis
Depression is a mental illness that negatively affects a person's thinking, action, and feeling.
Thus the rate of depression is identified by analysing Electroencephalogram (EEG) signals …
Thus the rate of depression is identified by analysing Electroencephalogram (EEG) signals …
AutoSleepNet: a multi-signal framework for automated sleep stage classification
SK Satapathy, H Madhani, S Garg… - 2022 IEEE World …, 2022 - ieeexplore.ieee.org
Sleep is one of the most vital yet often underrated components of human health. Sleep
studies are crucial for unearthing various abnormalities associated with sleep, widely …
studies are crucial for unearthing various abnormalities associated with sleep, widely …
Machine learning-empowered sleep staging classification using multi-modality signals
The goal is to enhance an automated sleep staging system's performance by leveraging the
diverse signals captured through multi-modal polysomnography recordings. Three …
diverse signals captured through multi-modal polysomnography recordings. Three …
A deep neural model CNN-LSTM network for automated sleep staging based on a single-channel EEG signal
Sleep plays a vital role in human physiological behaviors. Sleep staging is a critical criterion
for assessing sleep patterns. Therefore, it is essential to develop an automatic sleep staging …
for assessing sleep patterns. Therefore, it is essential to develop an automatic sleep staging …
Tree-based machine learning techniques for automated human sleep stage classification
Background: Sleep disorders pose significant health risks, necessitating accurate
diagnostics. The analysis of polysomnographic data and subsequent sleep stage …
diagnostics. The analysis of polysomnographic data and subsequent sleep stage …
An automated system for sleep staging using EEG brain signals based on a machine learning approach
SK Satapathy, HK Kondaveeti… - 2022 IEEE 19th India …, 2022 - ieeexplore.ieee.org
Correctly classifying the sleep stages is essential for analyzing sleep quality and diagnosing
sleep disorders. This research article aims to explore the classification performance of state …
sleep disorders. This research article aims to explore the classification performance of state …