Application of photoplethysmography signals for healthcare systems: An in-depth review
Background and objectives Photoplethysmography (PPG) is a device that measures the
amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn …
amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn …
[HTML][HTML] Application of deep learning models for automated identification of Parkinson's disease: A review (2011–2021)
Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting
over 6 million people globally. Although there are symptomatic treatments that can increase …
over 6 million people globally. Although there are symptomatic treatments that can increase …
Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals
Abstract The number of Major Depressive Disorder (MDD) patients is rising rapidly these
days following the incidence of COVID‐19 pandemic. It is challenging to detect MDD …
days following the incidence of COVID‐19 pandemic. It is challenging to detect MDD …
Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works
P Moridian, A Shoeibi, M Khodatars… - … : Data Mining and …, 2022 - Wiley Online Library
Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep
apnea may last for a few seconds and happen for many while sleeping. This reduction in …
apnea may last for a few seconds and happen for many while sleeping. This reduction in …
[HTML][HTML] GaborPDNet: Gabor transformation and deep neural network for Parkinson's disease detection using EEG signals
Parkinson's disease (PD) is globally the most common neurodegenerative movement
disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the …
disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the …
Automatic sleep stage classification: From classical machine learning methods to deep learning
RN Sekkal, F Bereksi-Reguig… - … Signal Processing and …, 2022 - Elsevier
Background and objectives The classification of sleep stages is a preliminary exam that
contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time …
contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time …
Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals
M Sharma, HS Dhiman, UR Acharya - Computers in biology and medicine, 2021 - Elsevier
Sleep is a fundamental human physiological activity required for adequate working of the
human body. Sleep disorders such as sleep movement disorders, nocturnal front lobe …
human body. Sleep disorders such as sleep movement disorders, nocturnal front lobe …
Automated identification of insomnia using optimal bi-orthogonal wavelet transform technique with single-channel EEG signals
Nowadays, sleep studies have gained a lot of attention from researchers due to the
immense importance of quality sleep. Human beings spend nearly one-third of their lives in …
immense importance of quality sleep. Human beings spend nearly one-third of their lives in …
[HTML][HTML] Automatic sleep-stage scoring in healthy and sleep disorder patients using optimal wavelet filter bank technique with EEG signals
Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep
related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The …
related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The …
Towards interpretable sleep stage classification using cross-modal transformers
J Pradeepkumar, M Anandakumar… - … on Neural Systems …, 2024 - ieeexplore.ieee.org
Accurate sleep stage classification is significant for sleep health assessment. In recent
years, several machine-learning based sleep staging algorithms have been developed, and …
years, several machine-learning based sleep staging algorithms have been developed, and …