[HTML][HTML] Machine learning-based data-driven fault detection/diagnosis of lithium-ion battery: A critical review

A Samanta, S Chowdhuri, SS Williamson - Electronics, 2021 - mdpi.com
Fault detection/diagnosis has become a crucial function of the battery management system
(BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated …

A review of automated sleep stage scoring based on physiological signals for the new millennia

O Faust, H Razaghi, R Barika, EJ Ciaccio… - Computer methods and …, 2019 - Elsevier
Abstract Background and Objective Sleep is an important part of our life. That importance is
highlighted by the multitude of health problems which result from sleep disorders. Detecting …

An attention-based deep learning approach for sleep stage classification with single-channel EEG

E Eldele, Z Chen, C Liu, M Wu… - … on Neural Systems …, 2021 - ieeexplore.ieee.org
Automatic sleep stage mymargin classification is of great importance to measure sleep
quality. In this paper, we propose a novel attention-based deep learning architecture called …

Joint classification and prediction CNN framework for automatic sleep stage classification

H Phan, F Andreotti, N Cooray… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Correctly identifying sleep stages is important in diagnosing and treating sleep disorders.
This paper proposes a joint classification-and-prediction framework based on convolutional …

Multi-view spatial-temporal graph convolutional networks with domain generalization for sleep stage classification

Z Jia, Y Lin, J Wang, X Ning, Y He… - … on Neural Systems …, 2021 - ieeexplore.ieee.org
Sleep stage classification is essential for sleep assessment and disease diagnosis.
Although previous attempts to classify sleep stages have achieved high classification …

A deep learning model for automated sleep stages classification using PSG signals

O Yildirim, UB Baloglu, UR Acharya - International journal of …, 2019 - mdpi.com
Sleep disorder is a symptom of many neurological diseases that may significantly affect the
quality of daily life. Traditional methods are time-consuming and involve the manual scoring …

Automated skeletal classification with lateral cephalometry based on artificial intelligence

HJ Yu, SR Cho, MJ Kim, WH Kim… - Journal of Dental …, 2020 - journals.sagepub.com
Lateral cephalometry has been widely used for skeletal classification in orthodontic
diagnosis and treatment planning. However, this conventional system, requiring manual …

[HTML][HTML] Physiology, sleep stages

AK Patel, V Reddy, KR Shumway, JF Araujo - StatPearls [Internet], 2022 - ncbi.nlm.nih.gov
The human body cycles through two phases of sleep,(1) rapid eye movement (REM) and (2)
non-rapid eye movement (NREM) sleep, which is further divided into three stages, N1-N3 …

Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement

D Jiang, Y Lu, MA Yu, W Yuanyuan - Expert Systems with Applications, 2019 - Elsevier
Sleep stage classification is a most important process in sleep scoring which is used to
evaluate sleep quality and diagnose sleep-related diseases. Compared to complex sleep …

[HTML][HTML] Internal short circuit detection in Li-ion batteries using supervised machine learning

A Naha, A Khandelwal, S Agarwal, P Tagade… - Scientific reports, 2020 - nature.com
With the proliferation of Li-ion batteries in smart phones, safety is the main concern and an
on-line detection of battery faults is much wanting. Internal short circuit is a very critical issue …