Automated capture of intraoperative adverse events using artificial intelligence: a systematic review and meta-analysis
MB Eppler, AS Sayegh, M Maas, A Venkat… - Journal of Clinical …, 2023 - mdpi.com
Intraoperative adverse events (iAEs) impact the outcomes of surgery, and yet are not
routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have …
routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have …
Application of machine learning in the field of intraoperative neurophysiological monitoring: a narrative review
Intraoperative neurophysiological monitoring (IONM) is being applied to a wide range of
surgical fields as a diagnostic tool to protect patients from neural injuries that may occur …
surgical fields as a diagnostic tool to protect patients from neural injuries that may occur …
MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network
S Mahmud, MS Hossain, MEH Chowdhury… - Neural Computing and …, 2023 - Springer
Electroencephalogram (EEG) signals suffer substantially from motion artifacts when
recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many …
recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many …
[HTML][HTML] Volitional control of upper-limb exoskeleton empowered by EMG sensors and machine learning computing
Processing multiple channels of bioelectrical signals for bionic assistive robot volitional
motion control is still a challenging task due to the interference of systematic noise, artifacts …
motion control is still a challenging task due to the interference of systematic noise, artifacts …
A novel interval type-2 fuzzy classifier based on explainable neural network for surface electromyogram gesture recognition
The existing hand gesture classification research based on surface electromyogram (sEMG)
faces the challenges of low classification accuracy, weak real-time ability, weak robustness …
faces the challenges of low classification accuracy, weak real-time ability, weak robustness …
Cross-modal integration and transfer learning using fuzzy logic techniques for intelligent upper limb prosthesis
The integration and interaction of proprioception and exteroception in the human
multisensory network facilitate high-level cognitive functionalities, such as cross-modal …
multisensory network facilitate high-level cognitive functionalities, such as cross-modal …
A systematic literature review on machine learning algorithms for human status detection
Human status detection (HSD) is important to understand the status of users when
interacting with various systems under different conditions. Recently, although various …
interacting with various systems under different conditions. Recently, although various …
Emg signal classification using reflection coefficients and extreme value machine
Electromyography is a promising approach to the gesture recognition of humans if an
efficient classifier with a high accuracy is available. In this paper, we propose to utilize …
efficient classifier with a high accuracy is available. In this paper, we propose to utilize …
Opportunities and challenges of supervised machine learning for the classification of motor evoked potentials according to muscles
Background Even for an experienced neurophysiologist, it is challenging to look at a single
graph of an unlabeled motor evoked potential (MEP) and identify the corresponding muscle …
graph of an unlabeled motor evoked potential (MEP) and identify the corresponding muscle …
Musculoskeletal joint angle estimation based on isokinetic motor coordination
Y Sheng, J Liu, Z Zhou, H Chen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Motion estimation plays an important role in motor coordination exploration and optimal
control of human-machine interaction. Muscular strength training, such as isokinetic …
control of human-machine interaction. Muscular strength training, such as isokinetic …