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| http://dx.doi.org/10.5056/jnm23082 | DOI Listing |
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Exploring pattern-specific components associated with hand gestures through different sEMG measures.
J Neuroeng Rehabil
December 2024
School of Information Science and Technology, Fudan University, Shanghai, 200433, China.
For surface electromyography (sEMG) based human-machine interaction systems, accurately recognizing the users' gesture intent is crucial. However, due to the existence of subject-specific components in sEMG signals, subject-specific models may deteriorate when applied to new users. In this study, we hypothesize that in addition to subject-specific components, sEMG signals also contain pattern-specific components, which is independent of individuals and solely related to gesture patterns.
View Article and Find Full Text PDFAdaptive deep feature representation learning for cross-subject EEG decoding.
BMC Bioinformatics
December 2024
College of Computer and Information Engineering/College of Artificial Intelligence, Nanjing Tech University, Nanjing, 210093, China.
Background: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies.
View Article and Find Full Text PDFAutomated differentiation of wide QRS complex tachycardia using QRS complex polarity.
Commun Med (Lond)
December 2024
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Background: Wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) remains challenging despite numerous 12-lead electrocardiogram (ECG) criteria and algorithms. Automated solutions leveraging computerized ECG interpretation (CEI) measurements and engineered features offer practical ways to improve diagnostic accuracy. We propose automated algorithms based on (i) WCT QRS polarity direction (WCT Polarity Code [WCT-PC]) and (ii) QRS polarity shifts between WCT and baseline ECGs (QRS Polarity Shift [QRS-PS]).
View Article and Find Full Text PDFImplementing an AI algorithm in the clinical setting: a case study for the accuracy paradox.
Eur Radiol
December 2024
Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA.
Objectives: We report our experience implementing an algorithm for the detection of large vessel occlusion (LVO) for suspected stroke in the emergency setting, including its performance, and offer an explanation as to why it was poorly received by radiologists.
Materials And Methods: An algorithm was deployed in the emergency room at a single tertiary care hospital for the detection of LVO on CT angiography (CTA) between September 1st-27th, 2021. A retrospective analysis of the algorithm's accuracy was performed.
Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.
Transl Psychiatry
December 2024
Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany.
Given the heterogeneous nature of attention-deficit/hyperactivity disorder (ADHD) and the absence of established biomarkers, accurate diagnosis and effective treatment remain a challenge in clinical practice. This study investigates the predictive utility of multimodal data, including eye tracking, EEG, actigraphy, and behavioral indices, in differentiating adults with ADHD from healthy individuals. Using a support vector machine model, we analyzed independent training (n = 50) and test (n = 36) samples from two clinically controlled studies.
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