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Separation of responsive and unresponsive patients under clinical conditions: comparison of symbolic transfer entropy and permutation entropy
Electroencephalogram (EEG)-based monitoring during general anesthesia may help prevent harmful effects of high or low doses of general anesthetics....
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An in-depth analysis of parameter settings and probability distributions of specific ordinal patterns in the Shannon permutation entropy during different states of consciousness in humans
As electrical activity in the brain has complex and dynamic properties, the complexity measure permutation entropy (PeEn) has proven itself to...
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Spatio-temporal modeling of human leptospirosis prevalence using the maximum entropy model
BackgroundLeptospirosis, a zoonotic disease, stands as one of the prevailing health issues in some tropical areas of Iran. Over a decade, its...
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Interpretable prediction of 3-year all-cause mortality in patients with chronic heart failure based on machine learning
BackgroundThe goal of this study was to assess the effectiveness of machine learning models and create an interpretable machine learning model that...
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Quantitative electroencephalogram in term neonates under different sleep states
Electroencephalogram (EEG) can be used to assess depth of consciousness, but interpreting EEG can be challenging, especially in neonates whose EEG...
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Electrocerebral Signature of Cardiac Death
BackgroundElectroencephalography (EEG) findings following cardiovascular collapse in death are uncertain. We aimed to characterize EEG changes...
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Photoplethysmography-derived approximate entropy and sample entropy as measures of analgesia depth during propofol–remifentanil anesthesia
The ability to monitor the physiological effect of the analgesic agent is of interest in clinical practice. Nonstationary changes would appear in...
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Resting-State EEG Signature of Early Consciousness Recovery in Comatose Patients with Traumatic Brain Injury
BackgroundResting-state electroencephalography (rsEEG) is usually obtained to assess seizures in comatose patients with traumatic brain injury (TBI)....
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Identifying key factors for predicting O6-Methylguanine-DNA methyltransferase status in adult patients with diffuse glioma: a multimodal analysis of demographics, radiomics, and MRI by variable Vision Transformer
PurposeThis study aimed to perform multimodal analysis by vision transformer (vViT) in predicting O6-methylguanine-DNA methyl transferase (MGMT)...
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Aberrant resting-state co-activation network dynamics in major depressive disorder
Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks....
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Functional brain network features specify DBS outcome for patients with treatment resistant depression
Deep brain stimulation (DBS) has shown therapeutic benefits for treatment resistant depression (TRD). Stimulation of the subcallosal cingulate gyrus...
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Predicting EGFR Status After Radical Nephrectomy or Partial Nephrectomy for Renal Cell Carcinoma on CT Using a Self-attention-based Model: Variable Vision Transformer (vViT)
ObjectiveTo assess the effectiveness of the vViT model for predicting postoperative renal function decline by leveraging clinical data, medical...
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Psilocybin-induced default mode network hypoconnectivity is blunted in alcohol-dependent rats
Alcohol Use Disorder (AUD) adversely affects the lives of millions of people, but still lacks effective treatment options. Recent advancements in...
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Anti-prostate cancer mechanism of black ginseng during the "nine steaming and nine sun-drying" process based on HPLC analysis combined with vector space network pharmacology
HPLC analysis determined six small-molecule organic acids, maltol, 5-hydroxymethylfurfural (5-HMF), 17 ginsenosides, four oligosaccharides, and 20...
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Monitoring of anesthetic depth and EEG band power using phase lag entropy during propofol anesthesia
BackgroundPhase lag entropy (PLE) is a novel anesthetic depth indicator that uses four-channel electroencephalography (EEG) to measure the temporal...
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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...
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Medical Image Encryption using Biometric Image Texture Fusion
In conjunction with pandemics, medical image data are growing exponentially. In some countries, hospitals collect biometric data from patients, such...
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Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study
Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify...
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Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics
ObjectiveDeep learning (DL) MRI reconstruction enables fast scan acquisition with good visual quality, but the diagnostic impact is often not...
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Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study
BackgroundThe aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in...
