ArticleSensors (Basel, Switzerland)2023
Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson's Disease.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2024Pooled it
- Electroencephalography-Based Clustering Reveals Robust Neurophysiological Subtypes in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Stimulus-Evoked Brain Signals for Parkinson's Detection: A Comprehensive Benchmark Performance Analysis on Cross-Stimulation and Channel-Wise Experiments.Bioengineering (Basel, Switzerland) · 2025Article
- Gait analysis for Parkinson's disease using multiscale entropy.Neurodegenerative disease management · 2025Review
- Applications of Entropy in Data Analysis and Machine Learning: A Review.Entropy (Basel, Switzerland) · 2024Review
- Improving Multiscale Fuzzy Entropy Robustness in EEG-Based Alzheimer's Disease Detection via Amplitude Transformation.Sensors (Basel, Switzerland) · 2024Article
- Functional Brain Network Disruptions in Parkinson's Disease: Insights from Information Theory and Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study.Applied sciences (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) using rest-state EEG signals (rs-EEG) from 20 PD subjects and 20 normal control (NC) subjects at a sampling rate of 128 Hz. Based on the comparative analysis of the effectiveness of entropy calculation methods, fuzzy entropy showed the best results in diagnosing and monitoring PD using rs-EEG, with classification accuracy (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.