Search Results for author:
Found 5 papers, 2 papers with code
Entropy-based machine learning model for diagnosis and monitoring of Parkinson's Disease in smart IoT environment
We computed different types of entropy from EEG signals and found that Fuzzy Entropy performed the best in diagnosing and monitoring PD using rs-EEG.
Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation
Entropy measures are effective features for time series classification problems.
Heart Disease Detection using Quantum Computing and Partitioned Random Forest Methods
Compared to earlier works, the proposed HQRF achieved a maximum area under the curve (AUC) of 96. 43% and 97. 78% in predicting heart diseases using Cleveland and Statlog datasets, respectively with HQNN.
Novel techniques for improving NNetEn entropy calculation for short and noisy time series
Firstly, this work investigates different methods of filling the reservoir with time series (signal) elements.
Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals
Employing traditional machine learning and deep learning methods, we explore (a) dimensional and categorical emotion recognition, and (b) PD vs HC classification from emotional EEG signals.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
