ArticleHealthcare technology letters2017
Analysis of physiological signals using state space correlation entropy.
Article in Healthcare technology letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- EEG biomarkers in Alzheimer's and prodromal Alzheimer's: a comprehensive analysis of spectral and connectivity features.Alzheimer's research & therapy · 2024Article
- Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations.The European journal of neuroscience · 2022Review
- Emotional Activity Is Negatively Associated With Cognitive Load in Multimedia Learning: A Case Study With EEG Signals.Frontiers in psychology · 2022Article
- Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis.Sensors (Basel, Switzerland) · 2021Article
- Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals.Entropy (Basel, Switzerland) · 2020Article
- An Improved Convolutional Neural Network Based Approach for Automated Heartbeat Classification.Journal of medical systems · 2019Article
- Quantification of Beat-To-Beat Variability of Action Potential Durations in Langendorff-Perfused Mouse Hearts.Frontiers in physiology · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this letter, the authors propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series. The SSCE is computed from the probability of the correlations of the embedding vectors. The performance of SSCE measure is evaluated using both synthetic and real valued signals. The experimental results reveal that, the proposed SSCE measure along with SVM classifier have sensitivity value of 91.60%, which is higher than the performance of both sample entropy and permutation entropy features for detection of shockable ventricular arrhythmia.
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Registered trials
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