Rozpoznawanie epilepsji za pomocą miar entropii i parametrów osadzania sygnałów EEG
Diagnosing Epilepsy using Entropy Measures and Embedding Parameters of EEG Signals
W skrócie
Badanie sprawdzało, jak dobrze różne matematyczne miary mogą automatycznie odróżnić osoby z epilepsją od osób zdrowych, analizując zapisy elektrycznej aktywności mózgu (EEG). Naukowcy testowali kilka miar entropii i parametrów matematycznych, a najlepsze z nich osiągnęły dokładność do 100% w rozpoznawaniu epilepsji, nawet gdy w danych były zakłócenia. Wyniki pokazują, że ten automatyczny system może być bardzo przydatny do szybkiego i niezawodnego diagnozowania epilepsji w praktyce klinicznej.
Oryginalny abstract (angielski)
BACKGROUND: Manual analysis of electroencephalography (EEG) for epilepsy diagnosis can be subjective and time-consuming, leading to potential errors. An automatic classification system with high detection accuracy is essential for improving diagnostic efficiency and reliability. OBJECTIVE: This study aimed to evaluate a comprehensive set of entropy measures, along with embedding parameters, to identify the most effective single measure for epilepsy diagnosis. MATERIAL AND METHODS: This analytical study used EEG data from the University of Bonn, including healthy controls (HCs) with open eyes and epileptic seizure patients, each with 100 single-channel segments. Discrete wavelet transform was applied, extracting ten entropy measures and two embedding parameters. Statistical tests evaluated feature significance, and a linear discriminant analysis (LDA) classifier was used for classification. Robustness was assessed by introducing Gaussian noise at varying signal-to-noise ratios (SNRs) and analyzing classification performance. RESULTS: Our findings indicated that embedding parameters, permutation entropy, fuzzy entropy, sample entropy, norm entropy, sure entropy, log entropy, and threshold entropy significantly differentiated epileptic patients from HCs. Among these, sample entropy, norm entropy, sure entropy, log entropy, threshold entropy, and embedding delay achieved classification accuracies between 97% and 100% using LDA classifier. Furthermore, even with substantial Gaussian noise, the classifier maintained an accuracy above 84%, demonstrating the robustness of these features in noisy conditions. CONCLUSION: This study demonstrated that embedding-based and entropy-based features can serve as effective individual measures for discriminating epileptic EEG signals from HCs. These findings underscore the potential of these measures in automated epilepsy diagnosis systems, resulting in a robust and reliable tool for clinical applications.