Alzheimer’s Disease Detection using Empirical Mode Decomposition and Hjorth parameters of EEG signal

Q: What is the clinical evidence from this study?

Study design: Cross-Sectional. Population: Alzheimer's disease (n=23). Intervention: Automatic AD detection system using EEG signal, empirical mode decomposition, and Hjorth parameters vs. Normal controls. Primary outcome: Classification accuracy.

Key Result

An automatic Alzheimer's disease detection system using EEG signals, empirical mode decomposition, and Hjorth parameters achieved a maximum classification accuracy of 92.90%.

Study Design

Type

Cross-Sectional (n=23)

Structured PICO

Does an automatic detection system using EEG signals and empirical mode decomposition accurately detect Alzheimer's disease?

Population

23 subjects (12 with Alzheimer's disease and 11 normal controls) evaluated for automatic AD detection using EEG signals.

Exposure

Automatic Alzheimer's disease detection system using Electroencephalogram (EEG) signals, empirical mode analysis (9 Intrinsic mode functions), Hjorth parameters, and least-square support vector machine (LS-SVM) model

Comparator

Normal controls

Outcome

Classification accuracysurrogate

An automatic detection system using EEG signals and empirical mode decomposition achieved 92.90% accuracy in detecting Alzheimer's disease.

Abstract

Alzheimer’s disease (AD) is a progressive neuro-degenerative disorder observed in the elderly. AD diagnosis is performed through interviews or questionnaires by an experienced psychiatrist. This process is time-consuming, biased, and subject-specific. Hence, its urgent need to develop an. The paper presents an automatic AD detection system using Electroencephalogram (EEG) signal to alleviate these problems and support neurologists. Nine IMFs (Intrinsic mode functions) are generated for each EEG signal using empirical mode analysis. Ten different features are extracted from these IMFs. Three Hjorth parameters (activity, mobility, complexity) are selected using the Kruskal-Wallis test. The selected features from EEG recordings of 23 subjects (AD-12 and NC-11) are evaluated using the least-square support vector machine (LS-SVM) model with 10-fold cross-validation for three kernels. A maximum of 92.90% classification accuracy is obtained using the features of IMF-4. The results showed that the proposed method detected AD patients efficiently. Further, the proposed method can be used to detect other neurological disorders.

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Cite This Study

Puri et al. (Wed,) conducted a cross-sectional in Alzheimer's disease (n=23). Automatic AD detection system using EEG signal, empirical mode decomposition, and Hjorth parameters vs. Normal controls was evaluated on Classification accuracy. An automatic Alzheimer's disease detection system using EEG signals, empirical mode decomposition, and Hjorth parameters achieved a maximum classification accuracy of 92.90%.

synapsesocial.com/papers/6a2b5c67f8aa8fa22f22fda2 https://doi.org/https://doi.org/10.1109/dasa54658.2022.9765111

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