ArticleJournal of medical systems2014
The effect of multiscale PCA de-noising in epileptic seizure detection.
Article in Journal of medical systems, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Validation-Aware Retrospective EEG Treatment-Response Modelling Using Chaotic Pattern of Prime Numbers Features: Segment-Level Separability and Subject-Wise Generalisation.Bioengineering (Basel, Switzerland) · 2026Article
- Predicting Depression Therapy Outcomes Using EEG-Derived Amplitude Polar Maps.Brain sciences · 2025Article
- [Automatic epilepsy detection with an attention-based multiscale residual network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.Cognitive neurodynamics · 2024Article
- Comparison of Empirical Mode Decomposition, Wavelets, and Different Machine Learning Approaches for Patient-Specific Seizure Detection Using Signal-Derived Empirical Dictionary Approach.Frontiers in digital health · 2021Article
- EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms.Journal of biomedical research · 2020Article
- EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges.Sensors (Basel, Switzerland) · 2019Review
- Removal of Artifacts from EEG Signals: A Review.Sensors (Basel, Switzerland) · 2019Review
- Epileptic Seizure Detection Based on EEG Signals and CNN.Frontiers in neuroinformatics · 2018Article
- Early Seizure Detection by Applying Frequency-Based Algorithm Derived from the Principal Component Analysis.Frontiers in neuroinformatics · 2017Article
- Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier.Journal of medical systems · 2016Article
- Analysis of spike waves in epilepsy using Hilbert-Huang transform.Journal of medical systems · 2015Article
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Authors and funding
2 authors.
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Abstract
In this paper we describe the effect of Multiscale Principal Component Analysis (MSPCA) de-noising method in terms of epileptic seizure detection. In addition, we developed a patient-independent seizure detection algorithm using Freiburg EEG database. Each patient contains datasets called "ictal" and "interictal". Window length of 16 s was applied to extract EEG segments from datasets of each patient. Furthermore, Power Spectral Density (PSD) of each EEG segment was estimated using different spectral analysis methods. Afterwards, these values were fed as input to different machine learning methods that were responsible for seizure detection. We also applied MSPCA de-noising method to EEG segments prior to PSD estimation to determine if MSPCA can further enhance the classifiers' performance. The MSPCA drastically improved both the sensitivity and the specificity, increasing the overall accuracy of all three classifiers up to 20%. The best overall detection accuracy (99.59%) was achieved when Eigenvector analysis was used for frequency estimation, and C4.5 as a classifier. The experiment results show that MSPCA is an effective de-noising method for improving the classification performance in epileptic seizure detection.
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