Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance

Lee, Sang-Hong; Lim, Joon S.; Kim, Jaekwon; Yang, Jong-Hyun; Lee, Young-Ho

doi:10.1016/j.cmpb.2014.04.012

Cited by 155 publications

(57 citation statements)

References 46 publications

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“…Specifically, the fourth-order Daubechies (db4) wavelet is selected due to its good local approximated performance for nonstationary signals [19,24]. Five frequency sub-bands of clinical interest are then obtained by using the wavelet decomposition and reconstruction: delta (0-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13)(14)(15)(16), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) and gamma . Herein, wavelet features of its good localizing properties are extracted from each sub-band in the time-frequency domain, followed by a well-known PCA algorithm of the dimensionality reduction in order to remove the irrelevant or spurious features.…”

Section: Methodsmentioning

confidence: 99%

“…The PSR is used to reconstruct the so-called phase space, which is a very useful tool for characterizing any low-dimensional or high-dimensional dynamic system. Alternatively, a point in the phase space represents the state of the system at any given time [11]. For an IMF v[n], where n = 1, 2, · · · , N, the phase space can be reconstructed by [11]:…”

Section: Feature Extraction Via Nonlinear Analysismentioning

confidence: 99%

“…Alternatively, a point in the phase space represents the state of the system at any given time [11]. For an IMF v[n], where n = 1, 2, · · · , N, the phase space can be reconstructed by [11]:…”

Section: Feature Extraction Via Nonlinear Analysismentioning

confidence: 99%

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Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Wang

Xue

et al. 2017

Entropy

207

106

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Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.

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Section: Methodsmentioning

confidence: 99%

Section: Feature Extraction Via Nonlinear Analysismentioning

confidence: 99%

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Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Wang

Xue

et al. 2017

Entropy

207

106

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“…Euclidean distance is one of the simplest and most intuitive distance measures, which has been adopted in many fields, such as gene identification [35], protein 3D structure reconstruction [36], robust automatic pectoral muscle segmentation [37] and classification of normal and epileptic seizure EEG signals [38], etc. However, there are many other distance measures, which could affect protein similarity analysis.…”

Section: Resultsmentioning

confidence: 99%

An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids

Song

Yang

et al. 2016

PLoS ONE

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In this paper, we have proposed a novel alignment-free method for comparing the similarity of protein sequences. We first encode a protein sequence into a 440 dimensional feature vector consisting of a 400 dimensional Pseudo-Markov transition probability vector among the 20 amino acids, a 20 dimensional content ratio vector, and a 20 dimensional position ratio vector of the amino acids in the sequence. By evaluating the Euclidean distances among the representing vectors, we compare the similarity of protein sequences. We then apply this method into the ND5 dataset consisting of the ND5 protein sequences of 9 species, and the F10 and G11 datasets representing two of the xylanases containing glycoside hydrolase families, i.e., families 10 and 11. As a result, our method achieves a correlation coefficient of 0.962 with the canonical protein sequence aligner ClustalW in the ND5 dataset, much higher than those of other 5 popular alignment-free methods. In addition, we successfully separate the xylanases sequences in the F10 family and the G11 family and illustrate that the F10 family is more heat stable than the G11 family, consistent with a few previous studies. Moreover, we prove mathematically an identity equation involving the Pseudo-Markov transition probability vector and the amino acids content ratio vector.

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“…This technique, which is based on the chaos theory, uses delay coordinate theorem to represent chaotic signals such as electrocardiogram (ECG), electroencephalogram (EEG), and the speech signal (Sharma and Pachori, 2015;Zhou et al, 2015;Koulaouzidis et al, 2015;Lee et al, 2014;Al-Fahoum and Qasaimeh, 2013;Thasleema et al, 2012;Nejadgholi et al, 2011;Johnson et al, 2005). Researchers have successfully implemented RPS-based feature extraction approaches in verity of speech applications (e.g., speech recognition, phoneme classification, pitch mark detection and speech enhancement) (Sun Contents , 2007Povinelli et al, 2006;Hagmüller and Kubin, 2006).…”

Section: Introductionmentioning

confidence: 99%

MLP-based isolated phoneme classification using likelihood features extracted from reconstructed phase space

Shekofteh

Almasganj

Daliri

2015

Engineering Applications of Artificial Intelligence

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Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance

Cited by 155 publications

References 46 publications

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids

MLP-based isolated phoneme classification using likelihood features extracted from reconstructed phase space

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