A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG

Abstract

In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has been widely used in computer-aided signal analysis of epileptic electroencephalography (EEG), such as the detection of seizures. One of the important hurdles in the applications of DWT is the settings of DWT, which are chosen empirically or arbitrarily in previous works. The objective of this study aimed to develop a framework for automatically searching the optimal DWT settings to improve accuracy and to reduce computational cost of seizure detection. To address this, we developed a method to decompose EEG data into 7 commonly used wavelet families, to the maximum theoretical level of each mother wavelet. Wavelets and decomposition levels providing the highest accuracy in each wavelet family were then searched in an exhaustive selection of frequency bands, which showed optimal accuracy and low computational cost. The selection of frequency bands and features removed approximately 40% of redundancies. The developed algorithm achieved promising performance on two well-tested EEG datasets (accuracy >90% for both datasets). The experimental results of the developed method have demonstrated that the settings of DWT affect its performance on seizure detection substantially. Compared with existing seizure detection methods based on wavelet, the new approach is more accurate and transferable among datasets.

Epileptic seizure detection by combining robust‐principal component analysis and least square‐support vector machine

“…Daubeches‐4 (DB4) is generally used for seizure detection, and this tool could decompose EEG signals into residual signal (a5) and detail signals (d1–d5) (see Figure ). The residual signal (a5) represents 0–3 Hz, and the detail signals represent 50–100 Hz (d1), 25–50 Hz (d2), 12–25 Hz (d3), 6–12 Hz (d4), and 3–6 Hz (d5) . The human EEG rhythms include: δ (< 4 Hz), θ (4–8 Hz), α (8–13 Hz), β (13–30 Hz), γ (> 30 Hz) .…”

“…The residual signal (a5) represents 0-3 Hz, and the detail signals represent 50-100 Hz (d1), 25-50 Hz (d2), 12-25 Hz (d3), 6-12 Hz (d4), and 3-6 Hz (d5). 15 The human EEG rhythms include: d (< 4 Hz), u (4-8 Hz), a (8-13 Hz), b (13-30 Hz), g (> 30 Hz). 16 A pattern could be found from the correspondence between those DB4 bands and EEG rhythm, and that two DB4 bands always cover one EEG rhythm.…”

Ictal recruitment of anterior nucleus of thalamus in human focal epilepsy

“…The time-frequency decomposition of field potentials was performed with the discrete wavelet transform relative wavelet energy (DWT -'db4', RWE, 6 levels). The DWT provides a nonredundant, highly efficient wavelet representation and direct estimation of local energies at the different relevant scales (Ursino et al, 2004;Chen et al, 2017). The motivation for selecting DWT is based on prior studies suggesting this method can be an optimal tool for online seizure detection that can be translated in implantable neural prosthesis (Kamboh et al, 2007;Narasimhan et al, 2011;Logesparan et al, 2012;Yang et al, 2014).…”

Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity

“…The EEG dataset was recorded from pediatric subjects with intractable seizures at Children's Hospital Boston. This database contains 22 subjects (17 females, ages 1.5-19; 5 males, ages [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and can be downloaded from the PhysioNet website: http://www .physionet.org/pn6/chbmit/. The International 10-20 system of EEG electrode positions and nomenclature was used to collect these EEG recordings.…”

“…Meanwhile, some methods only have promising results for selected patients with seizure of single type (such as temporal lobe seizure); the reproducibility and reliability of the results have been questioned when being tested on other patients with numerous seizure types [17]. Furthermore, the characteristics of EEG vary significantly across patients.…”