Regression analysis for detecting epileptic seizure with different feature extracting strategies

Lal Hussain; Sharjil Saeed; Adnan Idris; Imtiaz Ahmed Awan; Saeed Arif Shah; Abdul Majid; Bilal Ahmed; Quratul-Ain Chaudhary

doi:10.1515/bmt-2018-0012

Article

Regression analysis for detecting epileptic seizure with different feature extracting strategies

Lal Hussain , Sharjil Saeed , Adnan Idris , Imtiaz Ahmed Awan , Saeed Arif Shah , Abdul Majid , Bilal Ahmed and Quratul-Ain Chaudhary

Published/Copyright: May 30, 2019

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From the journal Biomedical Engineering / Biomedizinische Technik Volume 64 Issue 6

Abstract

Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.

Keywords: classification; decision tree; ensemble classifier; epilepsy; K-nearest neighbors; seizure detection; support vector machine

Appendix A

Feature extraction methods

1. Linear methods

1.1 Time domain analysis

Different time-domain features are extracted to measure the time variability in the EEG signals

1.1.1. SDSD between adjacent intervals in each segment of EEG.

1.1.2. SDNN of the EEG intervals in each segment.

(A1)SDNN=1N−1∑j=1N (Xj−X¯)2

1.1.3. RMSSD of N successive EEG intervals

(A2)RMSSD=1N−1∑j=1N−1 (Xj+1−X)2

1.1.4. SDANN of EEG intervals.

(A3)SDANN=SD [u1, u2, u3, u4,…, un]

1.2. Frequency domain analysis

Frequency domain features are helpful to extract the spectral components of an EEG time series.

1.2.1. Total power (TP): Total spectral power for all EEG time series signals is 0.4 Hz.

1.2.2. Very low frequency (VLF): Total spectral power of all EEG intervals between 0.003 and 0.04 Hz.

1.2.3. Low frequency (LF): Total spectral power of all EEG intervals between 0.04 and 0.15 Hz.

1.2.4. High frequency (HF): Total spectral power of all EEG intervals between 0.15 and 0.4 Hz.

1.2.5. ULF: Total spectral power of all EEG intervals up to 0.003 Hz.

1.2.6. LF/HF Ratio: Ratio of low to high frequency power. This measures overall balance between sympathetic and parasympathetic systems.

2. Nonlinear methods

EEG signals exhibit nonlinearity and contains hidden information about the dynamics of these signals. It is unrealistic to extract the valuable information using traditional approaches, thus information theoretic approaches based on entropy are most widely used for analysis of such signals.

2.1. Approximate entropy (ApEn):

ApEn developed by [122] is a statistical measure used to quantify the regularities in data. By computing the probability, ApEn indicate that similar patterns are not repeated.

(A4)ApEn (m, r, N) ∅m(r)−∅m+1(r)

2.2. Fast sample entropy (SampEn) with the KD tree approach

SampEn was proposed by [117] which is a modified form of ApEn. The physiology of a time series is computed using SampEn. It is also independent of data length and mathematically can be computed using:

(A5)SampEn(m, r)=limN→∞−lnPm(r)Qm(r)

where P^m(r) denotes the probability that two sequences will still match for m+1 points and Q^m(r) is the probability that two sequences will matches for m points (with tolerance of τ); where self matches are excluded. In this regard equation (1) can be expressed as:

(A6)SampEn(m, r, N)=−lnPm(r)Qm(r)

By setting Q={[(N−m−1)(N−m)]2}Qm(r) and P={[(N−m−1)(N−m)]2}Pm(r)

We have PQ =Pm(r)Qm(r) and thus sample entropy can be expressed as:

(A7)SampEn(m, r, N)=Pm(r)Qm(r)

The total number of forward matches of length m+1 are denoted by P and Q is the total number of template matches of length m. The fast sample entropy using the KD tree algorithmic base approached as implemented by [64] which is more efficient with respect to speed and efficiency.

2.3. Wavelet entropy

Wavelet entropy methods are also used to measure the nonlinearity in a time series. The commonly used wavelet methods [123] include Shannon, log energy, threshold, sure and nnorm, etc. Shannon entropy [123] was used to measure the complexity of signal to wavelet coefficients generated by a wavelet packet, the high uncertainty is denoted by larger values showing high complexity. Wavelet entropy used by [124] which provided the useful information to measure the underlying dynamical process associated with the signal. The entropy “E” must be an additive information cost function such that E(0)=0 and E(S)=∑iE(Si).

2.3.1. Shannon entropy

In 1948, Claude Shannon proposed Shannon entropy [125]. Since then, it was most widely used in different fields and information processing. Shannon entropy is a measure of the uncertainty associated with a random variable. Specifically, it quantifies the expected value of the information contained in a message. Mathematically, the Shannon entropy of variable X can be defined as follow:

(A8)V(X)=V(P1, …, Pn)= −∑i=1nPilog2 Pi

(A9)Pi=Pr (X=xi)

where P_i is defined in Eq. (9) with x_i indicating the ith possible value of X out of n symbols, and P_i denoting the possibility of X=x_i.

2.3.2. Wavelet norm entropy

Wavelet norm entropy [126] is defined as:

(A10)E(S)=∑i|Si|pN

where p is the power and must be 1≪P<2 the terminal node signal and (s_i) i the waveform of terminal.

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Received: 2018-01-26

Accepted: 2019-01-08

Published Online: 2019-05-30

Published in Print: 2019-12-18

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Articles in the same Issue

https://doi.org/10.1515/bmt-2018-0012

Keywords for this article

classification; decision tree; ensemble classifier; epilepsy; K-nearest neighbors; seizure detection; support vector machine