In this paper, an effective approach for the feature extraction of raw Electroencephalogram (EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented. For the importance of making the right decision, the proposed method was performed to be able to get better features of the EEG signals. The proposed method was consisted of two stages: feature extraction by 1D-LBP and classification by classifier algorithms with features extracted. On the classification stage, the several machine learning methods were employed to uniform and non-uniform 1D-LBP features. The proposed method was also compared with other existing techniques in the literature to find out benchmark for an epileptic data set. The implementation results showed that the proposed technique could acquire high accuracy in classification of epileptic EEG signals. Also, the present paper is an attempt to develop a general-purpose feature extraction scheme, which can be utilized to extract features from different categories of EEG signals.

1D-Local Binary Pattern Based Feature Extraction for Classification of Epileptic EEG Signals, Applied Mathematics and Computation243 (2014): 209-219.

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Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies

Timely detection of cardiac abnormalities from an Electrocardiogram (ECG) signal is very essential. This requires its appropriate and efficient processing. In the literature, most of the researchers focussed on linear techniques that were applied on filtered ECG datasets leaving an ample scope for exploring the use of non-linear techniques in the presence and absence of natural noise-processes. Therefore, there is a need of supplementing the existing research on ECG signal interpretation by using non-linear techniques on noisy ECG data. Non-linear techniques are expected to yield supplementary clues about the non-linearities in the underlying cardiovascular system. One such promising non-linear technique, known as chaos theory (analysis), has been considered here to estimate reliable and robust thresholds for R-peak detection using fractal dimension, Approximate Entropy, Sample Entropy, correlation dimension, and Lyapunov exponent based on time-delay dimension (embedding). Also, time–frequency analysis techniques have shown their effectiveness for analyzing such types of non-linear and non-stationary signals due to simultaneous interpretation of the signal in both time and frequency domain. Among existing TFA techniques such as wavelet transform, short time Fourier transform, Hilbert transform, Auto-regressive Time Frequency Analysis (ARTFA) offers good time–frequency resolution. Therefore, Chaos theory and ARTFA have been considered in this paper. First, raw ECG signal was filtered using Savitzky Golay Digital Filtering (SGDF) because it retains all important signal features after filtering. Second, a novel optimal trajectory detection step was proposed on the basis of phase space reconstruction (attractors) in chaos theory. Third, ARTFA has been used to find the spectral components of the extracted features using chaos theory. Here, ARTFA has been used for finding the autoregressive coefficients in the first step and time–frequency description in the second step. Burg method has been considered for Auto-regressive modeling to fit an ARTFA model for analyzing ECG signal by minimizing (least squares) the forward and backward prediction errors. MIT-BIH arrhythmia database has been considered for validating the present research effort. Some real time signals were also tested to explore direct usage of the proposed technique in practical applications. The obtained results show that the proposed technique enhances sensitivity, positive predictive value and accuracy thereby improving the detection of arrhythmias. Computational cost of the proposed technique is reduced to a great extent by using the chaos theory (analysis) yielding efficient detection performance. All results have been obtained in MATLAB environment R2011a. Improved values viz. 99.96% SE, 99.97% PPV, and 99.93% ACC are obtained.

Chaos Theory and ARTFA: Emerging Tools for Interpreting ECG Signals to Diagnose Cardiac Arrhythmias

Combining Gabor energy with equilibrium optimizer algorithm for multi-modality medical image fusion

Epileptic-seizure classification using phase-space representation of FBSE-EWT based EEG sub-band signals and ensemble learners

Electrocardiogram (ECG) signals are used to diagnose cardiovascular diseases. During ECG signal acquisition, various noises like power line interference, baseline wandering, motion artifacts, and electromyogram noise corrupt the ECG signal. As an ECG signal is non-stationary, removing these noises from the recorded ECG signal is quite tricky. In this paper, along with the proposed denoising technique using stationary wavelet transform, various denoising techniques like lowpass filtering, highpass filtering, empirical mode decomposition, Fourier decomposition method, discrete wavelet transform are studied to denoise an ECG signal corrupted with noise. Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance. The experimental result showed that the proposed stationary wavelet transform based ECG denoising technique outperformed the other ECG denoising techniques as more ECG signal components are preserved than other denoising algorithms.

Stationary wavelet transform based ECG signal denoising method.

A novel approach for automated alcoholism detection using Fourier decomposition method.

Epilepsy is a disease recognized as the chronic neurological dysfunction of the human brain which is described by the sudden and excessive electrical discharges of the brain cells Electroencephalogram (EEG) is a prime tool applied for the diagnosis of epilepsy In this study, a novel and effective approach is introduced to decompose the non-stationary EEG signals using the Fourier decomposition method The concept of position, velocity, and acceleration has been employed on the EEG signals for feature extraction using $$L^p$$
 norms computed from Fourier intrinsic band functions (FIBFs) The proposed scheme comprises three main sections In the first section, the EEG signal is decomposed into a finite number of FIBFs In the second stage, the features are extracted from FIBFs and relevant features are selected by using the Kruskal–Wallis test In the last stage, the significant features are passed on to the support vector machine (SVM) classifier By applying 10-fold cross-validation, the proposed method provides better results in comparison to the state-of-the-art methods discussed in the literature, with an average classification accuracy of 9996% and 9994% for classification of EEG signals from the BONN dataset and the CHB-MIT dataset, respectively It can be implemented using the computationally efficient fast Fourier transform (FFT) algorithm

An efficient method for identification of epileptic seizures from EEG signals using Fourier analysis

With the rapid innovation in the field of healthcare, various biomedical signals, namely, electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), play a crucial role for accurate measurement of various diseases such as cardiovascular diseases, brain disorders, etc. In the present work, an efficient method based on empirical mode decomposition (EMD) has been proposed to detect the epileptic activity. The present study is composed of three parts. In the first part, EMD is used to decompose the EEG signal into a set of amplitude modulated and frequency modulated components, referred to as intrinsic mode functions (IMFs). In the second part, features such as standard deviation, kurtosis, and Hjorth parameters have been extracted from various IMFs. In the last stage, the features are employed as inputs to support vector machine classifier for classification between non-seizure and seizure EEG signals. The simulation results show that the proposed scheme has attained better classification accuracy when compared to existing state-of-the-art methods.

Classification of Epileptic Seizure in EEG Signal Using Support Vector Machine and EMD

Automatic classification of abnormal electrocardiogram signals plays a vital role in developing robust unsupervised ECG analysis systems. This paper proposes a novel method for the automatic classification of normal and abnormal signals. The proposed framework utilizes the Fourier decomposition method (FDM), the statistical feature extraction, and support vector machine (SVM) based signal classification. ECG signals are decomposed using the FDM algorithm to disparate the ECG components from noise and artifacts. The statistical features such as mean, kurtosis, standard deviation, skewness, and root mean square (RMS) are computed from the extracted ECG signals. Finally, the SVM-based algorithm is applied to classifying the normal and abnormal ECG signals. The proposed method is tested rigorously using the MIT-BIH arrhythmia ECG database of different lengths. The proposed method achieves better classification of the accuracy of 99.50% compared to existing methods and accurately classifies short bursts of noises. The proposed method is also suitable for reducing false alarm rates and selecting appropriate noise-specific denoising techniques in automated ECG analysis applications.

Virender Kumar Mehla

Papers

Stationary wavelet transform based ECG signal denoising method.

A novel approach for automated alcoholism detection using Fourier decomposition method.

An efficient method for identification of epileptic seizures from EEG signals using Fourier analysis

Classification of Epileptic Seizure in EEG Signal Using Support Vector Machine and EMD

Classification of Normal and Abnormal ECG signals using Support Vector Machine and Fourier Decomposition Method