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Technological advancements in healthcare, production, automobile, and aviation industries have shifted working styles from manual to automatic. This automation requires smart, intellectual, and safe machinery to develop an accurate and efficient brain–computer interface (BCI) system. However, developing such BCI systems requires effective processing and analysis of human physiology. Electroencephalography (EEG) is one such technique that provides a low-cost, portable, non-invasive, and safe solution for BCI systems. However, the non-stationary and nonlinear nature of EEG signals makes it difficult for experts to perform accurate subjective analyses. Hence, there is an urgent need for the development of automatic mental state detection. This paper presents the classification of three mental states using an ensemble of the tunable Q wavelet transform, the multilevel discrete wavelet transform, and the flexible analytic wavelet transform. Various features are extracted from the subbands of EEG signals during focused, unfocused, and drowsy states. Separate and fused features from ensemble decomposition are classified using an optimized ensemble classifier. Our analysis shows that the fusion of features results in a dimensionality reduction. The proposed model obtained the highest accuracies of 92.45% and 97.8% with ten-fold cross-validation and the iterative majority voting technique. The proposed method is suitable for real-time mental state detection to improve BCI systems. [ABSTRACT FROM AUTHOR]

Diagnosis of schizophrenia (SZ) is traditionally performed through patient’s interviews by a skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias. Hence the aim of this study is to develop an automatic SZ identification scheme using electroencephalogram (EEG) signals that can eradicate the aforementioned problems and support clinicians and researchers. This study introduces a methodology design involving empirical mode decomposition (EMD) technique for diagnosis of SZ from EEG signals to perfectly handle the behavior of non-stationary and nonlinear EEG signals. In this study, each EEG signal is decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and then twenty-two statistical characteristics/features are calculated from these IMFs. Among them, five features are selected as significant feature applying Kruskal Wallis test. The performance of the obtained feature set is tested through several renowned classifierson a SZ EEG dataset. Among the considered classifiers, theensemble bagged tree performed as the best classifier producing 93.21% correct classification rate for SZ, with an overall accuracy of 89.59% for IMF 2. These results indicate that EEG signals discriminate SZ patients from healthy control (HC) subjects efficiently and have the potential to become a tool for the psychiatrist to support the positive diagnosis of SZ. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed for image denoising inspired by the bidimensional empirical mode decomposition algorithm and the diffusion-based filtering. In the bidimensional empirical mode decomposition technique, the noisy image is decomposed into its respective intrinsic mode functions and the high-frequency and the low-frequency noises are removed with the help of the proposed diffusion filtering method using its parameters such as connectivity, conductance function, gradient threshold and the number of iterations. The image is reconstructed with the help of these denoised intrinsic mode functions. Performance of the proposed technique is evaluated in terms of the peak signal-to-noise ratio, the structural similarity index, the mean square error and is then compared with the already existing techniques on the image denoising. From the experimentation and results obtained, it is clear that the bidimensional empirical mode decomposition method using the diffusion filtering algorithm is efficient for the denoising of the images both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Breast cancer is a serious disease for women in the world and ranks the second cancer for women in many countries. Computer-aided diagnosis provides a second view to aid for radiologists to detect and diagnose breast cancer. In this paper, we present a novel approach of textural features extraction from mammograms using bi-dimensional empirical mode decomposition (BEMD) method and classification for diagnosis of breast cancer. Preprocessing techniques such as noise removal, artifacts and background suppression and contrast enhancement are performed before features extraction stage. Gray-level co-occurrence matrices-based features are extracted from 2-D intrinsic mode functions obtained by applying BEMD method on mammographic images. Finally, these features are given as an input to least squares support vector machine for classification of mammogram as normal or abnormal. The experimental results show that the proposed method achieves 95% accuracy, which is better than as compared to other published methods in the Mini-MIAS database for diagnosis of breast cancer. The proposed method can be used as automatic, accurate and noninvasive method for breast cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Electrocardiograph (ECG) denoising is the most important step in diagnosis of heart-related diseases, as the diagnosis gets influenced with noises. In this paper, a new method for ECG denoising is proposed, which incorporates empirical mode decomposition algorithm with Riegmann Liouvelle (RL) fractional integral filtering and Savitzky–Golay (SG) filtering. In the proposed method, noisy ECG signal is decomposed into its intrinsic mode functions (IMFs), from which noisy IMFs, corrupted with high-frequency (HF) and low-frequency (LF) noises, are identified by proposed noisy-IMFs identification methodologies. To denoise the signal, RL fractional integral filtering and SG filtering are applied on noisy IMFs corrupted with HF and LF noises, respectively; ECG signal is reconstructed with denoised IMFs and remaining signal dominant IMFs to obtain noise-free ECG signal. Proposed methodology is tested with MIT-BIH arrhythmia database. Its performance, in terms of signal-to-noise ratio and mean square error, is compared with other related ECG denoising methods based on fractional integral, empirical mode decomposition, and ensemble empirical mode decomposition. The obtained results by proposed method prove that the proposed method gives efficient noise removal performance. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) contains five rhythms, which provide details about various activities of brain. These rhythms are separated using Hilbert–Huang transform for classification of focal and non‐focal EEG signals. For this, the EEG signal is disintegrated into narrow bands intrinsic mode functions (IMFs) using empirical mode decomposition, and analytic representation of IMFs is computed by Hilbert transformation that helps to extract instantaneous frequencies of respective IMFs. Frequency bands of EEG signals known as rhythms are separated from analytic IMFs using instantaneous frequencies. Two efficient parameters Pearson product‐moment correlation coefficient and Spearman rank correlation coefficient extracted from the rhythms are used with different kernel functions of least‐squares support vector machine for the classification of focal and non‐focal EEG signals. Thus, obtained results show improved performance of proposed method as compared to other existing methods. [ABSTRACT FROM AUTHOR]