Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems.

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model's training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System.

Magnetic resonance elastography (MRE) is used to assess the stiffness of the liver to rule out cirrhosis or fibrosis. The image, nevertheless, is regarded as shear-wave imaging and does not depict any anatomical features. Multimodality medical image fusion (MMIF), such as the fusion of MRE with computed tomography (CT) scan or magnetic resonance imaging (MRI), can help doctors optimize the advantages of each imaging technique. As a result, perceptions serve as valid and valuable assessment criteria. The contrast sensitivity function (CSF), which describes the rates of visual contrast sensitivity through the changing of spatial frequencies, is used mathematically to characterize the human visual system (HVS). As a result, we suggest novel methods for fusing images that use discrete wavelets transform (DWT) based on HVS and CSF models. Images from MRI or CT scan were combined with MRE images, and the outcomes were assessed both subjectively and objectively. Visual inspection of merging images was done throughout the qualitative analysis. The CT-MRE fused images in all four datasets were shown to be superior at maintaining bones and spatial resolution, despite the MRI-MRE being better at exhibiting soft tissues and contrast resolution. It is clear from all four datasets that the liver soft tissue in MRI and CT images mixed successfully with the red-colored stiffness distribution seen in MRE images. The proposed approach outperformed DWT, which produced visual artifacts such as signal loss. Quantitative evaluation using mean, standard deviation, and entropy showed that the generated images from the proposed technique performed better than the source images and DWT. Additionally, peak signal-to-noise ratio, mean square error, correlation coefficient, and structural similarity index measure were employed to compare the two fusion approaches, namely, MRI-MRE and CT-MRE. The comparison did not show the superiority of one approach over the other. In conclusion, both subjective and objective evaluation approaches revealed that the combined images contained more information and characteristics. Hence, the proposed method might be a useful procedure to diagnose and localize the stiffness regions on the liver soft tissue by fusion of MRE with MRI or CT.

Oral Cancer Prediction Using a Probability Neural Network (PNN).

OBJECTIVE: In India, usually, oral cancer is mostly identified at a progressive stage of malignancy. Hence, we are motivated to identify oral cancer in its early stages, which helps to increase the lifetime of the patient, but this early detection is also more challenging. METHODS: The proposed research work uses a probabilistic neural network (PNN) for the prediction of oral malignancy. The recommended work uses PNN along with the discrete wavelet transform to predict the cancer cells accurately. The classification accuracy of the PNN model is 80%, and hence this technique is best for the prediction of oral cancer. RESULT: Due to heterogeneity in the appearance of oral lesions, it is difficult to identify the cancer region. This research work explores the different computer vision techniques that help in the prediction of oral cancer. CONCLUSION: Oral screening is important in making a decision about oral lesions and also in avoiding delayed referrals, which reduces mortality rates.

Comparative analysis of ANN performance of four feature extraction methods used in the detection of epileptic seizures.

Epilepsy, a prevalent neurological disorder characterized by disrupted brain activity, affects over 70 million individuals worldwide, as reported by the World Health Organization (WHO). The development of computer-aided diagnosis systems has become vital in assessing epilepsy severity promptly and initiating timely treatment. These systems enable the detection of epileptic seizures by analyzing the electrical activity in the EEG recordings of the patients. In addition, it helps doctors to choose suitable treatment by quickly determining the type, duration, and characteristics of seizures and increases the patient's quality of life. The proposed computer-aided diagnosis system in this study comprises three modules: preprocessing, feature extraction, and classification. The initial module employs a low-pass Chebyshev II filter to eliminate noise artifacts from signal recordings. The second module involves deriving feature vectors using Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis. The third module employs the Artificial Neural Networks method for epileptic seizure detection. This study not only enables the comparison of feature extraction efficacy among Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis techniques, but it also reveals that Bispectrum Analysis and Empirical Mode Decomposition yield the highest accuracy rate. The method achieves 100% accuracy in detecting epileptic seizures. Additionally, sensitivity analysis has been conducted to enhance the success of Discrete Wavelet Transform and Wavelet Packet Analysis methods and to identify significant features.

Myocardial infarction detection using ITD, DWT and deterministic learning based on ECG signals.

Nowadays, cardiovascular diseases (CVD) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention issue. Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgement and several electrocardiographic (ECG) or vectorcardiographic (VCG) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study, we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector, which is decomposed into a series of proper rotation components (PRCs) by using the intrinsic time-scale decomposition (ITD) method. The novel cardiac vector may fully reflect the pathological alterations provoked by MI and may be correlated to the disparity of cardiac system dynamics between healthy and MI subjects. ITD is employed to measure the variability of cardiac vector and the first PRCs are extracted as predominant PRCs which contain most of the cardiac vector's energy. Second, four levels discrete wavelet transform with third-order Daubechies (db3) wavelet function is employed to decompose the predominant PRCs into different frequency bands, which combines with three-dimensional phase space reconstruction to derive features. The properties associated with the cardiac system dynamics are preserved. Since the frequency components above 40 Hz are lack of use in ECG analysis, in order to reduce the feature dimension, the advisable sub-band (D4) is selected for feature acquisition. Third, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.20%. Results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

Invariant Pattern Recognition with Log-Polar Transform and Dual-Tree Complex Wavelet-Fourier Features.

In this paper, we propose a novel method for 2D pattern recognition by extracting features with the log-polar transform, the dual-tree complex wavelet transform (DTCWT), and the 2D fast Fourier transform (FFT2). Our new method is invariant to translation, rotation, and scaling of the input 2D pattern images in a multiresolution way, which is very important for invariant pattern recognition. We know that very low-resolution sub-bands lose important features in the pattern images, and very high-resolution sub-bands contain significant amounts of noise. Therefore, intermediate-resolution sub-bands are good for invariant pattern recognition. Experiments on one printed Chinese character dataset and one 2D aircraft dataset show that our new method is better than two existing methods for a combination of rotation angles, scaling factors, and different noise levels in the input pattern images in most testing cases.

Motor imagery EEG signal classification with a multivariate time series approach.

BACKGROUND: Electroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy. Achieving a reliable classification of motor imagery EEG signals opens the door to possibilities such as the assessment of consciousness, brain computer interfaces or diagnostic tools. We seek a method that works with a reduced number of variables, in order to avoid overfitting and to improve interpretability. This work aims to enhance EEG signal classification accuracy by using methods based on time series analysis. Previous work on this line, usually took a univariate approach, thus losing the possibility to take advantage of the correlation information existing within the time series provided by the different electrodes. To overcome this problem, we propose a multivariate approach that can fully capture the relationships among the different time series included in the EEG data. To perform the multivariate time series analysis, we use a multi-resolution analysis approach based on the discrete wavelet transform, together with a stepwise discriminant that selects the most discriminant variables provided by the discrete wavelet transform analysis RESULTS: Applying this methodology to EEG data to differentiate between the motor imagery tasks of moving either hands or feet has yielded very good classification results, achieving in some cases up to 100% of accuracy for this 2-class pre-processed dataset. Besides, the fact that these results were achieved using a reduced number of variables (55 out of 22,176) can shed light on the relevance and impact of those variables. CONCLUSIONS: This work has a potentially large impact, as it enables classification of EEG data based on multivariate time series analysis in an interpretable way with high accuracy. The method allows a model with a reduced number of features, facilitating its interpretability and improving overfitting. Future work will extend the application of this classification method to help in diagnosis procedures for detecting brain pathologies and for its use in brain computer interfaces. In addition, the results presented here suggest that this method could be applied to other fields for the successful analysis of multivariate temporal data.

Coronavirus disease identification using Multi-subband feature analysis in DWT domain.

Coronavirus disease early identification and differentiating it with other lung infections is a complex and time-consuming task. At present RT-PCR and Antigen tests are used for diagnosis, but the whole process is tedious, time exhausting and sometimes gives inaccurate results. Radiological scans like CT scan and X-rays are often considered for confirmation of infection, as it contains vital information about region of infection, disease state and severity, texture, size and opacity of infection. Automated machine learning techniques along with CXR (Chest X-ray) images can serve as alternative approach for Covid-19 diagnosis and differentiating it with other health conditions. In this work, Covid-19 disease identification is performed based on multi-subband feature extraction using 2D Discrete Wavelet Transform (DWT) on CX-Ray images. The CX-ray images are decomposed into multi-subbands of frequencies using DWT. The quarter-sized decomposed low and high frequency components are concatenated into single feature vector. In order to find suitable wavelet filter for extracting features from CX-ray images, a rigorous experimentation is carried out among various wavelet families such as Haar, Daubechies, Symlets, Biorthogonal and their respective members that have different vanishing moment and regularity properties. The feature vector is then used for training machine learning model based on support vector machine classifier. Experimental result shows that the classification model based on Haar wavelet feature extraction performs better as compared to other wavelet families with classification accuracy of 100%.

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals.

Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals.

Fingerprint-based robust medical image watermarking in hybrid transform.

To protect the medical images integrity, digital watermark is embedded into the medical images. A non-blind medical image watermarking scheme based on hybrid transform is propounded. In this paper, fingerprint of the patient is used as watermark for better authentication, identifying the original medical image and privacy of the patients. In this scheme, lifting wavelet transform (LWT) and discrete wavelet transform (DWT) are utilized for amplifying the watermarking algorithm. The scaling and embedding factors are calculated adaptively with the help of Local Binary Pattern values of the host medical image to achieve better imperceptibility and robustness for medical images and fingerprint watermark, respectively. Two-level decomposition is done where for the first level LWT is utilized and for the second level decomposition DWT is utilized. At the extraction side, non-blind recovery of fingerprint watermark is performed which is similar to the embedding process. The propounded design is implemented on various medical images like Chest X-ray, CT scan and so on. The propounded design provides better imperceptibility and robustness with the combination of LWT-DWT. The result analysis proves that the proposed fingerprint watermarking scheme has attained best results in terms of robustness and authentication with different medical image attacks. Peak Signal to Noise Ratio and Normalized Correlation Coefficient metrics are used for evaluating the proposed scheme. Furthermore, superior results are obtained when compared to related medical image watermarking schemes.

A DM-ELM based classifier for EEG brain signal classification for epileptic seizure detection.

Epilepsy is one of the dreaded conditions that had taken billions of people under its cloud worldwide. Detecting the seizure at the correct time in an individual is something that medical practitioners focus in order to help people save their lives. Analysis of the Electroencephalogram (EEG) signal from the scalp area of the human brain can help in detecting the seizure beforehand. This paper presents a novel classification technique to classify EEG brain signals for epilepsy identification based on Discrete Wavelet Transform and Moth Flame Optimization-based Extreme Learning Machine (DM-ELM). ELM is a very popular machine learning method based on Neural Networks (NN) where the model is trained rigorously to get the minimized error rate and maximized accuracy. Here we have used several experimental evaluations to compare the performance of basic ELM and DM-ELM and it has been experimentally proved that DM-ELM outperforms basic ELM but with few time constraints.

Study and analysis of different segmentation methods for brain tumor MRI application.

Medical Resonance Imaging (MRI) is one of the preferred imaging methods for brain tumor diagnosis and getting detailed information on tumor type, location, size, identification, and detection. Segmentation divides an image into multiple segments and describes the separation of the suspicious region from pre-processed MRI images to make the simpler image that is more meaningful and easier to examine. There are many segmentation methods, embedded with detection devices, and the response of each method is different. The study article focuses on comparing the performance of several image segmentation algorithms for brain tumor diagnosis, such as Otsu's, watershed, level set, K-means, HAAR Discrete Wavelet Transform (DWT), and Convolutional Neural Network (CNN). All of the techniques are simulated in MATLAB using online images from the Brain Tumor Image Segmentation Benchmark (BRATS) dataset-2018. The performance of these methods is analyzed based on response time and measures such as recall, precision, F-measures, and accuracy. The measured accuracy of Otsu's, watershed, level set, K-means, DWT, and CNN methods is 71.42%, 78.26%, 80.45%, 84.34%, 86.95%, and 91.39 respectively. The response time of CNN is 2.519 s in the MATLAB simulation environment for the designed algorithm. The novelty of the work is that CNN has been proven the best algorithm in comparison to all other methods for brain tumor image segmentation. The simulated and estimated parameters provide the direction to researchers to choose the specific algorithm for embedded hardware solutions and develop the optimal machine-learning models, as the industries are looking for the optimal solutions of CNN and deep learning-based hardware models for the brain tumor.

Detection of Alzheimer's Dementia by Using Signal Decomposition and Machine Learning Methods.

Dementia is one of the most common neurological disorders causing defection of cognitive functions, and seriously affects the quality of life. In this study, various methods have been proposed for the detection and follow-up of Alzheimer's dementia (AD) with advanced signal processing methods by using electroencephalography (EEG) signals. Signal decomposition-based approaches such as empirical mode decomposition (EMD), ensemble EMD (EEMD), and discrete wavelet transform (DWT) are presented to classify EEG segments of control subjects (CSs) and AD patients. Intrinsic mode functions (IMFs) are obtained from the signals using the EMD and EEMD methods, and the IMFs showing the most significant differences between the two groups are selected by applying previously suggested selection procedures. Five-time-domain and 5-spectral-domain features are calculated using selected IMFs, and five detail and approximation coefficients of DWT. Signal decomposition processes are conducted for both 1 min and 5 s EEG segment durations. For the 1 min segment duration, all the proposed approaches yield prominent classification performances. While the highest classification accuracies are obtained using EMD (91.8%) and EEMD (94.1%) approaches from the temporal/right brain cluster, the highest classification accuracy for the DWT (95.2%) approach is obtained from the temporal/left brain cluster for 1 min segment duration.

A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices.

The quick diagnosis of the novel coronavirus (COVID-19) disease is vital to prevent its propagation and improve therapeutic outcomes. Computed tomography (CT) is believed to be an effective tool for diagnosing COVID-19, however, the CT scan contains hundreds of slices that are complex to be analyzed and could cause delays in diagnosis. Artificial intelligence (AI) especially deep learning (DL), could facilitate and speed up COVID-19 diagnosis from such scans. Several studies employed DL approaches based on 2D CT images from a single view, nevertheless, 3D multiview CT slices demonstrated an excellent ability to enhance the efficiency of COVID-19 diagnosis. The majority of DL-based studies utilized the spatial information of the original CT images to train their models, though, using spectral-temporal information could improve the detection of COVID-19. This article proposes a DL-based pipeline called CoviWavNet for the automatic diagnosis of COVID-19. CoviWavNet uses a 3D multiview dataset called OMNIAHCOV. Initially, it analyzes the CT slices using multilevel discrete wavelet decomposition (DWT) and then uses the heatmaps of the approximation levels to train three ResNet CNN models. These ResNets use the spectral-temporal information of such images to perform classification. Subsequently, it investigates whether the combination of spatial information with spectral-temporal information could improve the diagnostic accuracy of COVID-19. For this purpose, it extracts deep spectral-temporal features from such ResNets using transfer learning and integrates them with deep spatial features extracted from the same ResNets trained with the original CT slices. Then, it utilizes a feature selection step to reduce the dimension of such integrated features and use them as inputs to three support vector machine (SVM) classifiers. To further validate the performance of CoviWavNet, a publicly available benchmark dataset called SARS-COV-2-CT-Scan is employed. The results of CoviWavNet have demonstrated that using the spectral-temporal information of the DWT heatmap images to train the ResNets is superior to utilizing the spatial information of the original CT images. Furthermore, integrating deep spectral-temporal features with deep spatial features has enhanced the classification accuracy of the three SVM classifiers reaching a final accuracy of 99.33% and 99.7% for the OMNIAHCOV and SARS-COV-2-CT-Scan datasets respectively. These accuracies verify the outstanding performance of CoviWavNet compared to other related studies. Thus, CoviWavNet can help radiologists in the rapid and accurate diagnosis of COVID-19 diagnosis.

A sophisticated and provably grayscale image watermarking system using DWT-SVD domain.

Digital watermarking has attracted increasing attentions as it has been the current solution to copyright protection and content authentication in today's digital transformation, which has become an issue to be addressed in multimedia technology. In this paper, we propose an advanced image watermarking system based on the discrete wavelet transform (DWT) in combination with the singular value decomposition (SVD). Firstly, at the sender side, DWT is applied on a grayscale cover image and then eigendecomposition is performed on original HH (high-high) components. Similar operation is done on a grayscale watermark image. Then, two unitary and one diagonal matrices are combined to form a digital watermarked image applying inverse discrete wavelet transform (iDWT). The diagonal component of original image is transmitted through secured channel. At the receiver end, the watermark image is recovered using the watermarked image and diagonal component of the original image. Finally, we compare the original and recovered watermark image and obtained perfect normalized correlation. Simulation consequences indicate that the presented scheme can satisfy the needs of visual imperceptibility and also has high security and strong robustness against many common attacks and signal processing operations. The proposed digital image watermarking system is also compared to state-of-the-art methods to confirm the reliability and supremacy.

Wind-Direction Estimation from Single X-Band Marine Radar Image Improvement by Utilizing the DWT and Azimuth-Scale Expansion Method.

In this study, a method based on the discrete wavelet transform (DWT) and azimuth-scale expansion is presented to retrieve the sea-surface wind direction from a single X-band marine radar image. The algorithm first distinguishes rain-free and rain-contaminated radar images based on the occlusion zero-pixel percentage and then discards the rain-contaminated images. The radar image whose occlusion areas have been removed is decomposed into different low-frequency sub-images by the 2D DWT, and the appropriate low-frequency sub-image is selected. Images collected with a standard marine HH-polarized X-band radar operating at grazing incidence display a single intensity peak in the upwind direction. To overcome the influence of the occlusion area, before determining the wind direction, the data near the ship bow are shifted to expand the azimuth scale of the data. Finally, a harmonic function is least-square-fitted to the range-averaged radar return of the low-frequency sub-image as a function of the antenna look azimuth to determine the wind direction. Different from the wind-direction retrieval algorithms previously presented, this method is more suitable for sailing ships, as it functions well even if the radar data are heavily blocked. The results show that compared with the single-curve fitting algorithm, the algorithm based on DWT and azimuth-scale expansion can improve the wind-direction results in sailing ships, showing a reduction of 7.84° in the root-mean-square error with respect to the reference.

Seamless Copy-Move Replication in Digital Images.

The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy-move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain 3l+1 sub-bands, and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. Resulting manipulated host sub-bands are then subjected to inverse DWT to obtain the final manipulated host image. The proposed method shows good resistance against detection by two frequency-domain forgery detection methods from the literature. The purpose of this research work is to create a forgery and highlight the need to produce forgery detection methods that are robust against malicious copy-move forgery.

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration.

The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of the virus, alleviate lockdown constraints and diminish the burden on health organizations. Currently, the methods used to diagnose COVID-19 have several limitations, thus new techniques need to be investigated to improve the diagnosis and overcome these limitations. Taking into consideration the great benefits of electrocardiogram (ECG) applications, this paper proposes a new pipeline called ECG-BiCoNet to investigate the potential of using ECG data for diagnosing COVID-19. ECG-BiCoNet employs five deep learning models of distinct structural design. ECG-BiCoNet extracts two levels of features from two different layers of each deep learning technique. Features mined from higher layers are fused using discrete wavelet transform and then integrated with lower-layers features. Afterward, a feature selection approach is utilized. Finally, an ensemble classification system is built to merge predictions of three machine learning classifiers. ECG-BiCoNet accomplishes two classification categories, binary and multiclass. The results of ECG-BiCoNet present a promising COVID-19 performance with an accuracy of 98.8% and 91.73% for binary and multiclass classification categories. These results verify that ECG data may be used to diagnose COVID-19 which can help clinicians in the automatic diagnosis and overcome limitations of manual diagnosis.

A novel approach to automatic sleep stage classification using forehead electrophysiological signals.

Background: Sleep stage scoring is very important for the effective diagnosis and intervention of sleep disorders. However, the current automatic sleep staging methods generally have the problems of poor model generalization ability and non-portable acquisition equipment. Method: In this paper, we propose a novel automatic sleep scoring system based on forehead electrophysiological signals that is more effective and convenient than other systems. We extract 3 channel signals from the forehead, named forehead electroencephalogram 1 (Fh1), forehead electroencephalogram 2 (Fh2), and forehead center electrooculogram (Fhz). Spectral features, statistical features, and entropy features are extracted using the discrete wavelet transform (DWT) method. Light gradient boosting machine (LGB), random forest (RF), and support vector machine (SVM) are employed to classify four stages: awake, light sleep (LS), deep sleep (DS), and rapid eye movement (REM). Result: The performance of the proposed method is validated using databases of the Sleep-EDFX and our Own-data, which include polysomnograms (PSGs) and forehead signals of 28 subjects. The overall classification accuracy of using the combination of Fh1, Fh2, and Fhz can reach up to 90.25% accuracy with a kappa coefficient of 0.857. Conclusions: The proposed method could provide state-of-art multichannel sleep stage scoring performance with higher portability. This will facilitate the application of long-term monitoring of sleep quality in the future.

An empirical comparison of machine learning algorithms for the classification of brain signals to assess the impact of combined yoga and Sudarshan Kriya.

Today's fast paced life reports so much stress among people that it may lead to various psychological and physical illnesses. Yoga and meditation are the best strategies to reduce the effect of stress on physical and mental level without any side-effects. In this study, combined yoga and Sudarshan Kriya (SK) has been used as an alternative and complementary therapy for the management of stress. The aim of the study is to find a method to classify the meditator and non-meditator states with the best accuracy. The 50 subjects have been participating in this study and divided into two groups, i.e. study and control group. The subjects with regular practice of Yoga and SK are known as meditators and the ones without any practice of yoga and meditation were known as non-meditators. Electroencephalogram (EEG) signals were acquired from these both groups before and after 3 months. The statistical parameters were computed from these acquired EEG signals using Discrete Wavelet Transform (DWT). These extracted statistical parameters were given as input to the classifiers. The decision tree, discriminant analysis, logistic regression, Support Vector Machine (SVM), Weighted K- Nearest Neighbour (KNN) and ensemble classifiers were used for classification of meditator and non- meditator states from the acquired EEG signals. The results have demonstrated that the SVM method gives the highest classification accuracy as compared to other classifiers. The proposed method can be used as a diagnosis system in clinical practices.