Noisy iris smoothing and segmentation scheme based on improved Wildes method

In an automated iris recognition system, in order to get higher accuracy, we should have an efficient iris segmentation process. The reliability of accurate "iris recognition" system largely depends on the accuracy of segmentation process. Traditional "iris segmentation" methods are unable to detect the exact boundaries of iris and pupil, which is time consuming and also highly sensitive to noise. To overcome these problems, we have proposed an improved Wildes method (IWM) for segmentation in iris recognition system. The proposed algorithm consists of two major steps before applying Wildes method for segmentation: edge detection of iris and pupil from a noisy eye image with improved Canny with fuzzy logic (ICWFL) and removal of unwanted noise from above step with a hybrid restoration fusion filter (HRFF). A comparative study of various edge detection techniques is performed to prove the efficiency of ICWFL method. Similarly, the proposed method is tested with various noise densities from 10 to 95 dB. Also the working of the proposed HRFF is compared with some existing smoothing filters. Various experiments have been performed with the help of iris database of IIT_Delhi. Both visual and numerical results prove the efficiency of the proposed algorithm.

Design and Embedded Implementation of Secure Image Encryption Scheme Using DWT and 2D-LASM

In order to further improve the information effectiveness of digital image transmission, an image-encryption algorithm based on 2D-Logistic-adjusted-Sine map (2D-LASM) and Discrete Wavelet Transform (DWT) is proposed. First, a dynamic key with plaintext correlation is generated using Message-Digest Algorithm 5 (MD5), and 2D-LASM chaos is generated based on the key to obtain a chaotic pseudo-random sequence. Secondly, we perform DWT on the plaintext image to map the image from the time domain to the frequency domain and decompose the low-frequency (LF) coefficient and high-frequency (HF) coefficient. Then, the chaotic sequence is used to encrypt the LF coefficient with the structure of "confusion-permutation". We perform the permutation operation on HF coefficient, and we reconstruct the image of the processed LF coefficient and HF coefficient to obtain the frequency-domain ciphertext image. Finally, the ciphertext is dynamically diffused using the chaotic sequence to obtain the final ciphertext. Theoretical analysis and simulation experiments show that the algorithm has a large key space and can effectively resist various attacks. Compared with the spatial-domain algorithms, this algorithm has great advantages in terms of computational complexity, security performance, and encryption efficiency. At the same time, it provides better concealment of the encrypted image while ensuring the encryption efficiency compared to existing frequency-domain methods. The successful implementation on the embedded device in the optical network environment verifies the experimental feasibility of this algorithm in the new network application.

Wavelet-Domain Information-Hiding Technology with High-Quality Audio Signals on MEMS Sensors

Due to the rapid development of sensor technology and the popularity of the Internet, not only has the amount of digital information transmission skyrocketed, but also its acquisition and dissemination has become easier. The study mainly investigates audio security issues with data compression for private data transmission on the Internet or MEMS (micro-electro-mechanical systems) audio sensor digital microphones. Imperceptibility, embedding capacity, and robustness are three main requirements for audio information-hiding techniques. To achieve the three main requirements, this study proposes a high-quality audio information-hiding technology in the wavelet domain. Due to the fact that wavelet domain provides a useful and robust platform for audio information hiding, this study applies multi-coefficients of discrete wavelet transform (DWT) to hide information. By considering a good, imperceptible concealment, we combine signal-to-noise ratio (SNR) with quantization embedding for these coefficients in a mathematical model. Moreover, amplitude-thresholding compression technology is combined in this model. Finally, the matrix-type Lagrange principle plays an essential role in solving the model so as to reduce the carrying capacity of network transmission while protecting personal copyright or private information. Based on the experimental results, we nearly maintained the original quality of the embedded audio by optimization of signal-to-noise ratio (SNR). Moreover, the proposed method has good robustness against common attacks.

Iris recognition approach for identity verification with DWT and multiclass SVM

The iris has been proven to be one of the most stable and accurate biometrics. It has been widely used in recognition systems to determine the identity of the individual who attempts to access secured or restricted areas (e.g., airports, ATM, datacenters). An iris recognition (IR) technique for identity authentication/verification is proposed in this research. Iris image pre-processing, which includes iris segmentation, normalization, and enhancement, is followed by feature extraction, and matching. First, the iris image is segmented using the Hough Transform technique. The Daugman's rubber sheet model is the used to normalize the segmented iris area. Then, using enhancing techniques (such as histogram equalization), Gabor wavelets and Discrete Wavelets Transform should be used to precisely extract the prominent characteristics. A multiclass Support Vector Machine (SVM) is used to assess the similarity of the images. The suggested method is evaluated using the IITD iris dataset, which is one of the most often used iris datasets. The benefit of the suggested method is that it reduces the number of features in each image to only 88. Experiments revealed that the proposed method was capable of collecting a moderate quantity of useful features and outperformed other methods. Furthermore, the proposed method's recognition accuracy was found to be 98.92% on tested data.

CNN Deep Learning with Wavelet Image Fusion of CCD RGB-IR and Depth-Grayscale Sensor Data for Hand Gesture Intention Recognition

Pixel-based images captured by a charge-coupled device (CCD) with infrared (IR) LEDs around the image sensor are the well-known CCD Red-Green-Blue IR (the so-called CCD RGB-IR) data. The CCD RGB-IR data are generally acquired for video surveillance applications. Currently, CCD RGB-IR information has been further used to perform human gesture recognition on surveillance. Gesture recognition, including hand gesture intention recognition, is attracting great attention in the field of deep neural network (DNN) calculations. For further enhancing conventional CCD RGB-IR gesture recognition by DNN, this work proposes a deep learning framework for gesture recognition where a convolution neural network (CNN) incorporated with wavelet image fusion of CCD RGB-IR and additional depth-based depth-grayscale images (captured from depth sensors of the famous Microsoft Kinect device) is constructed for gesture intention recognition. In the proposed CNN with wavelet image fusion, a five-level discrete wavelet transformation (DWT) with three different wavelet decomposition merge strategies, namely, max-min, min-max and mean-mean, is employed; the visual geometry group (VGG)-16 CNN is used for deep learning and recognition of the wavelet fused gesture images. Experiments on the classifications of ten hand gesture intention actions (specified in a scenario of laboratory interactions) show that by additionally incorporating depth-grayscale data into CCD RGB-IR gesture recognition one will be able to further increase the averaged recognition accuracy to 83.88% for the VGG-16 CNN with min-max wavelet image fusion of the CCD RGB-IR and depth-grayscale data, which is obviously superior to the 75.33% of VGG-16 CNN with only CCD RGB-IR.

Tetromino pattern based accurate EEG emotion classification model

Nowadays, emotion recognition using electroencephalogram (EEG) signals is becoming a hot research topic. The aim of this paper is to classify emotions of EEG signals using a novel game-based feature generation function with high accuracy. Hence, a multileveled handcrafted feature generation automated emotion classification model using EEG signals is presented. A novel textural features generation method inspired by the Tetris game called Tetromino is proposed in this work. The Tetris game is one of the famous games worldwide, which uses various characters in the game. First, the EEG signals are subjected to discrete wavelet transform (DWT) to create various decomposition levels. Then, novel features are generated from the decomposed DWT sub-bands using the Tetromino method. Next, the maximum relevance minimum redundancy (mRMR) features selection method is utilized to select the most discriminative features, and the selected features are classified using support vector machine classifier. Finally, each channel's results (validation predictions) are obtained, and the mode function-based voting method is used to obtain the general results. We have validated our developed model using three databases (DREAMER, GAMEEMO, and DEAP). We have attained 100% accuracies using DREAMER and GAMEEMO datasets. Furthermore, over 99% of classification accuracy is achieved for DEAP dataset. Thus, our developed emotion detection model has yielded the best classification accuracy rate compared to the state-of-the-art techniques and is ready to be tested for clinical application after validating with more diverse datasets. Our results show the success of the presented Tetromino pattern-based EEG signal classification model validated using three public emotional EEG datasets.

A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection

In this paper, a robust hybrid watermarking method based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and scale-invariant feature transformation (SIFT) is proposed. Indeed, it is of prime interest to develop robust feature-based image watermarking schemes to withstand both image processing attacks and geometric distortions while preserving good imperceptibility. To this end, a robust watermark is embedded in the DWT-DCT domain to withstand image processing manipulations, while SIFT is used to protect the watermark from geometric attacks. First, the watermark is embedded in the middle band of the discrete cosine transform (DCT) coefficients of the HL1 band of the discrete wavelet transform (DWT). Then, the SIFT feature points are registered to be used in the extraction process to correct the geometric transformations. Extensive experiments have been conducted to assess the effectiveness of the proposed scheme. The results demonstrate its high robustness against standard image processing attacks and geometric manipulations while preserving a high imperceptibility. Furthermore, it compares favorably with alternative methods.

ECG denoising and feature extraction techniques - a review

The electrocardiogram (ECG) is a non-invasive approach for the recording of bioelectric signals generated by the heart which is used for the examination of the electro physical state, the function of the heart, and many cardiac diseases. However, various artefacts and measurement noise usually hinder providing accurate feature extraction such as power line interference, baseline wander, electromyographic noise (EMG) and electrode motion artefact. Therefore, for better analysis and interpretation ECG signals must be noise-free. Most recent and efficient techniques for ECG denoising and feature extraction techniques have been reviewed in this paper, as feature extraction and denoising of ECG are remarkably helpful in cardiology. This paper presents the review of contemporary signal processing techniques such as discrete wavelet transform (DWT), Empirical mode decomposition (EMD), Variational mode decomposition (VMD) and Empirical wavelet transform (EWT) for ECG signal denoising and feature extraction.

A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI

PURPOSE

Task-based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of a disease or other condition on the functional activity of the brain. Autism spectrum disorder (ASD) is a pervasive developmental disorder associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized for brain imaging aiming for objective ASD diagnostics. Recently, deep learning methods have been gaining more attention for fMRI classification. The goal of this paper is to develop a convolutional neural network (CNN)-based framework to help in global diagnosis of ASD using TfMRI data that are collected from a response to speech experiment.

METHODS

To achieve this goal, the proposed framework adopts a novel imaging marker integrating both spatial and temporal information that are related to the functional activity of the brain. The developed pipeline consists of three main components. In the first step, the collected TfMRI data are preprocessed and parcellated using the Harvard-Oxford probabilistic atlas included with the fMRIB Software Library (FSL). Second, a group analysis using FSL is performed between ASD and typically developing (TD) children to identify significantly activated brain areas in response to the speech task. In order to reduce brain spatial dimensionality, a K-means clustering technique is performed on such significant brain areas. Informative blood oxygen level-dependent (BOLD) signals are extracted from each cluster. A compression step for each extracted BOLD signal using discrete wavelet transform (DWT) has been proposed. The adopted wavelets are similar to the expected hemodynamic response which enables DWT to compress the BOLD signal while highlighting its activation information. Finally, a deep learning 2D CNN network is used to classify the patients as ASD or TD based on extracted features from the previous step.

RESULTS

Preliminary results on 100 TfMRI dataset (50 ASD, 50 TD) obtain 80% correct global classification using tenfold cross validation (with sensitivity = 84%, specificity = 76%).

CONCLUSION

The experimental results show the high accuracy of the proposed framework and hold promise for the presented framework as a helpful adjunct to currently used ASD diagnostic tools.

Twin Support Vector Regression for complex millimetric wave propagation environment

In this article, an effective millimetric wave channel estimation algorithm based on Twin Support Vector Regression (TSVR) is proposed. This algorithm exploits Discrete Wavelet Transform (DWT) in order to denoise samples in learning phase and then enhance fitting performance. An indoor complex conference room environment full of furniture and electronic equipments is adopted for experiments. Through the proposed approach, channel frequency responses are directly estimated using the Orthogonal Frequency Division Multiplexing (OFDM) reference symbol pattern by solving two quadratic programming problems in order to improve generalization aptitude and computational speed. We consider in this work a Channel Impulse Response (CIR) of 60 GHz multipath transmission system generated by the “Wireless InSite” ray tracer by Remcom. The numerical experiments confirm the performance of the proposed approach compared to other conventional algorithms for several configuration scenarios with and without mobility.

Dictionary selection for compressed sensing of EEG signals using sparse binary matrix and spatiotemporal sparse Bayesian learning

Online monitoring of electroencephalogram (EEG) signals is challenging due to the high volume of data and power requirements. Compressed sensing (CS) may be employed to address these issues. Compressed sensing using a sparse binary matrix, owing to its low power features, and reconstruction/decompression using spatiotemporal sparse Bayesian learning have been shown to constitute a robust framework for fast, energy efficient and accurate multichannel bio-signal monitoring. EEG signal, however, does not show a strong temporal correlation. Therefore, the use of sparsifying dictionaries has been proposed to exploit the sparsity in a transformed domain instead. Assuming sparsification adds values, a challenge, therefore, in employing this CS framework for the EEG signal, is to identify the suitable dictionary. Using real multichannel EEG data from 15 subjects, in this paper, we systematically evaluate the performance of the framework when using various wavelet bases while considering their key attributes namely number of vanishing moments and coherence with sensing matrix. We identified Beylkin as the wavelet dictionary leading to the best performance. Using the same dataset, we then compared the performance of Beylkin with the discrete cosine basis, often used in the literature, and the alternative of not using a sparsifying dictionary. We further demonstrate that using dictionaries (Beylkin and Discrete Cosine Transform (DCT)) may improve performance tangibly only for a high compression ratio (CR) of 80% and with smaller block sizes, as compared to using no dictionaries.

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks

Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (CNN) in feature extraction, the power of discrete wavelet transform (DWT) in signal processing, and the power of long short-term memory (LSTM) in signal classification. A CNN-DWT-LSTM method is proposed to classify the computed tomography (CT) images of livers with tumors and to classify the magnetic resonance (MR) images of brains with tumors. The proposed method classifies liver tumors images as benign or malignant and then classifies brain tumor images as meningioma, glioma, and pituitary. In the hybrid CNN-DWT-LSTM method, the feature vector of the images is obtained from pre-trained AlexNet CNN architecture. The feature vector is reduced but strengthened by applying the single-level one-dimensional discrete wavelet transform (1-D DWT), and it is classified by training with an LSTM network. Under the scope of the study, images of 56 benign and 56 malignant liver tumors that were obtained from Fırat University Research Hospital were used and a publicly available brain tumor dataset were used. The experimental results show that the proposed method had higher performance than classifiers, such as K-nearest neighbors (KNN) and support vector machine (SVM). By using the CNN-DWT-LSTM hybrid method, an accuracy rate of 99.1% was achieved in the liver tumor classification and accuracy rate of 98.6% was achieved in the brain tumor classification. We used two different datasets to demonstrate the performance of the proposed method. Performance measurements show that the proposed method has a satisfactory accuracy rate at the liver tumor and brain tumor classifying.

The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences

Advances in DNA sequencing technology are facilitating genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and fully exploit biological sequence data. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction (i.e., compression) methods are routinely used to lessen the computational burden of analyses. In this work, we explored the application of dimensionality reduction methods to numerically represent high-throughput sequence data for three important biological applications of virus sequence data: reference-based mapping, short sequence classification and de novo assembly. Leveraging highly compressed sequence transformations to accelerate sequence comparison, our approach yielded comparable accuracy to existing approaches, further demonstrating its suitability for sequences originating from diverse virus populations. We assessed the application of our methodology using both synthetic and real viral pathogen sequences. Our results show that the use of highly compressed sequence approximations can provide accurate results, with analytical performance retained and even enhanced through appropriate dimensionality reduction of sequence data.

A fast iris recognition system through optimum feature extraction

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris template classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA

Neuroscientists have investigated the functionality of the brain in detail and achieved remarkable results but this area still need further research. Functional magnetic resonance imaging (fMRI) is considered as the most reliable and accurate technique to decode the human brain activity, on the other hand electroencephalography (EEG) is a portable and low cost solution in brain research. The purpose of this study is to find whether EEG can be used to decode the brain activity patterns like fMRI. In fMRI, data from a very specific brain region is enough to decode the brain activity patterns due to the quality of data. On the other hand, EEG can measure the rapid changes in neuronal activity patterns due to its higher temporal resolution i.e., in msec. These rapid changes mostly occur in different brain regions. In this study, multivariate pattern analysis (MVPA) is used both for EEG and fMRI data analysis and the information is extracted from distributed activation patterns of the brain. The significant information among different classes is extracted using two sample t test in both data sets. Finally, the classification analysis is done using the support vector machine. A fair comparison of both data sets is done using the same analysis techniques, moreover simultaneously collected data of EEG and fMRI is used for this comparison. The final analysis is done with the data of eight participants; the average result of all conditions are found which is 65.7% for EEG data set and 64.1% for fMRI data set. It concludes that EEG is capable of doing brain decoding with the data from multiple brain regions. In other words, decoding accuracy with EEG MVPA is as good as fMRI MVPA and is above chance level.

Encryption and watermark-treated medical image against hacking disease-An immune convention in spatial and frequency domains

Digital Imaging and Communications in Medicine (DICOM) is one among the significant formats used worldwide for the representation of medical images. Undoubtedly, medical-image security plays a crucial role in telemedicine applications. Merging encryption and watermarking in medical-image protection paves the way for enhancing the authentication and safer transmission over open channels. In this context, the present work on DICOM image encryption has employed a fuzzy chaotic map for encryption and the Discrete Wavelet Transform (DWT) for watermarking. The proposed approach overcomes the limitation of the Arnold transform-one of the most utilised confusion mechanisms in image ciphering. Various metrics have substantiated the effectiveness of the proposed medical-image encryption algorithm.

Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network

The identification, segmentation and detection of infecting area in brain tumor MRI images are a tedious and time-consuming task. The different anatomy structure of human body can be visualized by an image processing concepts. It is very difficult to have vision about the abnormal structures of human brain using simple imaging techniques. Magnetic resonance imaging technique distinguishes and clarifies the neural architecture of human brain. MRI technique contains many imaging modalities that scans and capture the internal structure of human brain. In this study, we have concentrated on noise removal technique, extraction of gray-level co-occurrence matrix (GLCM) features, DWT-based brain tumor region growing segmentation to reduce the complexity and improve the performance. This was followed by morphological filtering which removes the noise that can be formed after segmentation. The probabilistic neural network classifier was used to train and test the performance accuracy in the detection of tumor location in brain MRI images. The experimental results achieved nearly 100% accuracy in identifying normal and abnormal tissues from brain MR images demonstrating the effectiveness of the proposed technique.

Emotion recognition from multichannel EEG signals using K-nearest neighbor classification

BACKGROUND

Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals.

OBJECTIVE

This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels.

METHODS

We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier.

RESULTS

The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands.

CONCLUSIONS

This paper provided better frequency bands and channels reference for emotion recognition based on EEG.

A Novel Real-Time Genome Comparison Method Using Discrete Wavelet Transform

Real-time genome comparison is important for identifying unknown species and clustering organisms. We propose a novel method that can represent genome sequences of different lengths as a 12-dimensional numerical vector in real time for this purpose. Given a genome sequence, a binary indicator sequence of each nucleotide base location is computed, and then discrete wavelet transform is applied to these four binary indicator sequences to attain the respective power spectra. Afterward, moments of the power spectra are calculated. Consequently, the 12-dimensional numerical vectors are constructed from the first three order moments. Our experimental results on various data sets show that the proposed method is efficient and effective to cluster genes and genomes. It runs significantly faster than other alignment-free and alignment-based methods.

Crypto-Watermarking of Transmitted Medical Images

Telemedicine is a booming healthcare practice that has facilitated the exchange of medical data and expertise between healthcare entities. However, the widespread use of telemedicine applications requires a secured scheme to guarantee confidentiality and verify authenticity and integrity of exchanged medical data. In this paper, we describe a region-based, crypto-watermarking algorithm capable of providing confidentiality, authenticity, and integrity for medical images of different modalities. The proposed algorithm provides authenticity by embedding robust watermarks in images' region of non-interest using SVD in the DWT domain. Integrity is provided in two levels: strict integrity implemented by a cryptographic hash watermark, and content-based integrity implemented by a symmetric encryption-based tamper localization scheme. Confidentiality is achieved as a byproduct of hiding patient's data in the image. Performance of the algorithm was evaluated with respect to imperceptibility, robustness, capacity, and tamper localization, using different medical images. The results showed the effectiveness of the algorithm in providing security for telemedicine applications.

Image denoising in bidimensional empirical mode decomposition domain: the role of Student's probability distribution function

Hybridisation of the bi-dimensional empirical mode decomposition (BEMD) with denoising techniques has been proposed in the literature as an effective approach for image denoising. In this Letter, the Student's probability density function is introduced in the computation of the mean envelope of the data during the BEMD sifting process to make it robust to values that are far from the mean. The resulting BEMD is denoted tBEMD. In order to show the effectiveness of the tBEMD, several image denoising techniques in tBEMD domain are employed; namely, fourth order partial differential equation (PDE), linear complex diffusion process (LCDP), non-linear complex diffusion process (NLCDP), and the discrete wavelet transform (DWT). Two biomedical images and a standard digital image were considered for experiments. The original images were corrupted with additive Gaussian noise with three different levels. Based on peak-signal-to-noise ratio, the experimental results show that PDE, LCDP, NLCDP, and DWT all perform better in the tBEMD than in the classical BEMD domain. It is also found that tBEMD is faster than classical BEMD when the noise level is low. When it is high, the computational cost in terms of processing time is similar. The effectiveness of the presented approach makes it promising for clinical applications.

Comparison study of EMG signals compression by methods transform using vector quantization, SPIHT and arithmetic coding

In this article, we make a comparative study for a new approach compression between discrete cosine transform (DCT) and discrete wavelet transform (DWT). We seek the transform proper to vector quantization to compress the EMG signals. To do this, we initially associated vector quantization and DCT, then vector quantization and DWT. The coding phase is made by the SPIHT coding (set partitioning in hierarchical trees coding) associated with the arithmetic coding. The method is demonstrated and evaluated on actual EMG data. Objective performance evaluations metrics are presented: compression factor, percentage root mean square difference and signal to noise ratio. The results show that method based on the DWT is more efficient than the method based on the DCT.

A Robustness Comparison of Two Algorithms Used for EEG Spike Detection

Spikes and sharp waves recorded on scalp EEG may play an important role in identifying the epileptogenic network as well as in understanding the central nervous system. Therefore, several automatic and semi-automatic methods have been implemented to detect these two neural transients. A consistent gold standard associated with a high degree of agreement among neuroscientists is required to measure relevant performance of different methods. In fact, scalp EEG data can often be corrupted by a set of artifacts and are not always served as data of gold standard. For this reason, the use of intracerebral EEG data mixed with gaussian noise seems to best resemble the output of scalp EEG brain and serves as a consistent gold standard. In the present framework, we test the robustness of two important methods that have been previously used for the automatic detection of epileptiform transients (spikes and sharp waves). These methods are based respectively on Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT). Our purpose is to elaborate a comparative study in terms of sensitivity and selectivity changes via the decrease of Signal to Noise Ratio (SNR), which is ranged from 10 dB up to -10 dB. The results demonstrate that, DWT approach turns to be more stable in terms of sensitivity, and it successfully follows the detection of relevant spikes with the decrease of SNR. However, CWT-based approach remains more stable in terms of selectivity, so that, it performs well in terms of rejecting false spikes compared to DWT approach.

Set of rules for genomic signal downsampling

Comparison and classification of organisms based on molecular data is an important task of computational biology, since at least parts of DNA sequences for many organisms are available. Unfortunately, methods for comparison are computationally very demanding, suitable only for short sequences. In this paper, we focus on the redundancy of genetic information stored in DNA sequences. We proposed rules for downsampling of DNA signals of cumulated phase. According to the length of an original sequence, we are able to significantly reduce the amount of data with only slight loss of original information. Dyadic wavelet transform was chosen for fast downsampling with minimum influence on signal shape carrying the biological information. We proved the usability of such new short signals by measuring percentage deviation of pairs of original and downsampled signals while maintaining spectral power of signals. Minimal loss of biological information was proved by measuring the Robinson-Foulds distance between pairs of phylogenetic trees reconstructed from the original and downsampled signals. The preservation of inter-species and intra-species information makes these signals suitable for fast sequence identification as well as for more detailed phylogeny reconstruction.

Hybrid fusion of linear, non-linear and spectral models for the dynamic modeling of sEMG and skeletal muscle force: an application to upper extremity amputation

Estimating skeletal muscle (finger) forces using surface Electromyography (sEMG) signals poses many challenges. In general, the sEMG measurements are based on single sensor data. In this paper, two novel hybrid fusion techniques for estimating the skeletal muscle force from the sEMG array sensors are proposed. The sEMG signals are pre-processed using five different filters: Butterworth, Chebychev Type II, Exponential, Half-Gaussian and Wavelet transforms. Dynamic models are extracted from the acquired data using Nonlinear Wiener Hammerstein (NLWH) models and Spectral Analysis Frequency Dependent Resolution (SPAFDR) models based system identification techniques. A detailed comparison is provided for the proposed filters and models using 18 healthy subjects. Wavelet transforms give higher mean correlation of 72.6 ± 1.7 (mean ± SD) and 70.4 ± 1.5 (mean ± SD) for NLWH and SPAFDR models, respectively, when compared to the other filters used in this work. Experimental verification of the fusion based hybrid models with wavelet transform shows a 96% mean correlation and 3.9% mean relative error with a standard deviation of ± 1.3 and ± 0.9 respectively between the overall hybrid fusion algorithm estimated and the actual force for 18 test subjects' k-fold cross validation data.