Performance Evaluation of Wavelet De-Noising Schemes for Suppression of Power Line Noise in Electrocardiogram Signals

Power line noise introduces distortions to recorded electrocardiogram (ECG) signals. These distortions compromise the integrity and negatively affect the interpretation of the ECG signals. Despite the fact that the amplifiers used in biomedical signal processing have high common mode rejection ratio (CMRR), ECG recordings are still often corrupted with residual Power Line Interference (PLI) noise. Further improvement in the hardware solutions do not have significant achievements in PLI noise suppression but rather introduce other adverse effects. Software approach is necessary to refine ECG data. Evaluation of PLI noise suppression in ECG signal in the wavelet domain is presented. The performance of the Hard Threshold Shrinkage Function (HTSF), the Soft Threshold Shrinkage Function (STSF), the Hyperbola Threshold Shrinkage Function (HYTSF), the Garrote Threshold Shrinkage Function (GTSF), and the Modified Garrote Threshold Shrinkage Function (MGTSF) for the suppression of PLI noise are evaluated and compared with the aid of an algorithm. The optimum tuning constant for the Modified Garrote Threshold Shrinkage Function (MGTSF) is found to be 1.18 for PLI noise. GTSF is found to have best performance closely followed by MGTSF in term of filtering Gain. HTSF recorded the lowest Gain. Filtering against PLI noise in the wavelet domain preserves the key features of the signal such as the QRS complex

Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

Exploring ECG Signal Analysis Techniques for Arrhythmia Detection: A Review

The heart holds paramount importance in the human body as it serves the crucial function of supplying blood and nutrients to various organs. Thus, maintaining its health is imperative. Arrhythmia, a heart disorder, arises when the heart's rhythm becomes irregular. Electrocardiogram (ECG) signals are commonly utilized for analyzing arrhythmia due to their simplicity and cost-effectiveness. The peaks observed in ECG graphs, particularly the R peak, are indicative of heart conditions, facilitating arrhythmia diagnosis. Arrhythmia is broadly categorized into Tachycardia and Bradycardia for identification purposes. This paper explores diverse techniques such as Deep Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVM), Neural Network (NN) classifiers, as well as Wavelet and Time–Frequency Transform (TQWT), which have been employed over the past decade for arrhythmia detection using various datasets. The study delves into the analysis of arrhythmia classification on ECG datasets, highlighting the effectiveness of data preprocessing, feature extraction, and classification techniques in achieving superior performance in classifying ECG signals for arrhythmia detection

LMS Adaptive Filters for Noise Cancellation: A Review

This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted

Synergistic Effects of Phase Folding and Wavelet Denoising with Applications in Light Curve Analysis

The growing size of cosmological data sets is causing the current human-centric approach to cosmology to become impractical. Autonomous data analysis techniques need to be developed in order to advance the field of cosmology. This research examines the benefits of combining two signal analysis techniques, namely phase folding and wavelet denoising, into a newly-developed suite of autonomous light curve analysis tools which includes aspects of component extraction and period detection. The improvements these tools provide, with respect to autonomy and signal quality, are demonstrated using both simulated and real-world light curve data. Although applied to light curve data, the suite of tools developed in this dissertation are advantageous to the processing, modeling, or extractions to any periodic signal analysis

Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research

This PhD thesis presents the development of a novel open multi-modal dataset for advanced studies on fetal cardiological assessment, along with a set of signal processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography (ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological recordings characterized by high sampling frequency and digital resolution, maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave Doppler (PWD) recordings and clinical annotations provided by expert clinicians at the time of the signal collection. To the best of our knowledge, there are no similar dataset available. The signal processing tools targeted both the PWD and the non-invasive fetal ECG, exploiting the recorded dataset. About the former, the study focuses on the processing aimed at the preparation of the signal for the automatic measurement of relevant morphological features, already adopted in the clinical practice for cardiac assessment. To this aim, a relevant step is the automatic identification of the complete and measurable cardiac cycles in the PWD videos: a rigorous methodology was deployed for the analysis of the different processing steps involved in the automatic delineation of the PWD envelope, then implementing different approaches for the supervised classification of the cardiac cycles, discriminating between complete and measurable vs. malformed or incomplete ones. Finally, preliminary measurement algorithms were also developed in order to extract clinically relevant parameters from the PWD. About the fetal ECG, this thesis concentrated on the systematic analysis of the adaptive filters performance for non-invasive fetal ECG extraction processing, identified as the reference tool throughout the thesis. Then, two studies are reported: one on the wavelet-based denoising of the extracted fetal ECG and another one on the fetal ECG quality assessment from the analysis of the raw abdominal recordings. Overall, the thesis represents an important milestone in the field, by promoting the open-data approach and introducing automated analysis tools that could be easily integrated in future medical devices

On Kalman smoothing with random packet loss

The abstract is included in the text

Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Processing Capability

The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing