A Combined Model for Noise Reduction of Lung Sound Signals Based on Empirical Mode Decomposition and Artificial Neural Network

Computer analysis of Lung Sound (LS) signals has been proposed in recent years as a tool to analyze the lungs' status but there have always been main challenges, including the contamination of LS with environmental noises, which come from different sources of unlike intensities. One of the common methods in noise reduction of LS signals is based on thresholding on Discrete Wavelet Transform (DWT) coefficients or Empirical Mode Decomposition (EMD) of the signal, however, in these methods, it is necessary to calculate the SNR value to determine the appropriate threshold for noise removal. To solve this problem, a combined model based on EMD and Artificial Neural Network (ANN) trained with different SNRs (0, 5, 10, 15, and 20dB) is proposed in this research. The model can denoise white and pink noises in the range of -2 to 20dB without thresholding or even estimating SNR, and at the same time, keep the main content of the LS signal well. The proposed method is also compared with the EMD-custom method, and the results obtained from the SNR, and fit criteria indicate the absolute superiority of the proposed method. For example, at SNR = 0dB, the combined method can improve the SNR by 9.41 and 8.23dB for white and pink noises, respectively, while the corresponding values are respectively 5.89 and 4.31dB for the EMD-Custom method

Automated analysis of sleep study parameters using signal processing and artificial intelligence.

An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG signals. In this research work, an empirical mode decomposition is used in combination with stacked autoencoders to conduct automatic sleep stage classification with reliable analytical performance. Due to the decomposition of the composite signal into several intrinsic mode functions, empirical mode decomposition offers an effective solution for denoising non-stationary signals such as EEG. Preliminary results showed that through these intrinsic modes, a signal with a high signal-to-noise ratio can be obtained, which can be used for further analysis with confidence. Therefore, later, when statistical features were extracted from the denoised signals and were classified using stacked autoencoders, improved results were obtained for Stage 1, Stage 2, Stage 3, Stage 4, and REM stage EEG signals using this combination

Адаптивное фильтрование дрейфа базовой линии нестационарных и нелинейных сигналов на основе метода эмпирического разложения

В статье рассматривается возможность применения эмпирической модовой декомпозиции (Empirical Mode Decomposition, EMD) для устранения дрейфа базовой линии на примере биомедицинских сигналов – измеряемых в клинике сигналов внутричерепного давления (ВЧД) и электрокардиограммы (ЭКГ). Для устранения нестационарной помехи из нестационарных и нелинейных сигналов используется адаптивное фильтрование на основе градиентного LMS-алгоритма Уидроу-Хоффа (Widrow-Hoff), в котором неизвест- ный опорный сигнал (вход в адаптивный фильтр) предлагается формировать с помощью внутренних модовых функций (IMF) эмпирического разложения исследуемого сигнала. Предлагаемая схема фильтрования, по сравнению с широко используемыми методами двухшаговой скользяще средней фильтрации, фильтром нижних частот нулевой фазы первого порядка и медианным фильтром, показала эффективное удаление дрейфа базовых линий ВЧД и ЭКГ сигналов без искажения их формы линий.У статті розглядається можливість застосування емпіричної модової декомпозиції (Empirical Mode Decomposition, EMD) для усунення дрейфу базової лінії на прикладі біомедичних сигналів – вимірюваних у клініці сигналів внутрішньочерепного тиску (ВЧТ) і електрокардіограми (ЕКГ). Для усунення нестаціонарної завади з нестаціонарних і нелінійних сигналів використовується адаптивне фільтрування на основі градієнтного LMS-алгоритму Уїдроу-Хоффа (Widrow-Hoff), у якому невідомий опорний сигнал (вхід в адаптивний фільтр) пропонується формувати за допомогою внутрішніх модових функцій (IMF) емпіричного розкладання досліджуваного сигналу. Запропонована схема фільтрування, у порівнянні з широко використовуваними методами двокрокової ковзне середньої фільтрації, фільтром нижніх частот нульової фази першого порядку і медіанним фільтром, показала ефективне усунення дрейфу базових ліній ВЧТ і ЕКГ сигналів без спотворення їх форми ліній.The goal of that work is check of the effectiveness of the presented EMD-method and the Widrow-Hoff gradient LMS-method for the baseline wander removal at ICP and electrocardiogram (ECG) signals, and comparison of the suggested method with statistically direct algorithms. The removal of such interference is a very important step in the preprocessing stage of essential medical signals for getting desired signal for clinical diagnoses. At this article a new method signal filtering was presented, in which the reconstruction of the reference signal is conditioned by lower frequency IMFs. This method does not use any preprocessing and post processing, and does not require prior estimates. The proposed filtering scheme, as compared to the widely used of a two-stage moving-average filter, lowpass-IIR and median filters, showed the effective baseline wander removal of ICP and EKG of signals without distortion of their waveform signals

Real-time Ultrasound Signals Processing: Denoising and Super-resolution

Ultrasound acquisition is widespread in the biomedical field, due to its properties of low cost, portability, and non-invasiveness for the patient. The processing and analysis of US signals, such as images, 2D videos, and volumetric images, allows the physician to monitor the evolution of the patient's disease, and support diagnosis, and treatments (e.g., surgery). US images are affected by speckle noise, generated by the overlap of US waves. Furthermore, low-resolution images are acquired when a high acquisition frequency is applied to accurately characterise the behaviour of anatomical features that quickly change over time. Denoising and super-resolution of US signals are relevant to improve the visual evaluation of the physician and the performance and accuracy of processing methods, such as segmentation and classification. The main requirements for the processing and analysis of US signals are real-time execution, preservation of anatomical features, and reduction of artefacts. In this context, we present a novel framework for the real-time denoising of US 2D images based on deep learning and high-performance computing, which reduces noise while preserving anatomical features in real-time execution. We extend our framework to the denoise of arbitrary US signals, such as 2D videos and 3D images, and we apply denoising algorithms that account for spatio-temporal signal properties into an image-to-image deep learning model. As a building block of this framework, we propose a novel denoising method belonging to the class of low-rank approximations, which learns and predicts the optimal thresholds of the Singular Value Decomposition. While previous denoise work compromises the computational cost and effectiveness of the method, the proposed framework achieves the results of the best denoising algorithms in terms of noise removal, anatomical feature preservation, and geometric and texture properties conservation, in a real-time execution that respects industrial constraints. The framework reduces the artefacts (e.g., blurring) and preserves the spatio-temporal consistency among frames/slices; also, it is general to the denoising algorithm, anatomical district, and noise intensity. Then, we introduce a novel framework for the real-time reconstruction of the non-acquired scan lines through an interpolating method; a deep learning model improves the results of the interpolation to match the target image (i.e., the high-resolution image). We improve the accuracy of the prediction of the reconstructed lines through the design of the network architecture and the loss function. %The design of the deep learning architecture and the loss function allow the network to improve the accuracy of the prediction of the reconstructed lines. In the context of signal approximation, we introduce our kernel-based sampling method for the reconstruction of 2D and 3D signals defined on regular and irregular grids, with an application to US 2D and 3D images. Our method improves previous work in terms of sampling quality, approximation accuracy, and geometry reconstruction with a slightly higher computational cost. For both denoising and super-resolution, we evaluate the compliance with the real-time requirement of US applications in the medical domain and provide a quantitative evaluation of denoising and super-resolution methods on US and synthetic images. Finally, we discuss the role of denoising and super-resolution as pre-processing steps for segmentation and predictive analysis of breast pathologies

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

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

Applied Mathematics to Mechanisms and Machines

This book brings together all 16 articles published in the Special Issue "Applied Mathematics to Mechanisms and Machines" of the MDPI Mathematics journal, in the section “Engineering Mathematics”. The subject matter covered by these works is varied, but they all have mechanisms as the object of study and mathematics as the basis of the methodology used. In fact, the synthesis, design and optimization of mechanisms, robotics, automotives, maintenance 4.0, machine vibrations, control, biomechanics and medical devices are among the topics covered in this book. This volume may be of interest to all who work in the field of mechanism and machine science and we hope that it will contribute to the development of both mechanical engineering and applied mathematics

Development, Optimization and Clinical Evaluation Of Algorithms For Ultrasound Data Analysis Used In Selected Medical Applications.

The assessment of soft and hard tissues is critical when selecting appropriate protocols for restorative and regenerative therapy in the field of dental surgery. The chosen treatment methodology will have significant ramifications on healing time, success rate and overall long-time oral health. Currently used diagnostic methods are limited to visual and invasive assessments; they are often user-dependent, inaccurate and result in misinterpretation. As such, the clinical need has been identified for objective tissue characterization, and the proposed novel ultrasound-based approach was designed to address the identified need. The device prototype consists of a miniaturized probe with a specifically designed ultrasonic transducer, electronics responsible for signal generation and acquisition, as well as an optimized signal processing algorithm required for data analysis. An algorithm where signals are being processed and features extracted in real-time has been implemented and studied. An in-depth algorithm performance study has been presented on synthetic signals. Further, in-vitro laboratory experiments were performed using the developed device with the algorithm implemented in software on animal-based samples. Results validated the capabilities of the new system to reproduce gingival assessment rapidly and effectively. The developed device has met clinical usability requirements for effectiveness and performance