Biomedical Signal Processing Research Articles

Investigating the existence, uniqueness, and stability of solutions in boundary value problem of fractional differential equations

Abstract This study uses fixed point theory and the Banach contraction principle to prove the existence, uniqueness, and stability of solutions to boundary value problem involving a Ψ-Caputo-type fractional differential equation. The conclusions are supported by illustrative cases, which raise the theoretical framework’s legitimacy. Fractional calculus is widely used in scientific fields, as seen by its applications in beam deflection analysis, groundwater pollution, and biomedical signal processing.

Advancing signal integrity and application analysis through data-flow mapping

Data-flow mapping is a crucial method in signal processing and optimization, managing data flow within systems. Its essential in signal compensation, particularly in telecommunications, audio processing, and biomedical signal processing. Four main algorithm categories underpin data-flow mapping: heuristics, meta-heuristics, Integer Linear Programming (ILP), and Constraint Satisfaction Problems (CSP). Heuristic and meta-heuristic methods like Genetic Algorithms (GA) and Ant Colony Optimization (ACO) provide approximate solutions, crucial for complex problems. ILP and Branch and Bound (B&B) methods offer precise solutions by exhaustive searches under constraints. CSP focuses on satisfying imposed conditions. These methodologies have practical applications, such as signal compensation in communication systems and improving medical imaging like MRI and ultrasound. Theyre also integrated with machine learning, quantum computing, and specialized hardware for 5G/6G communications and IoT. Real-time processing and noise reduction advancements enhance consumer audio and diverse sectors. In summary, data-flow mapping and its algorithms drive signal processing innovations across domains, with evolving technology integration ensuring their lasting importance.

Avoiding Post-processing with Event-Based Detection in Biomedical Signals.

Finding events of interest is a common task in biomedical signal processing. The detection of epileptic seizures and signal artefacts are two key examples. Epoch-based classification is the typical machine learning framework to detect such signal events because of the straightforward application of classical machine learning techniques. Usually, post-processing is required to achieve good performance and enforce temporal dependencies. Designing the right post-processing scheme to convert these classification outputs into events is a tedious, and labor-intensive element of this framework. We propose an event-based modeling framework that directly works with events as learning targets, stepping away from ad-hoc post-processing schemes to turn model outputs into events. We illustrate the practical power of this framework on simulated data and real-world data, comparing it to epoch-based modeling approaches. We show that event-based modeling (without tailored post-processing) performs on par with or better than epoch-based modeling with extensive post-processing. These results show the power of treating events as direct learning targets, instead of using ad-hoc post-processing to obtain them, severely reducing design effort. Significance The event-based modeling framework can easily be applied to other event detection problems in signal processing, removing the need for intensive task-specific post-processing.

A Reconfigurable 4th Order IIR Filter For The Low Frequency Applications

Huge numbers of advanced electrical engineering applications employ infinite impulse response (IIR) filters very frequently in order to meet market’s demands. Especially, some applications such as video processing, digital signal processing and high-speed digital communication necessitate computational efficiency and low latency. In this point of view, high-speed processing of digital data require a digital signal processor or an FPGA instead of a conventional microprocessor. In this respect, in the last decades, FPGAs are commonly used in order to bring new opportunities to the designers. This work gives a FPGA hardware design of a 4th order IIR reconfigurable filter structure. The proposed filter’s maximum clock frequency is around 32MHz which covers different low frequency applications from biomedical signal processing up to speech applications. To verify the performance of the filter, 4th order Butterworth and Chebyshev filters are realized in the basis of Matlab results and FPGA behavioral model. Also, consuming logic gates and blocks are given in order to evaluate chip area occupation. It should be considered that the proposed filter scheme presents promising results to meet low frequency applications.

A piecewise spline approach for modeling of ECG signals

This paper presents a new spline-based modeling method of electrocardiogram (ECG) signal that can reproduce normal as well as abnormal ECG beats. Large volume ECG data is required for automatic machine learning diagnostic systems, medical education, research and testing purposes but due to privacy issues, access to this medical data is very difficult. Given this, modeling an ECG signal is a very challenging task in the field of biomedical signal processing. Spline-based modeling is the latest and one of the most efficient methods with very low computational complexity in the domain of ECG signal generation. In this paper, healthy ECG and arrhythmia conditions have been considered for the synthetic generation, (namely Atrial fibrillation and Congestive heart failure ECG beats) because these are the leading causes of death globally. To validate the performance of the presented modeling method, it is tested on 100 signals, also the percentage root mean square difference (PRD) and the root mean square error (RMSE) have been determined. These calculated values are analyzed and the results are found to be very promising and show that the presented method is one of the best methods in the field of synthetic ECG signal generation. A comparison amongst relevant existing techniques and the proposed method is also presented. The performance merit values PRD and RMSE, for the proposed method obtained are 38.99 and 0.10092, respectively, which are lower than the values obtained in other compared methods. To ensure fidelity of the proposed modeling technique with respect to IEC60601 standard, few Conformance Testing Services (CTS)database signals have also been modelled with a very close resemblance with the standard signals.

Securing the Biometric through ECG using Machine Learning Techniques

In the current era, biometrics is widely used for maintaining the security. To extract the information from the biomedical signals, biomedical signal processing is needed. One of the significant tools used for the diagnostic is electrocardiogram (ECG). The main reason behind this is the certain uniqueness in the ECG signals of the individual.  In this paper, the focus will be on distinguishing the individual on the basis of ECG signals using feature extraction approaches and the machine learning algorithms. Other than preprocessing approach, the discrete cosine transform is applied to perform the extraction. The classification between the signals of the individuals is carried out using the Support Vector Machine and K-Nearest Neighbor machine learning techniques.  The classification accuracy achieved through SVM is 87% and K-NN has achieved a classification accuracy of 96.6% with k=3. The work has shown how machine learning can be used to classify the ECG signal.

Fully adaptive time-varying wave-shape model: Applications in biomedical signal processing

In this work, we propose a time-varying wave-shape extraction algorithm based on a modified version of the adaptive non-harmonic model for non-stationary signals. The model codifies the time-varying wave-shape information in the relative amplitude and phase of the harmonic components of the wave-shape. The algorithm was validated on both real and synthetic signals for the tasks of denoising, decomposition, and adaptive segmentation. For the denoising task, both monocomponent and multicomponent synthetic signals were considered. In both cases, the proposed algorithm can accurately recover the time-varying wave-shape of non-stationary signals, even in the presence of high levels of noise, outperforming existing wave-shape estimation algorithms and denoising methods based on short-time Fourier transform thresholding. The denoising of an electroencephalograph signal was also performed, giving similar results. For decomposition, our proposal was able to recover the composing waveforms more accurately by considering the time variations from the harmonic amplitude functions when compared to existing methods. Finally, the algorithm was used for the adaptive segmentation of synthetic signals and an electrocardiograph of a patient undergoing ventricular fibrillation.

ELM-based stroke classification using wavelet and empirical mode decomposition techniques

ABSTRACT Biomedical signal processing is crucial in many sectors that save lives. Artificial intelligence improvement in signal collection and conditioning boosted this application’s adaptability to varied bodily circumstances. In this study, a novel method is put forth for predicting the type of stroke in the human brain based on the observation of the Electroencephalography (EEG) signal. The signal is the first condition for removing undesirable frequencies by passing through a lowpass filter. To accurately extract the signal features, the signal is first transformed into a 1-second frame format and then normalised. Certain statistical and frequency domain aspects are highlighted to increase taxonomic accuracy. Under the wavelet packet transform, the empirical mode decomposition approach is utilised to recover the most information feasible from the signal. After training on extracted characteristics, the extreme learning machine is regarded to conduct classification. These work achieves 94.95 of Sensitivity, 84.95 of Specificity, 93.74 of Precision, 96.96 of Accuracy, 96.12 of F1 Score. Compared to the standard procedures, the proposed techniques have a greater accuracy rate of about 98%.

Preface

The International Conference on Signal Processing and Machine Learning (CONF-SPML) is an annual conference focusing on research areas including applied signal processing, biomedical signal processing, and machine learning. It aims to establish a broad and interdisciplinary platform for experts, researchers, and students worldwide to present, exchange, and discuss the latest advance and development in signal processing and machine learning.This volume contains the papers of the 3rd International Conference on Signal Processing and Machine Learning (CONF-SPML 2023). Each of these papers has gained a comprehensive review by the editorial team and professional reviewers. Each paper has been examined and evaluated for its theme, structure, method, content, language, and format.Cooperating with prestigious universities, CONF-SPML 2023 organized two workshops in China and the US. Dr. Marwan Omar chaired the workshop “Building a Digital Forensics Lab”, which was held at the Illinois Institute of Technology. Dr. Ziyi Yang chaired the workshop “Space-Air-Ground Integrated Wireless Communication” at the Beijing Institute of Technology.Besides these workshops, CONF-SPML 2023 also held an online session. Eminent professors from top universities worldwide were invited to deliver keynote speeches in this online session, including Prof. Roman Bauer from the University of Surrey, Prof. Brian A. Barsky from the University of California, Berkeley, Prof. Festus Adedoyin from Bournemouth University, Prof. Arshad Hassan Khan from the Department of Electrical Engineering NUCES-FAST, Islamabad, and Prof. Mazhar Javed Awan from University of Management & Technology. They have given keynote speeches on related topics of computational modeling, machine learning, electronic biosensor, etc.On behalf of the committee, we would like to give sincere gratitude to all authors and speakers who have made their contributions to CONF-SPML 2023, editors and reviewers who have guaranteed the quality of papers with their expertise, and the committee members who have devoted themselves to the success of CONF-SPML 2023.List of Committee Members are available in this pdf.

Current Trends in Feature Extraction and Classification Methodologies of Biomedical Signals.

Biomedical signal and image processing is the study of the dynamic behavior of various bio-signals, which benefits academics and research. Signal processing is used to assess the behavior of analogue and digital signals for the assessment, reconfiguration, improved efficiency, extraction of features, and reorganization of patterns. This paper unveils hidden characteristic information about input signals using feature extraction methods. The main feature extraction methods used in signal processing are based on studying time, frequency, and frequency domain. Feature exaction methods are used for data reduction, comparison, and reducing dimensions, producing the original signal with sufficient accuracy with a structure of an efficient and robust pattern for the classifier system. Therefore, an attempt has been made to study the various feature extraction methods, feature transformation methods, classifiers, and datasets for biomedical signals.

A novel method for modeling effective connections between brain regions based on EEG signals and graph neural networks for motor imagery detection

Classified as biomedical signal processing, cerebral signal processing plays a key role in human-computer interaction (HCI) and medical diagnosis. The motor imagery (MI) problem is an important research area in this field. Accurate solutions to this problem will greatly affect real-world applications. Most of the proposed methods are based on raw signal processing techniques. Known as prior knowledge, the structural-functional information and interregional connections can improve signal processing accuracy. It is possible to correctly perceive the generated signals by considering the brain structure (i.e. anatomical units), the source of signals, and the structural-functional dependence of different brain regions (i.e. effective connection) that are the semantic generators of signals. This study employed electroencephalograph (EEG) signals based on the activity of brain regions (cortex) and effective connections between brain regions based on dynamic causal modeling to solve the MI problem. EEG signals, as well as effective connections between brain regions to improve the interpretability of MI action, were fed into the architecture of Graph Convolutional Neural Network (GCN). The proposed model allowed GCN to extract more discriminative features. The results indicated that the proposed method was successful in developing a model with a MI detection accuracy of 93.73%.

Improving accuracy of medical data handling and processing using DCAF for IoT-based healthcare scenarios

The Internet of Things (IoT), uses communication technologies and intelligent computing to improve data analysis and computation accuracy, which is essential to the healthcare industry. The sensitivity and requirement for precision in managing and processing medical data make it a difficult task for diagnosing patients. To handle medical data in a healthcare scenario based on the IoT, a Data Classification and Analysis Framework (DCAF) is proposed in this research. The proposed framework seeks to improve the precision and dependability of handling and processing medical data, which are essential components of biomedical signal processing. By reducing errors and accelerating the processing of medical data, the framework can help in improving the accuracy of medical diagnosis and therapy, which is a key objective of biomedical signal processing and control. The goal of this framework is to increase the reliability of message delivery by concentrating on data classification and analysis. The earlier procedure is in charge of decreasing the volume of medical data transfers and ensuring the accuracy and delivery of consent data. The framework's latter process is in charge of allotting suitable processing time slots to increase delivery speed and lower data handling faults. A linear decision-making process based on accessibility, sequence order, and accuracy constraints and conditions supports the framework's joint process. Utilizing measures such as an accuracy ratio 0f 93.09 %, message delivery rate of 0.949 %, error with a range of 0.052 %, classification of 73.81 ms, and analysis time over fluctuating flow instances and analysis slots, the suggested framework's performance is validated.

Integrating Machine Learning with Biomedical Signal Processing and Systems Analysis: An Applications-based Course.

The growing importance of data analytics in biomedicine is increasingly becoming recognized in biomedical engineering curricula through the introduction of machine learning classes that generally run in parallel to, but separately from, more traditional engineering courses, such as signal and systems analysis. We propose a new approach that systematically integrates signal processing and systems analysis with key techniques in machine learning. In the proposed course, the student obtains hands-on experience in applying algorithms that can be applied to practical problems of physiological signal conditioning, analysis and interpretation. This is achieved by exposing the student to a sequence of 4 applications-based modules that represent different biomedical engineering problems: human activity recognition from wearable devices, epileptic seizure detection, quantification of dynamic respiratory-cardiac coupling in humans under different conditions, and detection of sleep apnea episodes from heart rate variability data. Within each module, the student gains the experience of working with the data in question "from the ground up". We also introduce a general plan for assessment of student learning, and discuss the expected outcomes and limitations of this integrative approach to teaching.Clinical Relevance- The proposed course is targeted at biomedical engineering students at the senior undergraduate or first-year graduate level who are interested in learning how to analyze physiological signals. The course would also be suitable for clinician-scientists who have prior training in statistics with some exposure to engineering mathematics.

Symmetric Projection Attractor Reconstruction Analysis as a Method to Assess Seismocardiogram Quality in a Healthy Population.

Signal quality significantly affects the processing, analysis, and interpretation of biomedical signals. There are many procedures for assessing signal quality that use averaged numerical values, thresholding, analysis in the time or frequency domain, or nonlinear approaches. An interesting approach to the assessment of signal quality is using symmetric projection attractor reconstruction (SPAR) analysis, which transforms an entire signal into a two-dimensional plot that reflects the waveform morphology. In this study, we present an application of SPAR to evaluate the quality of seismocardiograms (SCG signals) from the CEBS database, a publicly available seismocardiogram signal database. Visual inspection of symmetric projection attractors suggests that high-quality (clean) seismocardiogram projections resemble six-pointed asterisks (*), and any deviation from this shape suggests the influence of noise and artifacts.Clinical relevance- SPAR analysis enables quick identification of noise and artifacts that can affect the reliability of the diagnosis of cardiovascular diseases based on SCG signals.

Multiscale Diffusion Entropy Analysis for the Detection of Crucial Events in Cardiac Pathology.

The significance of crucial events in explaining the dynamics of a physiological system has only been recently emerging. Crucial events are yet to be fully understood and implemented in clinical applications of physiological signal processing. This paper proposes the application of modified diffusion entropy (MDEA) and novel multiscale diffusion entropy analyses (MSDEA) on measuring the temporal complexity of the ECG time series to improve crucial events detection performance. Thirty samples of each of three groups of ECG datasets from PhysioNet with recordings of cardiac arrhythmia (ARR), congestive heart failure (CHF) and normal sinus rhythm (NSR) were analyzed using MDEA with stripes followed by MSDEA. Healthy NSR ECGs showed an approximate 15% greater inverse power law (IPL) and scaling δ indices than pathologic CHF and ARR signals. Additionally, the scaling indices for the pathologic groups showed higher standard deviations, indicating that crucial events determined by MDEA reveal latent differences in ECG complexity that could better be investigated across multiple time scales of temporally decomposed signals using MSDEA which combines multiscale entropy (MSE) and MDEA. Hence, MSDEA showed an improved, clearer discrimination between the healthy and pathological cardiac signals (p<0.0005) characterized by a range of NSR complexity indices twice the range of the pathological values associated with ARR and CHF across twenty temporal scales as well as more reliable trend lines (R2>=0.95).Clinical Relevance- This research proposes a novel and enhanced diagnostic discrimination across healthy and pathologic cardiac conditions based on biomedical signal processing of ECG recordings utilizing the principle of crucial events detection.

Biomedical signal processing methods for neuromarketing: A comparative study

Neuromarketing involves the integration of neuropsychology into marketing research, focusing on analyzing consumer sensory-motor actions, including cognitive and emotional responses to marketing stimuli, using advanced technologies. It represents one of the latest strategies in marketing research and has the potential to shape the future of the field. Numerous studies have already been conducted in this domain to enhance research outcomes. Nevertheless, the literature indicates that there are still opportunities for further advancements and improvements. A literature review was conducted in this article to explore the availability of an openly accessible dataset widely utilized by researchers in the field of neuromarketing. We examined the signal preprocessing, feature selection, feature extraction, and classification methods employed in studies utilizing the dataset created by Yadava et al.

HISET: Hybrid interpretable strategies with ensemble techniques for respiratory sound classification

The human respiratory systems can be affected by several diseases and it is associated with distinctive sounds. For advanced biomedical signal processing, one of the most complex issues is automated respiratory sound classification. In this research, five Hybrid Interpretable Strategies with Ensemble Techniques (HISET) which are quite interesting and robust are proposed for the purpose of respiratory sounds classification. The first approach is termed as an Ensemble GSSR technique which utilizes L2 Granger Analysis and the proposed Supportive Ensemble Empirical Mode Decomposition (SEEMD) technique and then Support Vector Machine based Recursive Feature Elimination (SVM-RFE) is used for feature selection and followed by classification with Machine Learning (ML) classifiers. The second approach proposed is the implementation of a novel Realm Revamping Sparse Representation Classification (RR-SRC) technique and third approach proposed is a Distance Metric dependent Variational Mode Decomposition (DM-VMD) with Extreme Learning Machine (ELM) classification process. The fourth approach proposed is with the usage of Harris Hawks Optimization (HHO) with a Scaling Factor based Pliable Differential Evolution (SFPDE) algorithm termed as HHO-SFPDE and it is classified with ML classifiers. The fifth or the final approach proposed analyzes the application of dimensionality reduction techniques with the proposed Gray Wolf Optimization based Support Vector Classification (GWO-SVC) and another parallel approach utilizes a similar kind of analysis with the Grasshopper Optimization Algorithm (GOA) based Sparse Autoencoder. The results are examined for ICBHI dataset and the best results are shown for the 2-class classification when the analysis is carried out with Manhattan distance-based VMD-ELM reporting an accuracy of 95.39%, and for 3-class classification Euclidean distance-based VMD-ELM reported an accuracy of 90.61% and for 4-class classification, Manhattan distance-based VMD-ELM reported an accuracy of 89.27%.

Improved Direction of Arrival Estimation using Multiple Signal Classification (MUSIC) Algorithm with Decomposition and Normalization

It is important to determine the direction of arrival (DoA) of targets in various applications such as radar and sonar. Multiple Signal Classification (MUSIC), Estimation of Signal Parameters with Rotational In variance Technique (ESPRIT), and Weighted Subspace Fitting (WSF) are subspace-based methods that can be used to improve DoA estimation. MUSIC is effective for high-resolution, uncorrelated signals, but may struggle in cases where there are two nearby targets with a low signal-to-noise ratio (SNR). The goal of this research is to improve the performance of the MUSIC algorithm for DoA estimation with low SNR signals. The proposed solution involves decomposing and normalizing the signal during transmission. Simulations were conducted to test the modified procedure with MUSIC algorithm for DoA estimation, and it was found that received signal power improved though there is noisy environment as well as system can detect more number of targets. The proposed technique of decomposition and normalization could also be applied in other areas such as WiFi communication, autonomous vehicles and biomedical signal and image processing etc.

Reshaping engineered clinical decision support systems: Biomedical signal processing and artificial intelligence in health care

Reshaping engineered clinical decision support systems: Biomedical signal processing and artificial intelligence in health care

Special Issue: “Research on Biomedical Signal Processing”

Over recent years, the number of signals of a different type that can be acquired from the human body has increased extraordinarily [...]