Biomedical Signal Processing - Science topic

Feature selection is a preprocessing technique that identifies the salient features of a given scenario. It has been used in the past to a wide range of problems, including intrusion detection systems, financial problems, and the analysis of biological data. Feature selection has been especially useful in medical applications, where it may help ide...

Continuous gravitational wave searches with terrestrial, long-baseline interferometers are hampered by long-lived, narrowband features in the power spectral density of the detector noise, known as lines. Candidate GW signals which overlap spectrally with known lines are typically vetoed. Here we demonstrate a line subtraction method based on adapti...

System identification involves constructing mathematical models of dynamic systems using input-output data, enabling analysis and prediction of system behaviour in both time and frequency domains. This approach can model the entire system or capture specific dynamics within it. For meaningful analysis, it is essential for the model to accurately re...

Cardiopulmonary resuscitation (CPR) is a critical, life-saving intervention aimed at restoring blood circulation and breathing in individuals experiencing cardiac arrest or respiratory failure. Accurate and real-time analysis of biomedical signals during CPR is essential for monitoring and decision-making, from the pre-hospital stage to the intensi...

The electrocardiogram (ECG) is one of the most significant methods of diagnostics for determining heart rhythm disorders. For this study, raw ECG signals from the Physio Bank database are subjected to an important preprocessing step that uses empirical mode decomposition (EMD) on signal denoising and distortion elimination. Establishing functioning...

Children with type 1 diabetes (T1D) frequently have nocturnal hypoglycemia, daytime physical activity being the most important risk factor. The risk for late post-exercise hypoglycemia depends on various factors and is difficult to anticipate. The availability of continuous glucose monitoring (CGM) enabled the development of various machine learnin...

Review History Report for Prof. Adel El-Fishawy from Applied Soft Computing Journal, Artificial Intelligence in Medicine Journal, Biomedical Signal Processing and Control, Computers in Biology, Heliyon Journal, Journal of Radiation Research for Reviewing Research Articles for Their International Journal.jpg

Introduction Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limitations impact both experimental neurobiology research and next-generation neuroprosthetics. Methods We present a novel solution that leverages the high inte...

Review History Report for Prof. Adel El-Fishawy from 7 Journals: Applied Soft Computing Journal, Artificial Intelligence in Medicine Journal, Biomedical Signal Processing and Control, Computers in Biology, Heliyon Journal, Applied Soft Computing Journal, Journal of Radiation Research and Applied Sciences and Optik Journal for Reviewing their Intern...

Certificate for the more cited article in Numerical Methods for Biomedical Signal Processing, for Article "Deep-Learning Based for Seizure Prediction and Detection from Electroencephalogram Signals".

Reviewing Certificate from Biomedical Signal Processing and Control Journal for Prof. Adel Shaker El-Fishawy

Reviewing Certificate from Biomedical Signal Processing and Control Journal for Prof Adel Shaker Elfishawy, 2024

Reviewing Certificate from Biomedical Signal Processing and Control Journal for Prof Adel Shaker Elfishawy, 2024.pdf

Biomedical signal processing algorithms offer a variety of opportunities to improve performance. In this study, a new wavelet delta- function (WDF) method was proposed to effectively detect pathology from ECG signals. In this method, the delta function is determined for each value of the ECG signal. The coefficients 0, 1, and -1 are determined by s...

Background and Objectives Accurate diagnosis of cardiovascular diseases often relies on the electrocardiogram (ECG). Since the cardiac vector is located within a three-dimensional space and the standard ECG comprises 12 projections or leads derived from it, redundant information is inherently present. This study aims to quantify this redundancy and...

Self-supervised learning has emerged as a promising paradigm for enhancing the analysis of physiological signals, particularly Electrocardiogram (ECG) and Photoplethysmogram (PPG) data. This review paper surveys the application of self-supervised learning techniques in the domain of ECG and PPG signal analysis. Traditional supervised methods often...

Acquiring labeled data for machine learning algorithms in healthcare is expensive due to the laborious expert annotation and privacy concerns. This challenge is further complicated in the case of Mechanocardiogram (MCG) data, which are characterized by high inter- and intrapersonal complexity, compounded further by sensor variability. In this paper...

Depression is a widespread mental disorder with inconsistent symptoms that make diagnosis challenging in clinical practice and research. Nevertheless, the poor identification may be partially explained by the fact that present approaches ignore patients' vocal tract modifications in favour of merely considering speech perception aspects. This study...

Due to the advancement in biomedical technologies, to diagnose problems in people, a number of psychological signals are extracted from patients. We should be able to ensure that psychological signals are not altered by adversaries and it should be possible to relate a patient to his/her corresponding psychological signal. As far as our awareness e...

(1) Objective: This study aims to lay a foundation for noncontact intensive care monitoring of premature babies. (2) Methods: Arterial oxygen saturation and heart rate were measured using a monochrome camera and time-division multiplex controlled lighting at three different wavelengths (660 nm, 810 nm and 940 nm) on a piglet model. (3) Results: Usi...

This paper introduces a fully differential asynchronous successive approximation register analog-to-digital converter (SAR ADC) designed for biomedical signal processing. By extending the tracking time and utilizing fully differential inputs in the analog front-end circuit, the signal-to-noise ratio is enhanced in the system. Using an asynchronous...

Integrating artificial intelligence (AI) and deep learning (DL) techniques into medical and assistive technology (AT) is revolutionizing the healthcare landscape, offering unprecedented precision and efficiency in diagnosing, monitoring, and treating various conditions. As the demand for personalized and accessible healthcare grows, these technolog...

Estimating intracranial current sources underlying the electromagnetic signals observed from extracranial sensors is a perennial challenge in non-invasive neuroimaging. Established solutions to this inverse problem treat time samples independently without considering the temporal dynamics of event-related brain processes. This paper describes curr...

Electroretinography (ERG) is a non-invasive method of assessing retinal function by recording the retina’s response to a brief flash of light. This study focused on optimizing the ERG waveform signal classification by utilizing Short-Time Fourier Transform (STFT) spectrogram preprocessing with a machine learning (ML) decision system. Several window...

This paper mainly discusses various technologies of wearable smart devices in biomedical signal processing, including hardware composition, biomedical signal types, signal processing technology, feature extraction, data analysis and result presentation. This paper introduces in detail the signal acquisition technology of PPG and EEG, as well as the...

Light-to-digital converters (LDCs) are essential components in photoplethysmography (PPG) readout chains. Over the past decade, PPG sensors have gathered increased interest due to their non-invasiveness and employment in a wide variety of applications. Among these are cardiovascular monitoring, brain mapping, glucose sensing, skin cancer detection,...

Deep learning time-series processing often relies on convolutional neural networks with overlapping windows. This overlap allows the network to produce an output faster than the window length. However, it introduces additional computations. This work explores the potential to optimize computational efficiency during inference by exploiting convolut...

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient’s health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology...

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate varia...

This bibliometric analysis explores the synergy of artificial intelligence (AI), particularly machine learning, and biomedical signal processing in predicting patient mortality risk within the intensive care unit (ICU). Utilizing a comprehensive literature review, the study assesses the research landscape by applying these techniques to ICU data. E...

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)–electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement ba...

Fast Fourier Transform (FFT) is an indispensable tool in biomedical engineering, which optimizes the computational process of the traditional Discrete Fourier Transform (DFT) and effectively reduces the computational complexity. This paper first introduces the basic principle of FFT and its importance in biomedical signal processing. It focuses on...

The field of biomedical signal processing has experienced significant advancements in recent years, particularly in the realm of emotion recognition [...]

The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding in the identification of various cardiac diseases. Commonly, the interpretation of ECGs necessitates specialized knowledge. However, this paper explores the application of machine learning algorithms and deep learning algorithm to autonomously identi...

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmog...

Microelectrode recordings from human peripheral and cranial nerves provide a means to study both afferent and efferent axonal signals at different levels of detail, from multi- to single-unit activity. Their analysis can lead to advancements both in diagnostic and in the understanding of the genesis of neural disorders. However, most of the existin...

In this paper, a new non-uniform differential sample and hold circuit is proposed using low-distortion sampling switches for biomedical signal processing applications. The proposed design can be used in the biomedical low-frequency range with low-power consumption which makes the proposed design a good candidate for bio-signal sampling purposes. Th...

Biomedical signal processing has advanced to the point that tools and methods are now available to doctors to diagnose and track medical conditions connected to pregnancy. However, it is extremely difficult for researchers to look into novel procedures and approaches to uncover underlying pathological abnormalities associated with high-risk pregnan...

Objective This study was undertaken to develop and evaluate a machine learning‐based algorithm for the detection of focal to bilateral tonic–clonic seizures (FBTCS) using a novel multimodal connected shirt. Methods We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt...

Biomedical signal processing is a critical field in modern medicine, particularly for monitoring and diagnosing heart-related conditions using electrocardiogram (ECG) signals. However, ECG signals are often contaminated with noise, making accurate diagnosis challenging. This paper presents a novel complexity-efficient penta-diagonal quantum smoothi...

The purpose of this paper is to present a straightforward framework for Heart Rate (HR) estimation from a Phonocardiogram (PCG) records and study the impact of murmur severity on HR. The system focuses primarily on data processing procedure, which is based on signal preprocessing using Maximal Overlap Discrete Wavelet Transform (MODWT) to delineate...

Epilepsy is a neurological disorder characterized by abnormal neuronal discharges that manifest in life-threatening seizures. These are often monitored via EEG signals, a key aspect of biomedical signal processing (BSP). Accurate epileptic seizure (ES) detection significantly depends on the precise identification of key EEG features, which requires...

This study uses fixed point theory and the Banach contraction principle to prove the existence, uniqueness, and stability of solutions to boundary value problems 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...

Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for p...

Biomedical signal processing plays a vital role in analyzing and interpreting signals obtained from various physiological processes. With the advent of machine learning techniques, researchers have explored their potential to revolutionize biomedical signal processing by enabling more accurate and efficient analysis. In this comprehensive review, w...

This study rigorously investigates the synergistic integration of Asynchronous FIFO (First-In, First-Out) with FIR (Finite Impulse Response) filters, proposing an advanced framework for digital signal processing. It commences by elucidating the cardinal significance of Asynchronous FIFO and FIR filters within the modern landscape of digital signal...

The ADReSS-M Signal Processing Grand Challenge was held at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023. The challenge targeted difficult automatic prediction problems of great societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD) and the estimation of cognitive test sco...

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Goal: Poor arousal management may lead to reduced cognitive performance. Specifying a model and decoder to infer the cognitive arousal and performance contributes to arousal regulation via non-invasive actuators such as music. Methods: We employ a Ba...

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 Programmi...

Algorithms for QRS detection are fundamental in the ECG interpretive processing chain. They must meet several challenges, such as high reliability, high temporal accuracy, high immunity to noise, and low computational complexity. Unfortunately, the accuracy expressed by missed or redundant events statistics is often the only parameter used to evalu...

The sub 100 µV voltage levels and sub 100 Hz frequency range makes the processing of most popular signal electroencephalograph (EEG) for brain functionality analysis, a complex task. The low frequency content of EEG (useful signals below 70 Hz) is commonly used for diagnosis of various brain related disorders making low-pass filter (LPF) a key bloc...

Automated Emotion Recognition (AER) is the process of programmatically identifying and classifying affective responses to stimuli through the analysis of physiological signals. AER has applications in interpersonal communications via digital mediums, human-computer interactions, third-party monitoring and surveillance, personal health and wellness,...

El Telemonitoreo permite obtener información de rutina sobre el estado del paciente con fines de seguimiento y cuidado remoto. Las plataformas de telemonitoreo implementan mecanismos a través de sistemas de software que leen las alteraciones de los signos vitales, y permiten detectar descompensaciones en etapas incipientes para facilitar su tratami...

Published papers: 1. Systematic Review and Future Direction of Neuro-Tourism Research (https://doi.org/10.3390/brainsci13040682) 2. Emotion Recognition from Spatio-Temporal Representation of EEG Signals via 3D-CNN with Ensemble Learning Techniques (https://doi.org/10.3390/brainsci13040685) 3. Emotion Recognition Using a Novel Granger Causality Qu...

Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human-computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence...

Emotions significantly shape decision-making, and targeted emotional elicitations represent an important factor in neuromarketing, where they impact advertising effectiveness by capturing potential customers' attention intricately associated with emotional triggers. Analyzing biometric parameters after stimulus exposure may help in understanding em...

Biomedical Signal Processing and Control,Awarded for 4 reviews between December 2023 and January 2024 presented to MOHIT TIWARI in recognition of the review contributed to the journal

Signal quality assessment is essential for biomedical signal processing, analysis, and interpretation. Various methods exist, including averaged numerical values, thresholding, time- or frequency-domain analysis, and nonlinear approaches.The aim of this study was to evaluate the quality of electrocardiographic (ECG) signals, seismocardiographic sig...

Epilepsy is a neurological problem due to aberrant brain activity. Epilepsy diagnose through Electroencephalography (EEG) signal. Human interpretation and analysis of EEG signal for earlier detection of epilepsy is subjected to error. Detection of Epileptic seizures due to stress and anxiety is the major problem. Epileptic seizure signal size, and...

Cough is a common symptom associated with respiratory diseases and its analysis plays a crucial role in monitoring the health conditions of affected persons. Traditional cough detection approaches largely fail to identify single cough boundaries when continuous coughs are present, consequently limiting their suitability for effective cough monitori...

Heart rate is a crucial metric in health monitoring. Traditional computer vision solutions estimate cardiac signals by detecting physical manifestations of heartbeats, such as facial discoloration caused by blood oxygenation changes, from subject videos using regression methods. As continuous signals are more complex and expensive to de-noise, this...

Sleep-stage classification is a critical aspect of understanding sleep patterns in sleep research and healthcare. However, challenges arise when dealing with a limited number of labeled samples in the target domain. Traditional methods in Deep Learning (DL) and Domain Adaptation (DA) globally compare feature distributions, often overlooking intrica...

Increased physical activity can help reduce the occurrence of cardiovascular disease. However, cardiovascular disease during strenuous exercise also brings certain risks, so a convenient and effective method is needed to accurately identify heart rate. Due to the low amplitude characteristics of ECG signals, automatic classification of the impercep...

Emotion recognition based on electroencephalography (EEG) signals has garnered substantial attention in recent years and finds extensive applications in the domains of medicine and psychology. However, individual differences in EEG signals pose a challenge to accurate emotion recognition and limit the widespread adoption of such techniques. To addr...

The Internet of Medical Things (IoMT) has become a pivotal aspect of IoT applications, playing a crucial role in cutting down healthcare expenses, enhancing access to clinical services, and refining operational efficiency within the healthcare domain. An early detection of neurological brain disorders continues to present a formidable challenge. In...

In this manuscript, the problem of assessing the heart rate variability (HRV) of a single subject using a colocated multiple-input multiple-output radar of frequency modulated continuous wave type is investigated. The proposed solution exploits beamforming to acquire multiple measurements from different points on the body of the monitored subject....

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Goal: Electrodermal activity (EDA) shows a significant correlation with activation of the autonomic nervous system (ANS) activation. Regular ambulatory monitoring via wearables and consequent inference of ANS activation has a wide range of applications...

Biomedical Signal Processing takes into consideration the steps and the stages included in the preprocessing of physiological signals, recording the data, and examining the trends in the dataset. Such an aspect has been achieved with the aid of digital transformation of the working grounds in the healthcare industry. Through the inclusion of themat...

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Goal: Inferring autonomous nervous system (ANS) activity is a challenging issue and has critical applications in stress regulation. Sweat secretions caused by ANS activity influence the electrical conductance of the skin. Therefore, the variations in s...

Machine learning (ML) is a subset of artificial intelligence (AI) where computer is trained to make decisions like a human based on different characteristics from a set of data. Machine Learning is a subset of artificial intelligence (AI) which allows the software applications to predict the outcomes by considering the historical data as input. The...

Machine learning (ML) is a subset of artificial intelligence (AI) where computer is trained to make decisions like a human based on different characteristics from a set of data. Machine Learning is a subset of artificial intelligence (AI) which allows the software applications to predict the outcomes by considering the historical data as input. The...