Biosignal Processing

Escabı́, Monty A.

doi:10.1016/b978-0-12-238662-6.50012-4

Cited by 11 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is a sequence of ordered numbers that depicted trends and changes in quantity [1]. In another word, it is a space and time report of an event in a biological system such as a heart beating, body temperature, and muscle contraction [14]. Several activities occur in the biological body system in the form of electrical, chemical, and mechanical event, which produces signals that can be analyzed and measured.…”

Section: Concept Of Biomedical Signals and Image Processingmentioning

confidence: 99%

Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

Isa¹,

Iliyas²,

Zarma³

2024

Biomedical Engineering

View full text Add to dashboard Cite

Recent advances in the cutting-edge technologies of biomedical sensing and image processing tools provide us with big data of biomedical and various types of images that can’t be processed within a finite period by professional clinicians. Various techniques for processing biomedical images comprise mathematical algorithms that extract vital diagnostic features from biomedical information and biological data. Because of the complexity and big size of the data computation, intelligence techniques have been applied in processing, visualizing, diagnostic, and classification tasks. This study will explore the effectiveness of the variously artificial intelligence approaches on biomedical signal and image processing applications. The researchers and community entirely will benefit from this study as a guide to the state-of-the-art artificial intelligence techniques for biomedical signal and image processing applications.

show abstract

Section: Concept Of Biomedical Signals and Image Processingmentioning

confidence: 99%

Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

Isa¹,

Iliyas²,

Zarma³

2024

Biomedical Engineering

View full text Add to dashboard Cite

show abstract

“…We conducted a literature search on Google Scholar, PubMed, and IEEE Xplore using combinations of the terms "Continual Learning", "Lifelong Learning", "Incremental Learning", "ECG", "EEG", "EMG", and "Physiological Signal". The selection criteria for articles found through this search strategy were as follows: (1) The study utilizes at least one physiological signal; (2) The study encompasses one of the primary scenarios, which include task-incremental learning, class-incremental learning, or domain-incremental learning; (3) The study employs a deep neural network; (4) The study employs one or more continual-learning approaches, including replay-based approaches, regularization-based approaches, and architecture-based approaches; (5) The study is focused on healthcare; (6) The study is peer-reviewed and published between January 2021 and September 2023. Based on these criteria, we identified eight articles focusing on the application of continuallearning methods to physiological signals.…”

Section: Reviewsmentioning

confidence: 99%

“…Physiological signals reflect the biological activities, which can be measured within the human body [1]. These include electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG), blood pressure signal, respiratory signal, and oxygen saturation (SpO 2 ).…”

Section: Introductionmentioning

confidence: 99%

Continual Learning with Deep Neural Networks in Physiological Signal Data: A Survey

Li,

Yuan

2024

Healthcare

View full text Add to dashboard Cite

Deep-learning algorithms hold promise in processing physiological signal data, including electrocardiograms (ECGs) and electroencephalograms (EEGs). However, healthcare often requires long-term monitoring, posing a challenge to traditional deep-learning models. These models are generally trained once and then deployed, which limits their ability to adapt to the dynamic and evolving nature of healthcare scenarios. Continual learning—known for its adaptive learning capabilities over time—offers a promising solution to these challenges. However, there remains an absence of consolidated literature, which reviews the techniques, applications, and challenges of continual learning specific to physiological signal analysis, as well as its future directions. Bridging this gap, our review seeks to provide an overview of the prevailing techniques and their implications for smart healthcare. We delineate the evolution from traditional approaches to the paradigms of continual learning. We aim to offer insights into the challenges faced and outline potential paths forward. Our discussion emphasizes the need for benchmarks, adaptability, computational efficiency, and user-centric design in the development of future healthcare systems.

show abstract

“…In real life, a lot of signals are non-stationary, meaning that they change with time. In a number of disciplines, including audio, image processing, and biomedical engineering [2], non-stationary signals are frequently encountered. With the help of the Fourier transform, we can effectively describe a signal in the frequency domain [3] and extract valuable data about it, including its spectral properties [4] and frequency components.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Cabrel,

Mumanikidzwa,

Shen

et al. 2024

JST

View full text Add to dashboard Cite

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of nonstationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

show abstract

Biosignal Processing

Cited by 11 publications

References 2 publications

Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

Continual Learning with Deep Neural Networks in Physiological Signal Data: A Survey

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Contact Info

Product

Resources

About