Biomedical signal processing for health monitoring applications: a review

Sonawani, Shilpa; Patil, Kailas; Natarajan, Prabhu Manickam

doi:10.1504/ijass.2023.129065

Cited by 6 publications

(3 citation statements)

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“…The analysis of medical signals like electrocardiograms ECGs, EEGs, and even molecular data from gene expression studies is now possible using methods like signal processing, time-series analysis, and spectral analysis, which are frequently used in geophysics to analyze seismic, electromagnetic, and other data. These techniques assist in the detection of cardiac arrhythmias, neurological illnesses, and genetic diseases by locating patterns, abnormalities, and trends within complex medical data [ 104 , 105 ].…”

Section: Geophysical Data Analysis In the Health Sciencesmentioning

confidence: 99%

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

Singh,

Nayak,

Behl

et al. 2024

Diagnostics

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To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth’s subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.

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Section: Geophysical Data Analysis In the Health Sciencesmentioning

confidence: 99%

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

Singh,

Nayak,

Behl

et al. 2024

Diagnostics

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“…In the biomedical field, the topic of signal processing in ML is an active area of research. For instance, the work in [7] provides an end-to-end review of biomedical signal processing for health monitoring applications. It introduces a flow for developing biomedical signal processing systems.…”

Section: Introductionmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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“…The processing of biomedical digital data and the evaluation of data records in hospitals are important for designing decision support systems for physicians. Recently, there have been many studies on digital data processing and classification of patient data records (Nsugbe, 2023; Sonawani et al, 2023). In studies on digital data processing, numerical data (blood count, CRP level, etc.)…”

Section: Introductionmentioning

confidence: 99%

A new computer‐aided diagnostic method for classifying anaemia disease: Hybrid use of Tree Bagger and metaheuristics

Yagmur,

Dag,

Temurtas

2023

Expert Systems

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Anaemia occurs when the haemoglobin (Hgb) value falls below a certain reference range. It requires many blood tests, radiological images, and tests for diagnosis and treatment. By processing medical data from patients with artificial intelligence and machine learning methods, disease predictions can be made for newly ill individuals and decision‐support mechanisms can be created for physicians with these predictions. Thanks to these methods, which are very important in reducing the margin of error in the diagnoses made by doctors, the evaluation of data records in health institutions is also important for patients and hospitals. In this study, six hybrid models are proposed to classify non‐anaemia records, Hgb‐anaemia, folate deficiency anaemia (FDA), iron deficiency anaemia (IDA), and B12 deficiency anaemia by combining artificial intelligence and machine learning methods TreeBagger, Crow Search Algorithm (CSA), Chicken Swarm Optimization Algorithm (CSO) and JAYA methods. The proposed hybrid models are analysed with two different approaches, with/without applying the SMOTE technique to achieve high performance by better emphasizing the importance of parameters. To solve the multiclass anaemia classification problem, fuzzy logic‐based parameter optimization is applied to improve the class‐based accuracy as well as the overall accuracy in the dataset. The proposed methods are evaluated using ROC criteria to build a prediction model to determine the anaemia type of anaemic patients. As a result of the study on the dataset taken from the Kaggle database, it is observed that the six proposed hybrid methods outperformed other studies using the same dataset and similar studies in the literature.

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Biomedical signal processing for health monitoring applications: a review

Cited by 6 publications

References 0 publications

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

A new computer‐aided diagnostic method for classifying anaemia disease: Hybrid use of Tree Bagger and metaheuristics

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