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Applied Sciences
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Intelligent Biomedical Signals Processing: Extending and Enhancing Life
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Intelligent Biomedical Signals Processing: Extending and Enhancing Life

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2296

Special Issue Editors

Dr. Siti Anom Ahmad

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Interests: biomedical engineering; artificial intelligence; gerontechnology
Dr. Alpha Agape Gopalai

E-Mail Website
Guest Editor
School of Engineering, Monash University Malaysia, Subang Jaya, Malaysia
Interests: rehabilitation engineering; biomechanics; artificial intelligence

Special Issue Information

Dear Colleagues,

Biomedical signals consist of physiological measurements that are used mainly to extract relevant information such as heart rate, muscle activities, pulse and others. Electrocardiogram (ECG), electroencephalogram (EEG) and electromyogram (EMG) are the most commonly used biomedical signals in various applications such as for diagnosis, monitoring, prediction, human–computer interactions, among others. All these parameters find their way into applications that are either used to conserve, extend and even enhance life (and quality of life).

This Special Issue aims to provide a collection of latest contributions in the approaches and advancement of the biomedical signal processing field. Topics relevant to analysis of various biosignals and applications are welcome. The scope of the issue includes, but is not limited to, the following topics:

  • Biomedical Signal Analysis and Processing
  • Biomedical Imaging and Image Processing
  • Biosensors and Wearable Technology
  • Neural Engineering and Brain Computer Interface
  • Biomedical and Medical Robot

Dr. Siti Anom Ahmad
Dr. Alpha Agape Gopalai
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • biomedical signal processing
  • biomedical image processing
  • rehabilitation engineering
  • artificial intelligence in biomedical engineering

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Published Papers (2 papers)

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Research

21 pages, 712 KiB  
Article
Comparison of the Effectiveness of Various Classifiers for Breast Cancer Detection Using Data Mining Methods
by Noor Kamal Al-Qazzaz, Iyden Kamil Mohammed, Halah Kamal Al-Qazzaz, Sawal Hamid Bin Mohd Ali and Siti Anom Ahmad
Appl. Sci. 2023, 13(21), 12012; https://doi.org/10.3390/app132112012 - 3 Nov 2023
Cited by 1 | Viewed by 960
Abstract
Countless women and men worldwide have lost their lives to breast cancer (BC). Although researchers from around the world have proposed various diagnostic methods for detecting this disease, there is still room for improvement in the accuracy and efficiency with which they can [...] Read more.
Countless women and men worldwide have lost their lives to breast cancer (BC). Although researchers from around the world have proposed various diagnostic methods for detecting this disease, there is still room for improvement in the accuracy and efficiency with which they can be used. A novel approach has been proposed for the early detection of BC by applying data mining techniques to the levels of prolactin (P), testosterone (T), cortisol (C), and human chorionic gonadotropin (HCG) in the blood and saliva of 20 women with histologically confirmed BC, 20 benign subjects, and 20 age-matched control women. In the proposed method, blood and saliva were used to categorize the severity of the BC into normal, benign, and malignant cases. Ten statistical features were collected to identify the severity of the BC using three different classification schemes—a decision tree (DT), a support vector machine (SVM), and k-nearest neighbors (KNN) were evaluated. Moreover, dimensionality reduction techniques using factor analysis (FA) and t-stochastic neighbor embedding (t-SNE) have been computed to obtain the best hyperparameters. The model has been validated using the k-fold cross-validation method in the proposed approach. Metrics for gauging a model’s effectiveness were applied. Dimensionality reduction approaches for salivary biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 66.67% to 93.3% and 90%, respectively, by utilizing t-SNE and FA. Furthermore, dimensionality reduction strategies for blood biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 60% to 80% and 93.3%, respectively, by utilizing FA and t-SNE. These findings point to t-SNE as a potentially useful feature selection for aiding in the identification of patients with BC, as it consistently improves the discrimination of benign, malignant, and control healthy subjects, thereby promising to aid in the improvement of breast tumour early detection. Full article
(This article belongs to the Special Issue Intelligent Biomedical Signals Processing: Extending and Enhancing Life)
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19 pages, 10504 KiB  
Article
Improving Behavior Monitoring of Free-Moving Dairy Cows Using Noninvasive Wireless EEG Approach and Digital Signal Processing Techniques
by Ana Carolina de Sousa Silva, Aldo Ivan Céspedes Arce, Hubert Arteaga, Valeria Cristina Rodrigues Sarnighausen, Gustavo Voltani von Atzingen and Ernane José Xavier Costa
Appl. Sci. 2023, 13(19), 10722; https://doi.org/10.3390/app131910722 - 27 Sep 2023
Viewed by 1067
Abstract
Electroencephalography (EEG) is the most common method to access brain information. Techniques to monitor and extract brain signal characteristics in farm animals are not as developed as those for humans and laboratory animals. The objective of this study was to develop a noninvasive [...] Read more.
Electroencephalography (EEG) is the most common method to access brain information. Techniques to monitor and extract brain signal characteristics in farm animals are not as developed as those for humans and laboratory animals. The objective of this study was to develop a noninvasive method for monitoring brain signals in cattle, allowing the animals to move freely, and to characterize these signals. Brain signals from six Holstein heifers that could move freely in a paddock compartment were acquired. The control group consisted of the same number of bovines, contained in a climatic chamber (restrained group). In the second step, the signals were characterized by Power Spectral Density, Short-Time Fourier Transform, and Lempel–Ziv complexity. The preliminary results revealed an optimal electrode position, referred to as POS2, which is located at the center of the frontal region of the animal’s head. This positioning allowed for attaching the electrodes to the front of the bovine’s head, resulting in the acquisition of longer artifact-free signal sections. The signals showed typical EEG frequency bands, like the bands found in humans. The Lempel–Ziv complexity values indicated that the bovine brain signals contained random and chaotic components. As expected, the signals acquired from the retained bovine group displayed sections with a larger number of artifacts due to the hot 32 degree C temperature in the climatic chamber. We present a method that helps to monitor and extract brain signal features in unrestrained bovines. The method could be applied to investigate changes in brain electrical activity during animal farming, to monitor brain pathologies, and to other situations related to animal behavior. Full article
(This article belongs to the Special Issue Intelligent Biomedical Signals Processing: Extending and Enhancing Life)
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