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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 41064

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Special Issue Editor

Prof. Dr. Shing-Tai Pan

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Guest Editor

Department of Computer Sciences and Information Engineering, National University of Kaohsiung, Kaohsiung 81148, Taiwan
Interests: biomedical signal processing; speech recognition; intelligent computation; digital signal processing; control systems

Special Issue Information

Dear Colleagues,

Due to the significant progress in hardware and software development of information technologies in recent years, artificial intelligent (AI) technologies become more powerful and feasible than before. It is then promising to explore the topic of AI methods implemented for practical applications in biomedical signal sensing and processing. This Special Issue aims to bring together researchers in this area to break down barriers and develop innovative realizable AI biomedical signal processing systems. The developed systems can then predict the health conditions for users more precisely in the applications of health motoring. This Special Issue is hence focusing on emerging AI technologies for biomedical signal processing applications, including measurement and analysis of biomedical signals and images in clinical medicine. Original research and review articles are both welcome.

Potential topics include, but are not limited to, the following:

AI on biomedical signal processing for sleep status detection;
AI on biomedical signal processing for emotion recognition;
Detection of arrhythmia based on biomedical signals by using AI technologies;
AI on biomedical signal processing for brain diseases;
Feature extraction and pattern recognition of biomedical signals;
Applications of biomedical signals on health monitoring by using non-invasion device;
AI on biomedical signal processing for communication disorders detection;
Biomedical signal-inspired non-invasion devices design for sensing and monitoring.

Prof. Dr. Shing-Tai Pan
Guest Editor

Manuscript Submission Information

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Keywords

artificial intelligence (AI)
biomedical signal processing
biomedical signal sensing
disease diagnosis
human health monitoring
non-invasion device

Published Papers (6 papers)

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Research

30 pages, 22382 KiB

Open AccessArticle

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

by Md. Faysal Ahamed, Md. Khalid Syfullah, Ovi Sarkar, Md. Tohidul Islam, Md. Nahiduzzaman, Md. Rabiul Islam, Amith Khandakar, Mohamed Arselene Ayari and Muhammad E. H. Chowdhury

Sensors 2023, 23(18), 7724; https://doi.org/10.3390/s23187724 - 07 Sep 2023

Cited by 3 | Viewed by 1781

Abstract

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development. Full article

(This article belongs to the Special Issue AI on Biomedical Signal Sensing and Processing for Health Monitoring)

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11 pages, 2179 KiB

Open AccessArticle

Children’s Pain Identification Based on Skin Potential Signal

by Yubo Li, Jiadong He, Cangcang Fu, Ke Jiang, Junjie Cao, Bing Wei, Xiaozhi Wang, Jikui Luo, Weize Xu and Jihua Zhu

Sensors 2023, 23(15), 6815; https://doi.org/10.3390/s23156815 - 31 Jul 2023

Viewed by 808

Abstract

Pain management is a crucial concern in medicine, particularly in the case of children who may struggle to effectively communicate their pain. Despite the longstanding reliance on various assessment scales by medical professionals, these tools have shown limitations and subjectivity. In this paper, we present a pain assessment scheme based on skin potential signals, aiming to convert subjective pain into objective indicators for pain identification using machine learning methods. We have designed and implemented a portable non-invasive measurement device to measure skin potential signals and conducted experiments involving 623 subjects. From the experimental data, we selected 358 valid records, which were then divided into 218 silent samples and 262 pain samples. A total of 38 features were extracted from each sample, with seven features displaying superior performance in pain identification. Employing three classification algorithms, we found that the random forest algorithm achieved the highest accuracy, reaching 70.63%. While this identification rate shows promise for clinical applications, it is important to note that our results differ from state-of-the-art research, which achieved a recognition rate of 81.5%. This discrepancy arises from the fact that our pain stimuli were induced by clinical operations, making it challenging to precisely control the stimulus intensity when compared to electrical or thermal stimuli. Despite this limitation, our pain assessment scheme demonstrates significant potential in providing objective pain identification in clinical settings. Further research and refinement of the proposed approach may lead to even more accurate and reliable pain management techniques in the future. Full article

(This article belongs to the Special Issue AI on Biomedical Signal Sensing and Processing for Health Monitoring)

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14 pages, 2801 KiB

Open AccessArticle

Hidden Semi-Markov Models-Based Visual Perceptual State Recognition for Pilots

by Lina Gao, Changyuan Wang and Gongpu Wu

Sensors 2023, 23(14), 6418; https://doi.org/10.3390/s23146418 - 14 Jul 2023

Cited by 1 | Viewed by 1137

Abstract

Pilots’ loss of situational awareness is one of the human factors affecting aviation safety. Numerous studies have shown that pilot perception errors are one of the main reasons for a lack of situational awareness without a proper system to detect these errors. The main objective of this study is to examine the changes in pilots’ eye movements during various flight tasks from the perspective of visual awareness. The pilot’s gaze rule scanning strategy is mined through cSPADE, while a hidden semi-Markov model-based model is used to detect the pilot’s visuoperceptual state, linking the correlation between the hidden state and time. The performance of the proposed algorithm is then compared with that of the hidden Markov model (HMM), and the more flexible hidden semi-Markov model (HSMM) is shown to have an accuracy of 93.55%. Full article

(This article belongs to the Special Issue AI on Biomedical Signal Sensing and Processing for Health Monitoring)

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19 pages, 1612 KiB

Open AccessArticle

Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals

by Mikhail Kulyabin, Aleksei Zhdanov, Anton Dolganov and Andreas Maier

Sensors 2023, 23(13), 5813; https://doi.org/10.3390/s23135813 - 22 Jun 2023

Cited by 1 | Viewed by 1330

Abstract

The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision medicine, particularly in disease prevention, detection, and personalized treatment. This study aims to determine the optimal combination of the mother wavelet and AI model for the analysis of pediatric electroretinogram (ERG) signals. The dataset, consisting of signals and corresponding diagnoses, undergoes Continuous Wavelet Transform (CWT) using commonly used wavelets to obtain a time-frequency representation. Wavelet images were used for the training of five widely used deep learning models: VGG-11, ResNet-50, DensNet-121, ResNext-50, and Vision Transformer, to evaluate their accuracy in classifying healthy and unhealthy patients. The findings demonstrate that the combination of Ricker Wavelet and Vision Transformer consistently yields the highest median accuracy values for ERG analysis, as evidenced by the upper and lower quartile values. The median balanced accuracy of the obtained combination of the three considered types of ERG signals in the article are 0.83, 0.85, and 0.88. However, other wavelet types also achieved high accuracy levels, indicating the importance of carefully selecting the mother wavelet for accurate classification. The study provides valuable insights into the effectiveness of different combinations of wavelets and models in classifying ERG wavelet scalograms. Full article

(This article belongs to the Special Issue AI on Biomedical Signal Sensing and Processing for Health Monitoring)

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24 pages, 4723 KiB

Open AccessArticle

EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients’ Rehabilitation

by Noor Kamal Al-Qazzaz, Alaa A. Aldoori, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad, Ahmed Kazem Mohammed and Mustafa Ibrahim Mohyee

Sensors 2023, 23(8), 3889; https://doi.org/10.3390/s23083889 - 11 Apr 2023

Cited by 3 | Viewed by 1989

Abstract

The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain–computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (

F D

) and Hurst exponent (

H u r

) were then calculated as complexity features, and Tsallis entropy (

T s E n

) and dispersion entropy (

D i s p E n

) were assessed as irregularity parameters. The MI-based BCI features were then statistically retrieved from each participant using two-way analysis of variance (ANOVA) to demonstrate the individuals’ performances from four classes (left hand, right hand, foot, and tongue). The dimensionality reduction algorithm, Laplacian Eigenmap (

L E

), was used to enhance the MI-based BCI classification performance. Utilizing k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) classifiers, the groups of post-stroke patients were ultimately determined. The findings show that

L E

with RF and KNN obtained 74.48% and 73.20% accuracy, respectively; therefore, the integrated set of the proposed features along with ICA denoising technique can exactly describe the proposed MI framework, which may be used to explore the four classes of MI-based BCI rehabilitation. This study will help clinicians, doctors, and technicians make a good rehabilitation program for people who have had a stroke. Full article

(This article belongs to the Special Issue AI on Biomedical Signal Sensing and Processing for Health Monitoring)

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15 pages, 284 KiB

Open AccessArticle

Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models

by Mouna Benchekroun, Pedro Elkind Velmovitsky, Dan Istrate, Vincent Zalc, Plinio Pelegrini Morita and Dominique Lenne

Sensors 2023, 23(4), 1807; https://doi.org/10.3390/s23041807 - 06 Feb 2023

Cited by 2 | Viewed by 33021

Abstract

Stress is an increasingly prevalent mental health condition across the world. In Europe, for example, stress is considered one of the most common health problems, and over USD 300 billion are spent on stress treatments annually. Therefore, monitoring, identification and prevention of stress are of the utmost importance. While most stress monitoring is carried out through self-reporting, there are now several studies on stress detection from physiological signals using Artificial Intelligence algorithms. However, the generalizability of these models is only rarely discussed. The main goal of this work is to provide a monitoring proof-of-concept tool exploring the generalization capabilities of Heart Rate Variability-based machine learning models. To this end, two Machine Learning models are used, Logistic Regression and Random Forest to analyze and classify stress in two datasets differing in terms of protocol, stressors and recording devices. First, the models are evaluated using leave-one-subject-out cross-validation with train and test samples from the same dataset. Next, a cross-dataset validation of the models is performed, that is, leave-one-subject-out models trained on a Multi-modal Dataset for Real-time, Continuous Stress Detection from Physiological Signals dataset and validated using the University of Waterloo stress dataset. While both logistic regression and random forest models achieve good classification results in the independent dataset analysis, the random forest model demonstrates better generalization capabilities with a stable F1 score of 61%. This indicates that the random forest can be used to generalize HRV-based stress detection models, which can lead to better analyses in the mental health and medical research field through training and integrating different models. Full article

(This article belongs to the Special Issue AI on Biomedical Signal Sensing and Processing for Health Monitoring)

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