Article
Computer Science, Artificial Intelligence
Syed Fawad Hussain, Saeed Mian Qaisar
Summary: Epilepsy, characterized by seizures, requires constant monitoring, and EEG signals are commonly used for diagnosis. A new framework for EEG-based epilepsy detection has been proposed to reduce power consumption and improve accuracy in multiclass classification. This framework involves data preprocessing and a novel classification paradigm, achieving high accuracy in testing on different datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Medicine, General & Internal
Gopal Chandra Jana, Anupam Agrawal, Prasant Kumar Pattnaik, Mangal Sain
Summary: Brain Computer Interface technology is used for seizure detection through the analysis of EEG signals. This study proposes a DWT-EMD feature level fusion-based approach for seizure detection using both multi and single channel EEG signals. The performance of different classifiers is evaluated and the DWT-EMD feature fusion method shows improved results compared to individual DWT and EMD features. Quantification results are provided in the Results section.
Article
Computer Science, Artificial Intelligence
JungHo Jeon, Hubo Cai
Summary: Improving workers' safety in the construction industry is of utmost importance. This study explores the use of wearable EEG devices and virtual reality to analyze workers' brain waves in relation to construction hazards and develops a classifier to identify these hazards. The initial results showed promising accuracy, and further strategies were implemented to improve the performance, resulting in a higher accuracy rate. The findings showcase the potential of coupling EEG, VR, and machine learning for hazard identification and contribute to the overall safety of construction workplaces.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Engineering, Multidisciplinary
Nitin Burud, J. M. Chandra Kishen
Summary: This work delves into the spectral realm of acoustic emission waveforms, proposing the use of wavelet entropy to estimate spectral disorder. It demonstrates the potential dual application of wavelet entropy as a signal discriminator and damage index. The increase in statistical variance of wavelet entropy distribution with stress level indicates the presence of multi-sources and multi-mechanistic fracture processes.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Construction & Building Technology
JungHo Jeon, Hubo Cai
Summary: This study investigates the feasibility of identifying construction hazards by developing an EEG classifier based on experiments conducted in an immersive virtual reality environment. The CatBoost classifier achieved the highest performance with 95.1% accuracy, while also identifying key channel locations and frequency bands closely associated with hazard perception.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Engineering, Mechanical
Dada Saheb Ramteke, Ram Bilas Pachori, Anand Parey
Summary: The study utilized a flexible analytic wavelet transform method to decompose the bevel gear wear signal and extract features using various entropies. These quantitative features were then fed into the Least-Squares Support Vector Machine (LS-SVM) classifier for fault diagnosis, resulting in accurate results using log energy entropy-based multi-class LS-SVM classifier and the RBF kernel function. The methodologies employed in this study outperformed previous methods such as continuous wavelet transform (CWT), discrete wavelet transform (DWT), and others in accurately identifying multi-class gearbox faults.
JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES
(2021)
Article
Engineering, Electrical & Electronic
Sudip Modak, Sayanjit Singha Roy, Rohit Bose, Soumya Chatterjee
Summary: In this study, a novel approach for automated detection and classification of focal EEG signals was proposed, utilizing cross wavelet transform and a customized CNN model. The experiment showed promising results, with 100% accuracy achieved for the delta rhythm and significantly reduced training time compared to existing CNN models.
IEEE SENSORS JOURNAL
(2021)
Article
Acoustics
Sachin Taran, Varun Bajaj, G. R. Sinha, Kemal Polat
Summary: The paper proposes detecting sleep apnea using Lampel-Ziv complexity of EEG signals, achieving high accuracy in identifying apnea events through TQWT, KW test, and ensemble classification technique.
Article
Engineering, Biomedical
Zhen Liu, Bingyu Zhu, Manfeng Hu, Zhaohong Deng, Jingxiang Zhang
Summary: This paper proposes a revised tunable Q-factor wavelet transform (RTQWT) to overcome the limitations of traditional methods and improve the adaptability to nonstationary EEG signals. Classification experiments using the extracted features show that RTQWT can effectively extract detailed features and improve the classification accuracy of EEG signals.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Biology
Asghar Zarei, Babak Mohammadzadeh Asl
Summary: A novel algorithm was developed for automatic seizure detection from EEG signals using DWT and OMP techniques, which improved detection accuracy by extracting signal coefficients, calculating nonlinear features, and statistical features. The proposed OMP-based technique with SVM classifier showed good performance in different classification tasks according to the experimental results.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Chemistry, Analytical
Ajay Kumar Maddirala, Kalyana C. Veluvolu
Summary: The use of portable electroencephalogram (EEG) devices has increased in recent years for recording brain signals in healthcare monitoring and other applications. However, the measured EEG signals often contain artifacts from eyelid blinking or eye movements, which mislead the understanding of the brain state. Traditional artifact removal techniques cannot be applied to single-channel EEG signals, calling for new techniques. In this paper, a method combining singular spectrum analysis (SSA), continuous wavelet transform (CWT), and k-means clustering algorithm is proposed to remove eye-blink artifacts from single-channel EEG signals without affecting the low frequencies.
Article
Chemistry, Analytical
Jammisetty Yedukondalu, Lakhan Dev Sharma
Summary: This study presents an automated method for removing electrooculogram (EOG) artifacts from electroencephalogram (EEG) signals. The approach decomposes the contaminated signals into intrinsic mode functions (IMFs) using Circulant Singular Spectrum Analysis (CiSSA) and removes the artifact components using 4-level discrete wavelet transform (DWT). The proposed technique effectively eliminates EOG artifacts while preserving low-frequency EEG information.
Article
Engineering, Biomedical
Jinlin Zheng, Yan Li, Yawen Zhai, Nan Zhang, Haoyang Yu, Chi Tang, Zheng Yan, Erping Luo, Kangning Xie
Summary: A physiological system consists of components operating at different time scales. The multiscale entropy (MSE) analysis has been proposed to characterize complex processes across multiple time scales. However, the use of relative scale factors in MSE analysis may result in incomparable results due to inconsistent sampling rates. This study introduces the use of absolute time scales in addition to relative scale factors in MSE analysis (MaSE). The study compares the effects of sampling rates on MSE and MaSE of simulated and real EEG time series and demonstrates the improved classification rate achieved by choosing appropriate sampling rates.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Varsha Harpale, Vinayak Bairagi
Summary: EEG analysis plays a crucial role in detecting and predicting various brain diseases, with a focus on classifying normal EEG signals from epileptic EEG signals. The study aims to identify pre-seizure and seizure states of EEG signals using time and frequency features, utilizing a fuzzy classifier for prediction accuracy.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Engineering, Multidisciplinary
Mohammed Diykh, Firas Sabar Miften, Shahab Abdulla, Ravinesh C. Deo, Siuly Siuly, Jonathan H. Green, Atheer Y. Oudahb
Summary: This paper proposes a new framework for the automated detection of neonatal seizures based on the Morse Wavelet approach, local binary pattern algorithm, and graph-based community detection algorithm. The experimental results demonstrate that the proposed model is more accurate in detecting seizures compared to traditional approaches.
Article
Engineering, Biomedical
Wenwen Wu, Yanqi Huang, Xiaomei Wu
Summary: In this study, a 2D deep learning classification network SRT was proposed to improve automatic ECG analysis. The model structure was enhanced with the CNN and Transformer-encoder modules, and a novel attention module and Dilated Stem structure were introduced to improve feature extraction. Comparative experiments showed that the proposed model outperformed several advanced methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chiheb Jamazi, Ghaith Manita, Amit Chhabra, Houssem Manita, Ouajdi Korbaa
Summary: In this study, a new dynamic and intelligent clustering method for brain tumor segmentation is proposed by combining the improved Aquila Optimizer (AO) and the K-Means algorithm. The proposed MAO-Kmeans approach aims to automatically extract the correct number and location of cluster centers and the number of pixels in each cluster in abnormal MRI images, and the experimental results demonstrate its effectiveness in improving the performance of conventional K-means clustering.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Alberto Hernando, Maria Dolores Pelaez-Coca, Eduardo Gil
Summary: This study applied a new algorithm to decompose the photoplethysmogram (PPG) pulse and identified changes in PPG pulse morphology due to pressure. The results showed that there was an increase in amplitude, width, and area values of the PPG pulse, and a decrease in ratios when pressure increased, indicating vasoconstriction. Furthermore, some parameters were found to be related to the pulse-to-pulse interval.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jens Moeller, Eveline Popanda, Nuri H. Aydin, Hubert Welp, Iris Tischoff, Carsten Brenner, Kirsten Schmieder, Martin R. Hofmann, Dorothea Miller
Summary: In this study, a method based on texture features is proposed, which can classify healthy gray and white matter against glioma degrees 4 samples with reasonable classification performance using a relatively low number of samples for training. The method achieves high classification performance without the need for large datasets and complex machine learning approaches.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Amrutha Bhaskaran, Manish Arora
Summary: The study evaluates a cyclic repetition frequency-based algorithm for fetal heart rate estimation. The algorithm improves accuracy and reliability for poor-quality signals and performs well for different gestation weeks and clinical settings.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Manan Patel, Harsh Bhatt, Manushi Munshi, Shivani Pandya, Swati Jain, Priyank Thakkar, Sangwon Yoon
Summary: Electroencephalogram (EEG) signals have been effectively used to measure and analyze neurological data and brain-related ailments. Artificial Intelligence (AI) algorithms, specifically the proposed CNN-FEBAC framework, show promising results in studying the EEG signals of autistic patients and predicting their response to stimuli with 91% accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Wencheng Gu, Kexue Sun
Summary: This research proposes an improved version of YOLOv5 (AYOLOv5) based on the attention mechanism to address the issue of low recognition rate in cell detection. Experimental results demonstrate that AYOLOv5 can accurately identify cell targets and improve the quality and recognition performance of cell pictures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Anita Gade, V. Vijaya Baskar, John Panneerselvam
Summary: Analysis of exhaled breath is an increasingly used diagnostic technique in medicine. This study introduces a new NICBGM-based model that utilizes various features and weight optimization for accurate data interpretation and result optimization.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Arsalan Asemi, Keivan Maghooli, Fereidoun Nowshiravan Rahatabad, Hamid Azadeh
Summary: Biometric authentication systems can perform identity verification with optimal accuracy in various environments and emotional changes, while the performance of signature verification systems can be affected when people are under stress. This study examines the performance of a signature verification system based on muscle synergy patterns as biometric characteristics for stressed individuals. EMG signals from hand and arm muscles were recorded and muscle synergies were extracted using Non-Negative Matrix Factorization. The extracted patterns were classified using Support Vector Machine for authentication of stressed individuals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tianjiao Guo, Jie Yang, Qi Yu
Summary: This paper proposes a CNN-based approach for segmenting four typical DR lesions simultaneously, achieving competitive performance. This approach is significant for DR lesion segmentation and has potential in other segmentation tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
G. Akilandasowmya, G. Nirmaladevi, S. U. Suganthi, A. Aishwariya
Summary: This study proposes a technique for skin cancer detection and classification using deep hidden features and ensemble classifiers. By optimizing features to reduce data dimensionality and combining ensemble classifiers, the proposed method outperforms in skin cancer classification and improves prediction accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tuuli Uudeberg, Juri Belikov, Laura Paeske, Hiie Hinrikus, Innar Liiv, Maie Bachmann
Summary: This article introduces a novel feature extraction method, the in-phase matrix profile (pMP), specifically adapted for electroencephalographic (EEG) signals, for detecting major depressive disorder (MDD). The results show that pMP outperforms Higuchi's fractal dimension (HFD) in detecting MDD, making it a promising method for future studies and potential clinical use for diagnosing MDD.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
P. Nancy, M. Parameswari, J. Sathya Priya
Summary: Stroke is the third leading cause of mortality worldwide, and early detection is crucial to avoid health risks. Existing research on disease detection using machine learning techniques has limitations, so a new stroke detection system is proposed. The experimental results show that the proposed method achieves a high accuracy rate in stroke detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Shimin Liu, Zhiwen Huang, Jianmin Zhu, Baolin Liu, Panyu Zhou
Summary: In this study, a continuous blood pressure (BP) monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model was developed. The method extracted features from electrocardiogram (ECG) and photoplethysmography (PPG) signals, and employed RFFS to select sensitive features highly correlated with BP. A hybrid prediction model of gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm was established to learn the relationship between BP and sensitive features. Experimental results demonstrated the effectiveness and advancement of the proposed method.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Weijun Gong, Yurong Qian, Weihang Zhou, Hongyong Leng
Summary: The recognition of dynamic facial expressions is challenging due to various factors, and obtaining discriminative expression features has been difficult. Traditional deep learning networks lack understanding of global and temporal expressions. This study proposes an enhanced spatial-temporal learning network to improve dynamic facial expression recognition.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
