Article
Engineering, Electrical & Electronic
R. Rajapriya, K. Rajeswari, Deepak Joshi, S. J. Thiruvengadam
Summary: This study aims to overcome limitations in existing methods through a novel feature set extracted from electromyography's wavelet bispectrum. The proposed method shows independence when dealing with multiple dynamic factors and outperforms conventional feature sets in terms of classification accuracy.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Biomedical
Padmini Sahu, Bikesh Kumar Singh, Neelamshobha Nirala
Summary: This paper presents an efficient EMG feature selection technique using an improved Artificial Bee Colony (ABC) algorithm to classify different prosthetic hand movements recorded from subjects. The proposed algorithm outperforms or is competitive with state-of-the-art algorithms in EMG feature selection and classification, achieving high accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Review
Physics, Multidisciplinary
Rodrigo Capobianco Guido
Summary: Wavelet-based analyses have made remarkable achievements in physics and related sciences. However, many people still misunderstand the fundamentals of wavelets. This article provides clear explanations of different types of wavelet transforms and their applications, helping readers to effectively utilize wavelets in their research.
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
(2022)
Article
Engineering, Multidisciplinary
Lizhi Pan, Kai Liu, Kun Zhu, Jianmin Li
Summary: Amputees have poorer performances in EMG pattern recognition compared to able-bodied individuals, and factors such as muscle weakness and atrophy, limb length, and motor cortex decrease have been studied. However, the impact of the absence of joint movements has not been explored. This study found that hand and wrist joint movements significantly affect EMG pattern recognition, providing a new perspective for future research.
JOURNAL OF BIONIC ENGINEERING
(2022)
Article
Neurosciences
Burak Tasci, Gulay Tasci, Sengul Dogan, Turker Tuncer
Summary: This study proposes a new hand-crafted feature engineering model based on ECG signals for automatic detection of psychiatric disorders. By utilizing multileveled feature extraction, best feature selection, artificial neural network classification, and majority voting algorithm, successful classification of bipolar disorder, depression, and schizophrenia is achieved with improved accuracy.
COGNITIVE NEURODYNAMICS
(2022)
Article
Chemistry, Analytical
Aberham Genetu Feleke, Luzheng Bi, Weijie Fei
Summary: This study successfully predicted the 3-D hand position of complex movements using an RFNN model, achieving an average performance of CC = 0.85 and NRMSE = 0.105. While predictions were slightly better for tasks involving quick movements, the difference in accuracy between quick and slow motions was insignificant.
Article
Chemistry, Analytical
Sara Abbaspour, Autumn Naber, Max Ortiz-Catalan, Hamid GholamHosseini, Maria Linden
Summary: This study compared the offline and real-time performance of nine different classification algorithms, showing that linear discriminant analysis and maximum likelihood estimation performed well in offline decoding, while the multilayer perceptron also excelled in real-time investigation.
Article
Mathematics
David Leserri, Nils Grimmelsmann, Malte Mechtenberg, Hanno Gerd Meyer, Axel Schneider
Summary: Machine learning-based modeling approaches for limb movement prediction using sEMG signals have shown promising results, with certain combinations of time-domain and frequency-domain features, as well as segmentation parameters, improving prediction accuracy of the neural network. Further research is needed to explore which features of sEMG signals contribute most to the accuracy of machine learning models.
Article
Entomology
Junwei Yu, Fupin Zhai, Nan Liu, Yi Shen, Quan Pan
Summary: Insect pests in stored grains cause significant losses in nutrition and economy through their activities. Therefore, it is necessary to detect the pests and estimate their population density to implement proper management for control. Image recognition, specifically through convolutional neural networks, has been shown to provide a rapid, economic, and accurate solution for grain pest detection. However, the segmentation of small pests from cluttered grain background remains challenging. This study proposes a saliency detection model that leverages frequency clues and a new receptive field block to improve the segmentation of small insects.
Article
Engineering, Biomedical
Parul Madan, Vijay Singh, Devesh Pratap Singh, Manoj Diwakar, Avadh Kishor
Summary: This paper introduces a new algorithm called STWaTV, which utilizes total variation method and a bivariate shrinkage rule in the stationary wavelet transform domain for thresholding of ECG signals to remove noise. Experimental results show that STWaTV can effectively denoise ECG signals without altering the amplitude of the original signals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Information Systems
Ali H. Al-Timemy, Youssef Serrestou, Rami N. Khushaba, Slim Yacoub, Kosai Raoof
Summary: In this study, the focus was on improving upper limb prostheses control methods using pattern recognition. Alternative control signals, such as Acoustic myography (AMG), were proposed and tested. A novel feature extraction method based on the Wavelet Scattering Transform (WST) was developed and achieved an average accuracy of 88% across a large dataset of AMG signals.
Article
Computer Science, Artificial Intelligence
Cheng Zhao, Bei Xia, Weiling Chen, Libao Guo, Jie Du, Tianfu Wang, Baiying Lei
Summary: The study proposed a pediatric echocardiographic segmentation method combining MS-Net, BFF-Net, and W-Unet, which significantly improved the diagnostic capability for congenital heart diseases.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Cosimo Ieracitano, Francesco Carlo Morabito, Amir Hussain, Nadia Mammone
Summary: In this paper, a hybrid-domain deep learning approach is proposed to decode hand movement preparation phases from EEG recordings, achieving a significant performance improvement compared to temporal-only and time-frequency-only-based methods, with an average accuracy of 76.21 +/- 3.77%. By combining temporal and time-frequency information using two CNNs and a standard multi-layer perceptron, a better classification result is achieved.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Automation & Control Systems
Jae-Beom Ahn, Hyun-Bin Jo, Hong-Je Ryoo
Summary: This article introduces a method for detecting series arc faults in photovoltaic systems based on noise pattern analysis. The proposed method distinguishes between system noise and arc noise and prevents false detection. The method uses periodic feature analysis and zero-range density analysis to achieve false detection prevention and detect arc noise. The reliability of the method is verified through noise distinction experiments.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Computer Science, Theory & Methods
Zhaohui Zheng, Bichao Xu, Jianping Ju, Zhongyuan Guo, Changhui You, Qiang Lei, Qiang Zhang
Summary: This paper proposes a new texture model CLTP for anti-counterfeiting identification, which extracts effective local texture descriptors using random features of inkjet printing. The method has high discrimination, stability, and effectiveness in anti-counterfeiting pattern images.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Health Policy & Services
Emrah Aydemir, Mehmet Baygin, Sengul Dogan, Turker Tuncer, Prabal Datta Barua, Subrata Chakraborty, Oliver Faust, N. Arunkumar, Feyzi Kaysi, U. Rajendra Acharya
Summary: Accurate classification of mental performance is crucial in brain-computer interfaces. This study presents an artificial intelligence model to quantify the clarity of thought during mental arithmetic tasks, achieving high accuracy in classification. The results suggest that it is possible to determine mental performance using artificial intelligence.
INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT
(2023)
Article
Engineering, Biomedical
Prabal Datta Barua, Tugce Keles, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Caner Feyzi Demir, Hamido Fujita, Ru-San Tan, Chui Ping Ooi, U. Rajendra Acharya
Summary: This study aims to develop a sentence classification model based on EEG signals. With the use of a novel feature extractor and an iterative multi-classifiers based majority voting algorithm, the model can effectively classify Turkish sentences in different modes.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Interdisciplinary Applications
Emrah Hancer, Abdulhamit Subasi
Summary: This article introduces an EEG-based emotion recognition framework, which performs well in identifying emotions through stages such as preprocessing, feature extraction, feature selection, and classification.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Turker Tuncer, Sengul Dogan, M. Cagri Kaya, Abdulhamit Subasi
Summary: This work proposes a new hand-crafted feature-based EEG signal classification model, incorporating a novel local histogram-based feature generation function called the cube pattern. The model includes preprocessing/signal denoising, feature extraction using the cube pattern, feature selection based on neighborhood component analysis, and classification using 25 classifiers. The model achieves over 99% accuracy with 24 out of 25 classifiers, demonstrating the high performance of the presented cube pattern and neighborhood component analysis-based model.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Prabal Datta Barua, Emrah Aydemir, Sengul Dogan, Mehmet Erten, Feyzi Kaysi, Turker Tuncer, Hamido Fujita, Elizabeth Palmer, U. Rajendra Acharya
Summary: Specific language impairment (SLI) is a common disease in children, and early diagnosis is crucial. Clinicians face difficulties in accurately detecting SLI through clinical assessments, so a machine learning model based on the graph of favipiravir molecule and vowel dataset was proposed to aid in SLI detection. The results showed that the proposed model achieved high accuracy in identifying SLI children.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Sakir Engin Sahin, Gokhan Gulhan, Prabal Datta Barua, Turker Tuncer, Sengul Dogan, Oliver Faust, U. Rajendra Acharya
Summary: We propose an automated fault type detection model called PrismPatNet, which is based on sound signals. The model achieves high accuracy and low computational complexity by decomposing the signal into six levels, extracting parameter values, and using the NCA algorithm for feature ranking and selection. The SVM classifier is then used for classification. Validation results show that our model achieves accuracy of 99.19% and 98.75% using 80:20 hold-out validation and 10-fold cross-validation, respectively. Our model outperforms previous studies in terms of overall classification accuracy and is ready for real-world applications.
Article
Computer Science, Interdisciplinary Applications
Oznur Ozaltin, Ozgur Yeniay, Abdulhamit Subasi
Summary: Coronavirus disease 2019 (COVID-19) is spreading rapidly, causing an increased workload for experts. A new deep convolutional neural network architecture called OzNet has been developed and compared with pretrained models. The classification success of CT scans using different preprocessing methods has also been evaluated. Using the DWT preprocessing method, the proposed DWT-OzNet achieved a high classification performance of more than 98.8%.
Article
Computer Science, Artificial Intelligence
Erhan Akbal, Prabal Datta Barua, Sengul Dogan, Turker Tuncer, U. Rajendra Acharya
Summary: Classification of animal species using animal sounds is important for bioacoustics work, especially for determining anurans as an indicator of climate change. However, it is challenging to count and classify anurans in their natural habitat, thus requiring computer-assisted intelligent systems. This study collected a new anuran sound dataset and proposed a hand-modeled sound classification system, achieving a high classification accuracy of 99.35% for 26 anuran species using an improved 1D-LBP and TQWT-based feature extraction method.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biochemistry & Molecular Biology
Nevsun Pihtili Tas, Oguz Kaya, Gulay Macin, Burak Tasci, Sengul Dogan, Turker Tuncer
Summary: This study successfully diagnosed ankylosing spondylitis (AS) using a pre-trained hybrid model and magnetic resonance imaging (MRI). The model demonstrated excellent classification performance across three cases and showed the ability to diagnose AS using only axial images, representing significant advancements in healthcare and economics.
Article
Medicine, General & Internal
Sermal Arslan, Mehmet Kaan Kaya, Burak Tasci, Suheda Kaya, Gulay Tasci, Filiz Ozsoy, Sengul Dogan, Turker Tuncer
Summary: In this study, a novel attention convolutional model named TurkerNeXt was proposed for detecting bipolar disorder using OCT images. A unique OCT image dataset was curated and the proposed model achieved 100% accuracy in testing and validation. The research findings highlight the potential of OCT images as a biomarker for bipolar disorder.
Article
Computer Science, Information Systems
Arif Metehan Yildiz, Masayuki Tanabe, Makiko Kobayashi, Ilknur Tuncer, Prabal Datta Barua, Sengul Dogan, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: This paper introduces Sound-based Community Emotion Recognition (SCED) as a new challenge in the machine learning domain and proposes the FF-BTP feature engineering model for discerning crowd sentiments. By utilizing a unique dataset and incorporating various techniques for feature extraction and selection, the model achieves impressive classification accuracy on the SCED dataset.
Article
Computer Science, Information Systems
Prabal Datta Barua, Makiko Kobayashi, Masayuki Tanabe, Mehmet Baygin, Jose Kunnel Paul, Thomas Iype, Sengul Dogan, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: This study aimed to develop a lightweight machine learning model for diagnosing fibromyalgia using single-lead ECG signals recorded during sleep. The model achieved high accuracy in differentiating the electrocardiographic signatures of fibromyalgia patients from control subjects, demonstrating its efficacy in clinical integration.
Article
Computer Science, Artificial Intelligence
Sinan Tatli, Gulay Macin, Irem Tasci, Burak Tasci, Prabal Datta Barua, Mehmet Baygin, Turker Tuncer, Sengul Dogan, Edward J. Ciaccio, U. Rajendra Acharya
Summary: This study aims to propose a new algorithm for early diagnosis of multiple sclerosis (MS) using machine learning. The algorithm utilizes transfer learning and hybrid feature engineering, and calculates feature vectors through multiple layers of neural networks, resulting in high classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
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)