Article
Computer Science, Artificial Intelligence
Turker Tuncer, Sengul Dogan, U. Rajendra Acharya
Summary: The study developed a model using chaotic feature generation function for EEG signal classification, achieving high classification performance by using chaotic one-dimensional local binary pattern and wavelet packet decomposition techniques for abnormal EEG detection.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
P. Shanthi, S. Nickolas
Summary: A method utilizing feature fusion technique to analyze the association among adjacent pixels by combining LBP with LNEP for efficient texture representation is proposed. Experimental findings show that the hybrid feature outperforms individual features in recognition accuracy, particularly in noisy environments.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Multidisciplinary Sciences
Sayyad Alizadeh, Hossein B. Jond, Vasif V. Nabiyev, Cemal Kose
Summary: In this paper, a novel automatic method called Modified Multi-Block Local Binary Pattern (MMB-LBP) was proposed to maintain the local features of a shoeprint image and place a pattern in a block. The proposed method outperforms other methods in terms of retrieving complete and incomplete shoeprints, and shows significant resistance to distortions like rotation, salt and pepper noise, and Gaussian white noise.
Article
Medicine, General & Internal
S. Jeba Priya, Arockia Jansi Rani, M. S. P. Subathra, Mazin Abed Mohammed, Robertas Damasevicius, Neha Ubendran
Summary: This study utilized various feature extraction methods for the recognition and classification of Parkinson's disease, providing a basis for early detection and prevention of deteriorating health. The SWLNGP method showed better performance and could serve as an effective feature extraction technique for identifying Parkinsonian gait.
Article
Radiology, Nuclear Medicine & Medical Imaging
Suat Kamil Sut, Mustafa Koc, Gokhan Zorlu, Ihsan Serhatlioglu, Prabal Datta Barua, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: A new handcrafted machine learning method has been developed for the automated and accurate classification of adrenal gland CT images. The method analyzed a dataset of 759 CT image slices from 96 subjects, and achieved high accuracy in classification using k-nearest neighbor, support vector machine, and neural network classifiers.
JOURNAL OF DIGITAL IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Mohsen Gholami, Rabab Ward, Ravneet Mahal, Maryam Mirian, Kevin Yen, Kye Won Park, Martin J. McKeown, Z. Jane Wang
Summary: In this study, an automatic, objective, and weakly supervised method was proposed for labeling gait videos of Parkinson's Disease patients. The method utilizes labeling functions and a generative model to classify patients' gait, and incorporates a weakly supervised 3D human pose estimation method to improve accuracy. The results suggest that the use of labeling functions may provide a robust means to interpret and classify patient-oriented videos involving motor tasks.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Computer Science, Information Systems
Chengyang Ye, Qiang Ma
Summary: This study proposes an unsupervised representation learning model for multivariate time series by comprehensively considering the global and local information of the data. It introduces a specially designed local binary pattern method for multivariate time series to improve the representation performance, and also presents a novel unsupervised approach for learning multivariate time series representations.
Article
Computer Science, Information Systems
Burak Tasci, Gulay Tasci, Hakan Ayyildiz, Aditya P. Kamath, Prabal Datta Barua, Turker Tuncer, Sengul Dogan, Edward J. Ciaccio, Subrata Chakraborty, U. Rajendra Acharya
Summary: This paper proposes a novel feature engineering technique using scattergram images obtained through a blood test for automated Schizophrenia detection. The experiment results demonstrate the effectiveness of scattergram images in achieving high classification accuracy, indicating their potential as a valuable tool.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Hang Yuan, Zhenxing Lei, Xianglong You, Zhe Dong, Huijuan Zhang, Chi Zhang, Yubin Zhao, Jianjuan Liu
Summary: Rack and pinion drives are crucial for battery-swapping systems in electric heavy trucks. This study proposes a fault diagnosis framework that incorporates adaptive down-sampling, three-dimensional acceleration data fusion, multi-scale local binary pattern extraction, and sparse representation. The effectiveness of this approach is demonstrated using monitoring data, and comparative studies show its superior performance.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Nuh Alpaslan
Summary: This paper presents novel hybrid methods based on neutrosophic set and LBP features. By transforming the input image into a neutrosophic domain and combining with grayscale images, the proposed methods can extract more robust features. The methods contribute to the classification performance with reasonable computational cost and achieve satisfactory results in experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Robotics
Iago Suarez, Jose M. Buenaposada, Luis Baumela
Summary: New binary image descriptors, BAD and HashSIFT, offer a balance between accuracy and resource consumption, with BAD being the fastest implementation and HashSIFT showing high accuracy and computational efficiency. Public source code is available for further research and development.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Review
Engineering, Biomedical
Vasileios Skaramagkas, Anastasia Pentari, Zinovia Kefalopoulou, Manolis Tsiknakis
Summary: This study provides an exhaustive review on deep learning techniques used in the prognosis and evolution of Parkinson's Disease symptoms and characteristics. It summarizes the relevant information regarding learning and development process, primary outcomes, and sensory equipment related information. Deep learning algorithms have outperformed conventional machine learning approaches in many PD-related tasks, but there are significant drawbacks including a lack of data availability and interpretability of models in the existing research.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Guo Bai, Zhiyuan Qu, Qianyang Xie, Hongyi Jing, Shihui Chen, Leilei Yu, Zhiyuan Zhang, Chi Yang
Summary: In this study, a Multimodal Stepped Attention Net (MSANet) was built and a deep learning network was used to train the assisted diagnosis AI model (TMJ MRI-Net) for temporomandibular joint (TMJ) disc displacement diagnosis. The AI-assisted strategy significantly improved the diagnostic accuracy and efficiency of physicians based on TMJ MRI.
Article
Engineering, Biomedical
N. J. Sairamya, M. S. P. Subathra, Easter S. Suviseshamuthu, S. Thomas George
Summary: This study proposes a comprehensive feature representation using a one-dimensional quad binary pattern for effective epileptic focus localization in EEG signals. Different strategies are employed, including local pattern transformation, nonlinear feature computation, and histogram feature extraction, to classify non-focal and focal EEG signals with high accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Information Systems
Zeng Qiang, Adu Jianhua, Sun Xiaoya, Hong Sunyan
Summary: An extended complete LBP (ELBP) method is proposed for texture classification in this paper, which provides a detailed description and analysis of the composition of local feature vectors. Experimental results show that the algorithm has good scalability and robustness.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
N. J. Sairamya, M. Joel Premkumar, S. Thomas George, M. S. P. Subathra
Summary: Wavelet transforms have been widely used in characterizing EEG signals for automatic diagnosis of epileptic seizure. In this study, DWT was found to achieve the highest classification accuracy for all experimental cases, demonstrating its effectiveness in this area.
IETE JOURNAL OF RESEARCH
(2021)
Review
Engineering, Electrical & Electronic
D. Merlin Praveena, D. Angelin Sarah, S. Thomas George
Summary: This paper presents a detailed survey on the application of deep learning architecture in EEG signals, discussing the use of different deep learning methods and architectures for EEG signal analysis, as well as the challenges and limitations in classification.
IETE JOURNAL OF RESEARCH
(2022)
Article
Engineering, Biomedical
N. J. Sairamya, M. S. P. Subathra, Easter S. Suviseshamuthu, S. Thomas George
Summary: This study proposes a comprehensive feature representation using a one-dimensional quad binary pattern for effective epileptic focus localization in EEG signals. Different strategies are employed, including local pattern transformation, nonlinear feature computation, and histogram feature extraction, to classify non-focal and focal EEG signals with high accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Engineering, Electrical & Electronic
Sanjukta Rani Jena, Selvaraj Thomas George, Deivendran Narain Ponraj
Summary: The study proposes a new nodule diagnosis model, MS YOLO, which achieves high accuracy classification without the need for spatial annotation of nodules. By sampling multiple cross-sections of the tumor and using convolutional neural networks for processing, the research demonstrates better performance and achieves high classification accuracy in experiments.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2021)
Article
Clinical Neurology
R. Catherine Joy, S. Thomas George, A. Albert Rajan, M. S. P. Subathra
Summary: This research proposes an efficient computer-aided technological solution for detecting and classifying ADHD subjects based on different nonlinear entropy estimators and an artificial neural network classifier. The experiment results show that the permutation entropy has the highest classification accuracy, sensitivity, and specificity for ADHD detection. Different entropy estimators derived significant variance in potential features obtained from specific brain regions, indicating their importance in distinguishing ADHD from normal subjects.
CLINICAL EEG AND NEUROSCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Sanjukta Rani Jena, S. Thomas George, D. Narain Ponraj
Summary: This paper introduces a method for lung cancer diagnosis using a combination of deep Gaussian mixture model and region-based convolutional neural network. By preprocessing, segmentation, and feature extraction of images, the diagnostic accuracy can be significantly improved, outperforming traditional methods.
NEURAL COMPUTING & APPLICATIONS
(2021)
Review
Health Care Sciences & Services
J. Prasanna, M. S. P. Subathra, Mazin Abed Mohammed, Robertas Damasevicius, Nanjappan Jothiraj Sairamya, S. Thomas George
Summary: This review paper focuses on automatic seizure detection in pediatric patients using EEG signals and classifiers. It summarizes the application of personalized medicine approaches in the diagnosis of epilepsy, analyzes challenges and performance metrics using data from the CHB-MIT database.
JOURNAL OF PERSONALIZED MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
N. J. Sairamya, M. S. P. Subathra, S. Thomas George
Summary: A computer-aided diagnosis method using RLNDiP technique for Schizophrenia was proposed in this study, achieving a maximum accuracy of 100% with the fusion of alpha brain rhythm and TD features in an artificial neural network, surpassing existing methods in classification performance by selecting effective connectivity features.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Correction
Computer Science, Information Systems
Abraham George, X. Anitha Mary, S. Thomas George
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Abraham George, X. Anitha Mary, S. Thomas George
Summary: This paper presents a novel approach for determining the musical key of a given song using machine learning algorithms. The proposed model achieved high accuracy and demonstrated its potential for efficient key determination.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Review
Computer Science, Information Systems
J. Anu Shilvya, S. Thomas George, M. S. P. Subathra, P. Manimegalai, Mazin Abed Mohammed, Mustafa Musa Jaber, Afsaneh Kazemzadeh, Mohammed Nasser Al-Andoli
Summary: Internet of Things (IoT) is a significant advancement in the medical field, allowing medical devices to be interconnected with the internet for better problem identification and patient adaptation. However, standardization becomes a key issue due to the availability of multiple sensors and communication systems. This study presents the current research on sensors and communication models used for home-based monitoring, aiding researchers in selecting the most suitable protocols for healthcare devices.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2022)
Article
Engineering, Biomedical
M. Bhuvaneshwari, E. Grace Mary Kanaga, S. Thomas George
Summary: This article discusses the classification problem of electroencephalography (EEG) signals, and proposes the use of convolutional neural network (CNN) and automated hyperparameter optimization algorithm to address this issue. The experimental results show that the proposed algorithm achieves competitive performance in classification.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE
(2023)
Proceedings Paper
Telecommunications
Jobin T. Philip, S. Thomas George, M. S. P. Subathra
SECOND INTERNATIONAL CONFERENCE ON COMPUTER NETWORKS AND COMMUNICATION TECHNOLOGIES, ICCNCT 2019
(2020)
Article
Engineering, Biomedical
Shobha Jose, S. Thomas George, M. S. P. Subathra, Vikram Shenoy Handiru, Poornaselvan Kittu Jeevanandam, Umberto Amato, Easter Selvan Suviseshamuthu
IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY
(2020)
Proceedings Paper
Computer Science, Theory & Methods
P. Nagabushanam, S. Thomas George, Praharsha Davu, P. Bincy, Meghana Naidu, S. Radha
2020 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATION SYSTEMS (ICACCS)
(2020)
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)
