Article
Multidisciplinary Sciences
Bushra Saeed, Muhammad Zia-ur-Rehman, Syed Omer Gilani, Faisal Amin, Asim Waris, Mohsin Jamil, Muhammad Shafique
Summary: This study compared the performance of different classifiers on multiple EMG datasets and found that artificial neural networks (ANN) outperformed linear discriminant analysis (LDA) in classifying and recognizing hand movements.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Habib Adabi Ardakani, Maryam Taghizadeh, Farzaneh Shayegh
Summary: This paper presents a method for diagnosing autism based on EEG signal analysis. By dividing the signals from individuals with autism and healthy individuals into images and using a 2D-DCNN for classification, high accuracy in diagnosis is achieved. To address the issue of limited data, an image mixing method is used for data augmentation.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Engineering, Biomedical
Elias Mazrooei Rad, Mahdi Azarnoosh, Majid Ghoshuni, Mohammad Mahdi Khalilzadeh
Summary: A new method for diagnosing Alzheimer's disease in the early stage by analyzing the features of EEG brain signals was proposed. Results showed that changes in P300 component amplitude and latency after stimulation were related to age and disease stages, and mild Alzheimer's patients exhibited altered activity in different frequency bands. Using proper features and analysis methods, Elman and convolutional neural networks outperformed linear discriminant analysis in classifying participant features accurately.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Chemistry, Analytical
Katarzyna Anna Dylag, Wiktoria Wieczorek, Waldemar Bauer, Piotr Walecki, Bozena Bando, Radek Martinek, Aleksandra Kawala-Sterniuk
Summary: This paper applies Naive Bayesian classifiers to differentiate EEG signals between children with FASD and healthy ones. The results obtained are promising, indicating that EEG recordings can be a helpful tool for potential diagnostics of FASD children, especially those without visible physical signs.
Article
Biology
Berna Ari, Nebras Sobahi, Omer F. Alcin, Abdulkadir Sengur, U. Rajendra Acharya
Summary: This article introduces a new method for automated detection of Autism Spectrum Disorders (ASD), using techniques such as the DP algorithm, sparse coding, and deep CNNs. The results show that this method achieves high accuracy and sensitivity in ASD detection.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Information Systems
Bahareh Salafian, Eyal Fishel Ben-Knaan, Nir Shlezinger, Sandrine De Ribaupierre, Nariman Farsad
Summary: We propose a hybrid model-based data-driven seizure detection algorithm called MICAL, which utilizes neural MI estimators, 1D CNN, and factor graph inference to improve the detection of seizures from EEG signals. The method successfully captures inter-channel statistical dependence and temporal correlation, leading to state-of-the-art performance.
Article
Neurosciences
Fatemeh Salehi, Mehrad Jaloli, Robert Coben, Ali Motie Nasrabadi
Summary: Studying brain connectivity helps understand brain functions, while the MVAR model may lead to fake connectivity. This study aims to discover instantaneous effects through a neural network and evaluate the algorithm's efficiency by applying it to simulated signals.
COGNITIVE NEURODYNAMICS
(2022)
Article
Engineering, Biomedical
Parikha Chawla, Shashi B. Rana, Hardeep Kaur, Kuldeep Singh
Summary: This paper presents a novel approach for automated diagnosis of autism spectrum disorder (ASD) using flexible analytic wavelet transform (FAWT) on multichannel EEG signals. The approach decomposes the EEG signals into sub-bands and extracts feature vectors for evaluation. The analysis reveals that convolutional neural network (CNN) achieves the highest accuracy for identifying ASD patients, making it extremely helpful for neurologists in the diagnostic process of autism disease.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE
(2023)
Article
Engineering, Electrical & Electronic
Nahid Ghoreishi, Ateke Goshvarpour, Samane Zare-Molekabad, Narjes Khorshidi, Somayeh Baratzade
Summary: The study aimed to diagnose ASD at an early age using a low computationally algorithm based on EEG signals. Results showed that the proposed algorithm was able to distinguish normal and autistic children with satisfactory accuracy.
SIGNAL IMAGE AND VIDEO PROCESSING
(2021)
Article
Biotechnology & Applied Microbiology
Sivamani Palanisamy, Harikumar Rajaguru
Summary: Machine learning techniques were used to improve the performance of classifiers for the detection of cardiovascular disease (CVD) from Photoplethysmography (PPG) signals. Data from 41 subjects were analyzed, and five dimensionality reduction techniques and twelve different classifiers were utilized. Results showed that the Hilbert transform techniques with the harmonic search classifier performed the best, with an accuracy of 98.31%.
BIOENGINEERING-BASEL
(2023)
Article
Computer Science, Information Systems
Fahd A. Alturki, Majid Aljalal, Akram M. Abdurraqeeb, Khalil AlSharabi, Abdullrahman A. Al-Shamma'a
Summary: This study focused on diagnosing epilepsy and ASDs through the analysis and processing of EEGs, using ICA to remove artifacts and new methods to extract features of EEGs. Different classification techniques were employed to compare and recommend optimal combination, showing that CSP-LBP-KNN combination provided the best performance in diagnosing ASDs and epilepsy.
Article
Engineering, Biomedical
Mehrnoosh Sadat Safi, Seyed Mohammad Mehdi Safi
Summary: This study successfully enhanced the accuracy of early detection of AD from EEG signals by combining Hjorth parameters with other common features. The highest accuracy was achieved using the DWT method for signal decomposition and the KNN algorithm for classification.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Carlos Emiliano Solorzano-Espindola, Humberto Sossa, Erik Zamora
Summary: This paper compares several classification methods and regularization methods to establish a baseline for common datasets in the motor imagery paradigm. It measures how these methods influence inter-subject classification.
PATTERN RECOGNITION (MCPR 2021)
(2021)
Article
Engineering, Biomedical
Qaysar Mohi Ud Din, A. K. Jayanthy
Summary: This study focuses on using EEG signals to diagnose Autism Spectrum Disorder (ASD) and classifying it using pre-trained deep Convolutional Neural Networks (CNNs) such as GoogLeNet, AlexNet, MobileNet, and SqueezeNet. The results indicate that SqueezeNet performed better in feature extraction and classification accuracy compared to other models.
BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS
(2022)
Article
Engineering, Biomedical
Qaysar Mohi Ud Din, A. K. Jayanthy
Summary: Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, affects social communication and interaction, and manifests as restricted and repetitive behaviors. Early diagnosis is crucial for intervention, but clinical diagnosis based on behavioral assessments causes delays. This study successfully categorized ASD and normal subjects using EEG features and machine learning classifiers with an accuracy of 90% using k-NN algorithm and various extracted features, and 93% using DFT features.
BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Meghana Karri, Chandra Sekhara Rao Annvarapu, U. Rajendra Acharya
Summary: Automated brain tumor segmentation using MRI is crucial for clinical decision-making and surgical planning. This study proposes a method called SGC-ARANet, which utilizes deep learning models and multiple modules to improve segmentation performance. The results demonstrate that this approach outperforms several state-of-the-art algorithms in various evaluation measures.
APPLIED INTELLIGENCE
(2023)
Review
Engineering, Biomedical
V. Jahmunah, Joel En Wei Koh, Vidya K. Sudarshan, U. Raghavendra, Anjan Gudigar, Shu Lih Oh, Hui Wen Loh, Oliver Faust, Prabal Datta Barua, Edward J. Ciaccio, U. Rajendra Acharya
Summary: In this study, information extraction methods were used to differentiate Celiac Disease (CD) from non-CD. Statistical and non-linear methods were found to be the most important for information extraction. These tools can reduce variability in clinical workflows, but bias introduced during the design of diagnostic support systems may limit the general validity of the results. Understanding the limitations of information extraction tools can improve the process.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2023)
Article
Automation & Control Systems
Moloud Abdar, Mohammad Amin Fahami, Leonardo Rundo, Petia Radeva, Alejandro F. Frangi, U. Rajendra Acharya, Abbas Khosravi, Hak-Keung Lam, Alexander Jung, Saeid Nahavandi
Summary: This article proposes a simple and novel hierarchical attentive multilevel feature fusion model for medical image classification, combined with an uncertainty-aware module for uncertainty quantification. The model is tested on various medical imaging datasets and achieves excellent classification accuracy.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Moloud Abdar, Soorena Salari, Sina Qahremani, Hak-Keung Lam, Fakhri Karray, Sadiq Hussain, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
Summary: The COVID-19 pandemic poses a major threat to human health, making the development of computer-aided detection systems a priority. This study introduces a new deep learning feature fusion model called UncertaintyFuseNet, which accurately classifies CT scan and X-ray images. The results demonstrate the efficiency and robustness of the model.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Prabal Datta Barua, Emrah Aydemir, Sengul Dogan, Mehmet Ali Kobat, Fahrettin Burak Demir, Mehmet Baygin, Turker Tuncer, Shu Lih Oh, Ru-San Tan, U. Rajendra Acharya
Summary: In this study, a novel multilevel hybrid feature extraction-based classification model was developed for the classification of myocardial infarction (MI). The model achieved accurate MI classification with low time complexity by utilizing pooling functions and feature selection algorithms to extract features from ECG signals.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Medicine, General & Internal
Faruk Oztekin, Oguzhan Katar, Ferhat Sadak, Muhammed Yildirim, Hakan Cakar, Murat Aydogan, Zeynep Ozpolat, Tuba Talo Yildirim, Ozal Yildirim, Oliver Faust, U. Rajendra Acharya
Summary: Dental caries is a common dental health issue that can cause pain and infections, reducing quality of life. Applying machine learning models for caries detection can lead to early treatment, but lack of explainability may hinder their acceptance. In this study, an explainable deep learning model for detecting dental caries was developed and evaluated. The ResNet-50 model showed slightly better performance compared to EfficientNet-B0 and DenseNet-121, achieving an accuracy of 92.00% and a sensitivity of 87.33%. The heat maps provided by the model helped explain the classification results, enabling dentists to validate and reduce misclassification.
Article
Computer Science, Interdisciplinary Applications
Oliver Faust, Simona De Michele, Joel E. W. Koh, V Jahmunah, Oh Shu Lih, Aditya P. Kamath, Prabal Datta Barua, Edward J. Ciaccio, Suzanne K. Lewis, Peter H. Green, Govind Bhagat, U. Rajendra Acharya
Summary: This study aimed to utilize AI models to distinguish between normal individuals, individuals with celiac disease (CD), and individuals with non-celiac duodenitis (NCD) based on characteristics of the small intestinal lamina propria. By preprocessing high magnification biopsy images, the study found that AI models can effectively differentiate between different types of intestinal disorders and can potentially be applied in clinical settings.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Meghana Karri, Chandra Sekhara Rao Annavarapu, U. Rajendra Acharya
Summary: This study proposes a new two-phase cross-domain transfer learning system for effective skin lesion segmentation from dermoscopic images. By using model-level and data-level transfer learning, combined with a high-performing DL network called nSknRSUNet, the system achieves excellent segmentation performance. The results show that the system outperforms existing methods in both qualitative and quantitative evaluations.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Engineering, Biomedical
Sueleyman Yaman, Elif Isilay Unlu, Hasan Guler, Abdulkadir Sengur, U. Rajendra Acharya
Summary: This study presents a new deep learning-based ensemble model and a novel double iterative ReliefF (DIRF) feature selection algorithm to enhance the automatic detection of atrophy, ischemia, and WMI using MRI images.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Biomedical
Serkan Kirik, Sengul Dogan, Mehmet Baygin, Prabal Datta Barua, Caner Feyzi Demir, Tugce Keles, Arif Metehan Yildiz, Nursena Baygin, Ilknur Tuncer, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: We developed an efficient handcrafted feature engineering model for EEG-based language identification using four directed graphs modeled on Feynman graph patterns (FGPat). We collected a dataset of 3252 EEG signals from native English-speaking Nigerian-born and Turkish subjects. In our FGPat18 model, input EEG signals and their Q wavelet transform-decomposed wavelet bands were used to extract textural and statistical features. The obtained feature vectors were input to a classification algorithm to achieve high accuracy rates. The FGPat18 model achieved a classification accuracy rate of 99.38% with 10-fold cross-validation.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Biology
Oh Shu Lih, V. Jahmunah, Elizabeth Emma Palmer, Prabal D. Barua, Sengul Dogan, Turker Tuncer, Salvador Garcia, Filippo Molinari, U. Rajendra Acharya
Summary: Epilepsy is a common neurological condition that requires a rapid and accurate diagnosis. This study proposes an automated system using deep learning to detect and monitor epilepsy using a large database. The results show high classification accuracy and the potential for transformative impact on neurological diagnostics worldwide.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Review
Computer Science, Information Systems
S. Janifer Jabin Jui, Ravinesh C. Deo, Prabal Datta Barua, Aruna Devi, Jeffrey Soar, U. Rajendra Acharya
Summary: An automated Neurological Disorder detection system is a cost-effective and resource efficient tool for medical and healthcare applications. The concept of entropy is a promising approach for processing electroencephalogram signals. Support Vector Machines and sample entropy are the most commonly used machine learning model and entropy measure respectively.
Article
Computer Science, Information Systems
Manish Sharma, Harsh Lodhi, Rishita Yadav, Niranjana Sampathila, K. S. Swathi, U. Rajendra Acharya
Summary: Sleep is important for health and well-being, covering one-third of life. The traditional evaluation of sleep quality does not consider important transient phenomena, such as K-complexes and transient fluctuations, which are crucial for diagnosing sleep disorders. This study proposes an automated, computerized approach for classifying sleep phases using a machine learning model with explainable artificial intelligence capabilities based on wavelet-based Hjorth parameters.
Article
Remote Sensing
Suat Gokhan Ozkaya, Nursena Baygin, Prabal D. Barua, Arvind R. Singh, Mohit Bajaj, Mehmet Baygin, Sengul Dogan, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: This study aims to develop a computationally lightweight, accurate, and explainable machine learning model for the automated detection of seismogram signals that could serve as an effective warning system for earthquake prediction. The model achieved a classification accuracy of 96.82% using a balanced dataset of 5001 earthquakes and 5001 non-earthquake signals. It utilized multilevel feature extraction, selector-based feature selection, and a greedy algorithm for model selection.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
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)
