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Kaya E, Saritas I. Identifying optimal channels and features for multi-participant motor imagery experiments across a participant's multi-day multi-class EEG data. Cogn Neurodyn 2024; 18:987-1003. [PMID: 38826644 PMCID: PMC11143128 DOI: 10.1007/s11571-023-09957-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 01/31/2023] [Accepted: 03/08/2023] [Indexed: 03/29/2023] Open
Abstract
The concept of the brain-computer interface (BCI) has become one of the popular research topics of recent times because it allows people to express their thoughts and control different applications and devices without actual movement. The communication between the brain and the computer or a machine is generally provided through Electroencephalogram (EEG) signals because they are cost-effective and easy to implement in normal life, not just in healthcare facilities. On the other hand, they are hard to process efficiently due to their nonlinearity and noisy nature. Thus, the field of BCI and EEG needs constant work and improvement. This paper focuses on generalizing the most efficient EEG channels and the most significant features of motor imagery (MI) signals by analyzing the recordings of one participant obtained over 20 different days. Because the classification performance usually decreases with an increasing number of class labels, we have realized the study by analyzing the signals through a new paradigm consisting of multi-class directional labels: right, left, forward, and backward. Afterward, the results are tested on EEG data obtained from 5 participants to see if the results are consistent with each other. The average accuracy of binary and multi-class classification using the Ensemble Subspace Discriminant classifier was found as 87.39 and 61.44%, respectively, with the most efficient 3-channel combination for daily BCI evaluation of one participant. On the other hand, the average accuracy of binary and multi-class classification was found as 71.84 and 50.42%, respectively, for 5 participants, with the most efficient channel combination of 4, where the first three are the same as the daily performance of one participant. During signal processing, the outliers of the signals were discarded by considering the channels separately. An algorithm was developed to dismiss the inconsistent samples within the classes. A novel adaptive filtering approach, correlation-based adaptive variational mode decomposition (CBAVMD), was proposed. The feature selection was realized based on the standard deviation values of the features between the classes. The paradigm based on the direction movements was found to be most effective, especially for binary classification of right and left directions. The generalization of effective channels and features was found to be generally successful.
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Affiliation(s)
- Esra Kaya
- Electrical and Electronics Engineering Department, Faculty of Technology, Selcuk University, Konya, Turkey
| | - Ismail Saritas
- Electrical and Electronics Engineering Department, Faculty of Technology, Selcuk University, Konya, Turkey
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Su B, Li H. Development and validation of models for risk of death in patients with systemic lupus erythematosus admitted to the intensive care unit: a retrospective study. Clin Rheumatol 2023; 42:2987-2999. [PMID: 37479889 DOI: 10.1007/s10067-023-06701-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 07/07/2023] [Accepted: 07/10/2023] [Indexed: 07/23/2023]
Abstract
OBJECTIVE For patients with systemic lupus erythematosus (SLE) in the intensive care unit (ICU), early detection of the mortality risk is a significant factor in improving outcomes. In this study, we developed and validated predictive models for the mortality risk. METHODS Using MIMIC-III v1.4 and MIMIC-IV v0.4, we collected data of patients with SLE who were admitted to the ICU. The patients were divided into two groups based on death or survival within 30 days. Two prediction models were built for each group: a traditional logistic regression model and a linear discriminant analysis (LDA) model constructed by the random subspace method (RSM) (RSM-LDA model). The performance of the two models was analyzed using the area under the receiver operating characteristic curve (AUC). RESULTS MIMIC-III and MIMIC-IV were used to establish and validate the models. This study involved 383 patients with SLE, 65 of whom died. They were divided into two groups according to whether they died within 30 days. The predictive factors were the type of admission to the ICU, SLE-associated interstitial pneumonia, lupus nephritis, immunoglobulin G level, and cardiolipin antibody level. A logistic regression model and an RSM-LDA model were established. The AUCs of the two models were 0.87 (95% confidence interval, 0.86-0.90) and 0.91 (95% confidence interval, 0.88-0.93), respectively. CONCLUSION The RSM-LDA model can predict the risk of death in patients with SLE admitted to the ICU at an early stage. Key Points • Compared with traditional prediction models, RSM-LDA model has a better ability to predict the risk of death inpatients with systemic lupus erythematosus. • Compared with traditional prediction models, the more input variables (mortality related risk factors), the better the prediction results of RSM-LDA model.
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Affiliation(s)
- Bo Su
- Department of Endocrinology, Aviation General Hospital, China Medical University, Beijing, 100012, People's Republic of China.
| | - Hui Li
- Department of the Infirmary, The Automation Engineering School of Beijing, Beijing, People's Republic of China
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Mishra NK, Kumar S, Singh SK. MmLwThV framework: A masked face periocular recognition system using thermo-visible fusion. APPL INTELL 2023; 53:2471-2487. [PMID: 35572051 PMCID: PMC9084274 DOI: 10.1007/s10489-022-03517-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2022] [Indexed: 01/14/2023]
Abstract
In wake of COVID-19, the world has adapted to a new order. People have started wearing mask on their faces to prevent getting infected. The present face recognition models are no longer proving to be efficient in the current circumstances. This is because, most of the informative part of the face is covered by mask. The periocular recognition therefore holds the key to future of face recognition. However, the periocular region proves to be insufficiently enough to generate highly discriminative features. Also, most of the pre-COVID-19 algorithms fail to work in cases, where the number of training images available is very less. We propose a lightweight periocular recognition framework that uses thermo-visible features and ensemble subspace network classifier to improve upon the existing periocular recognition systems named as Masked Mobile Lightweight Thermo-visible Face Recognition (MmLwThV). The framework successfully improves the accuracy over a single visible modality by mitigating the effect of noise present in the thermo-visible features. The experiments on WHU-IIP dataset and an in-house collected dataset named, CVBL masked dataset, successfully validate the efficacy of our proposed framework. The MmLwFR framework is lightweight and can be easily deployed on mobile phones with a visible and an infrared camera.
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Affiliation(s)
- Nayaneesh Kumar Mishra
- grid.417946.90000 0001 0572 6888Indian Institute of Information Technology, Allahabad, India
| | - Sumit Kumar
- grid.417946.90000 0001 0572 6888Indian Institute of Information Technology, Allahabad, India
| | - Satish Kumar Singh
- grid.417946.90000 0001 0572 6888Indian Institute of Information Technology, Allahabad, India
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Toğaçar M, Ergen B, Tümen V. Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Kobat MA, Kivrak T, Barua PD, Tuncer T, Dogan S, Tan RS, Ciaccio EJ, Acharya UR. Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds. Diagnostics (Basel) 2021; 11:1962. [PMID: 34829308 PMCID: PMC8620352 DOI: 10.3390/diagnostics11111962] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/17/2021] [Accepted: 10/19/2021] [Indexed: 01/22/2023] Open
Abstract
COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.
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Affiliation(s)
- Mehmet Ali Kobat
- Department of Cardiology, Firat University Hospital, Firat University, Elazig 23119, Turkey; (M.A.K.); (T.K.)
| | - Tarik Kivrak
- Department of Cardiology, Firat University Hospital, Firat University, Elazig 23119, Turkey; (M.A.K.); (T.K.)
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (T.T.); (S.D.)
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (T.T.); (S.D.)
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore;
- Department of Cardiology, Duke-NUS Graduate Medical School, Singapore 169857, Singapore
| | - Edward J. Ciaccio
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Clementi 599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Saygılı A. Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021; 47:2435-2453. [PMID: 34642612 PMCID: PMC8494633 DOI: 10.1007/s13369-021-06240-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/20/2021] [Indexed: 12/11/2022]
Abstract
COVID-19 is a virus that has been declared an epidemic by the world health organization and causes more than 2 million deaths in the world. To achieve this, computer-aided automatic diagnosis systems are created on medical images. In this study, an image processing and machine learning-based method is proposed that enables segmenting of CT images taken from COVID-19 patients and automatic detection of the virus through the segmented images. The main purpose of the study is to automatically diagnose the COVID-19 virus. The study consists of three basic steps: preprocessing, segmentation and classification. Image resizing, image sharpening, noise removal, contrast stretching processes are included in the preprocessing phase and segmentation of images with Expectation–Maximization-based Gaussian Mixture Model in the segmentation phase. In the classification stage, COVID-19 is classified as positive and negative by using kNN, decision tree, and two different ensemble methods together with the kernel support vector machines method. In the study, two different CT datasets that are open to the public and a mixed dataset created by combining these datasets were used. The best accuracy values for Dataset-1, Dataset-2 and Mixed Dataset are 98.5%, 86.3%, 94.5%, respectively. The achieved results prove that the proposed approach advances state-of-the-art performance. Within the scope of the study, a GUI that can automatically detect COVID-19 has been created.
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Affiliation(s)
- Ahmet Saygılı
- Computer Engineering Department, Tekirdağ Namık Kemal University, Silahtarağa Mahallesi Üniversite 1.Sokak, No:13, 59860 Çorlu, Tekirdağ Turkey
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Nturambirwe JFI, Perold WJ, Opara UL. Classification Learning of Latent Bruise Damage to Apples Using Shortwave Infrared Hyperspectral Imaging. SENSORS 2021; 21:s21154990. [PMID: 34372227 PMCID: PMC8348186 DOI: 10.3390/s21154990] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 06/29/2021] [Accepted: 07/01/2021] [Indexed: 11/20/2022]
Abstract
Bruise damage is a very commonly occurring defect in apple fruit which facilitates disease occurrence and spread, leads to fruit deterioration and can greatly contribute to postharvest loss. The detection of bruises at their earliest stage of development can be advantageous for screening purposes. An experiment to induce soft bruises in Golden Delicious apples was conducted by applying impact energy at different levels, which allowed to investigate the detectability of bruises at their latent stage. The existence of bruises that were rather invisible to the naked eye and to a digital camera was proven by reconstruction of hyperspectral images of bruised apples, based on effective wavelengths and data dimensionality reduced hyperspectrograms. Machine learning classifiers, namely ensemble subspace discriminant (ESD), k-nearest neighbors (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) were used to build models for detecting bruises at their latent stage, to study the influence of time after bruise occurrence on detection performance and to model quantitative aspects of bruises (severity), spanning from latent to visible bruises. Over all classifiers, detection models had a higher performance than quantitative ones. Given its highest speed in prediction and high classification performance, SVM was rated most recommendable for detection tasks. However, ESD models had the highest classification accuracy in quantitative (>85%) models and were found to be relatively better suited for such a multiple category classification problem than the rest.
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Affiliation(s)
- Jean Frederic Isingizwe Nturambirwe
- Eresearch Office, DVC—Research and Innovation, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa;
- SARChI Postharvest Technology Research Laboratory, Faculty of AgrSciences, African Institute for Postharvest Technology, Stellenbosch University, Private Bag X1, Stellenbosch 7602, South Africa
| | - Willem Jacobus Perold
- Department of Electrical and Electronic Engineering, Stellenbosch University, Private Bag X1, Stellenbosch 7602, South Africa;
| | - Umezuruike Linus Opara
- SARChI Postharvest Technology Research Laboratory, Faculty of AgrSciences, African Institute for Postharvest Technology, Stellenbosch University, Private Bag X1, Stellenbosch 7602, South Africa
- UNESCO International Centre for Biotechnology, Nsukka 410001, Nigeria
- Correspondence:
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Classification of the Acoustics of Loose Gravel. SENSORS 2021; 21:s21144944. [PMID: 34300684 PMCID: PMC8309789 DOI: 10.3390/s21144944] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 11/21/2022]
Abstract
Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.
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Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning. ENTROPY 2021; 23:e23040382. [PMID: 33804831 PMCID: PMC8063804 DOI: 10.3390/e23040382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/20/2021] [Accepted: 03/22/2021] [Indexed: 12/18/2022]
Abstract
The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM, k-nearest neighbors (k-NN) ensemble, and subspace discriminant ensemble models trained via 5-fold cross validation. Of these models, random forest performed the best, achieving an Area Under the receiver operating characteristic Curve (AUC) of 0.91 and an F1 score of 0.80 on the test set. These results suggest that an automated, quantitative assessment of traumatic spleen injury has the potential to enable faster triage and improve patient outcomes.
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12
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Liu W, Zhang R, Ling Y, Tang H, She R, Wei G, Gong X, Lu Y. Automatic recognition of breast invasive ductal carcinoma based on terahertz spectroscopy with wavelet packet transform and machine learning. BIOMEDICAL OPTICS EXPRESS 2020; 11:971-981. [PMID: 32206399 PMCID: PMC7041450 DOI: 10.1364/boe.381623] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 01/13/2020] [Accepted: 01/13/2020] [Indexed: 05/22/2023]
Abstract
We demonstrate an automatic recognition strategy for terahertz (THz) pulsed signals of breast invasive ductal carcinoma (IDC) based on a wavelet entropy feature extraction and a machine learning classifier. The wavelet packet transform was implemented into the complexity analysis of the transmission THz signal from a breast tissue sample. A novel index of energy to Shannon entropy ratio (ESER) was proposed to distinguish different tissues. Furthermore, the principal component analysis (PCA) method and machine learning classifier were further adopted and optimized for automatic classification of the THz signal from breast IDC sample. The areas under the receiver operating characteristic curves are all larger than 0.89 for the three adopted classifiers. The best breast IDC recognition performance is with the precision, sensitivity and specificity of 92.85%, 89.66% and 96.67%, respectively. The results demonstrate the effectiveness of the ESER index together with the machine learning classifier for automatically identifying different breast tissues.
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Affiliation(s)
- Wenquan Liu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Rui Zhang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Yu Ling
- Shenzhen Maternity and Child Healthcare Hospital affiliated with Southern Medical University, Shenzhen 518048, Guangdong Province, China
| | - Hongping Tang
- Shenzhen Maternity and Child Healthcare Hospital affiliated with Southern Medical University, Shenzhen 518048, Guangdong Province, China
| | - Rongbin She
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Guanglu Wei
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Xiaojing Gong
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Yuanfu Lu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
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Siuly S, Zhang X. Guest Editorial: Special issue on "Application of artificial intelligence in health research". Health Inf Sci Syst 2019; 8:1. [PMID: 31867102 DOI: 10.1007/s13755-019-0089-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Affiliation(s)
- Siuly Siuly
- 1Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Australia
| | - Xiangliang Zhang
- 2King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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