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Farhadi Sedehi J, Jafarnia Dabanloo N, Maghooli K, Sheikhani A. Multimodal insights into granger causality connectivity: Integrating physiological signals and gated eye-tracking data for emotion recognition using convolutional neural network. Heliyon 2024; 10:e36411. [PMID: 39253213 PMCID: PMC11381760 DOI: 10.1016/j.heliyon.2024.e36411] [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] [Received: 05/28/2024] [Revised: 08/14/2024] [Accepted: 08/14/2024] [Indexed: 09/11/2024] Open
Abstract
This study introduces a groundbreaking method to enhance the accuracy and reliability of emotion recognition systems by combining electrocardiogram (ECG) with electroencephalogram (EEG) data, using an eye-tracking gated strategy. Initially, we propose a technique to filter out irrelevant portions of emotional data by employing pupil diameter metrics from eye-tracking data. Subsequently, we introduce an innovative approach for estimating effective connectivity to capture the dynamic interaction between the brain and the heart during emotional states of happiness and sadness. Granger causality (GC) is estimated and utilized to optimize input for a highly effective pre-trained convolutional neural network (CNN), specifically ResNet-18. To assess this methodology, we employed EEG and ECG data from the publicly available MAHNOB-HCI database, using a 5-fold cross-validation approach. Our method achieved an impressive average accuracy and area under the curve (AUC) of 91.00 % and 0.97, respectively, for GC-EEG-ECG images processed with ResNet-18. Comparative analysis with state-of-the-art studies clearly shows that augmenting ECG with EEG and refining data with an eye-tracking strategy significantly enhances emotion recognition performance across various emotions.
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Affiliation(s)
- Javid Farhadi Sedehi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Nader Jafarnia Dabanloo
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ali Sheikhani
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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2
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Siuly S, Khare SK, Kabir E, Sadiq MT, Wang H. An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network. Comput Biol Med 2024; 174:108462. [PMID: 38599069 DOI: 10.1016/j.compbiomed.2024.108462] [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: 10/13/2023] [Revised: 04/07/2024] [Accepted: 04/07/2024] [Indexed: 04/12/2024]
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.
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Affiliation(s)
- Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Australia; Centre for Health Research, University of Southern Queensland, Toowoomba, Australia.
| | - Smith K Khare
- Mærsk Mc-Kinney Møller Institute, Faculty of Engineering, University of Southern Denmark, Denmark
| | - Enamul Kabir
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia
| | - Muhammad Tariq Sadiq
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, United Kingdom
| | - Hua Wang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Australia
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Wang J, Fang J, Xu Y, Zhong H, Li J, Li H, Li G. Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder. Front Hum Neurosci 2022; 16:1074587. [PMID: 36504623 PMCID: PMC9731337 DOI: 10.3389/fnhum.2022.1074587] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 11/08/2022] [Indexed: 11/25/2022] Open
Abstract
Growing evidences indicate that age plays an important role in the development of mental disorders, but few studies focus on the neuro mechanisms of generalized anxiety disorder (GAD) in different age groups. Therefore, this study attempts to reveal the neurodynamics of Young_GAD (patients with GAD under the age of 50) and Old_GAD (patients with GAD over 50 years old) through statistical analysis of multidimensional electroencephalogram (EEG) features and machine learning models. In this study, 10-min resting-state EEG data were collected from 45 Old_GAD and 33 Young_GAD. And multidimensional EEG features were extracted, including absolute power (AP), fuzzy entropy (FE), and phase-lag-index (PLI), on which comparison and analyses were performed later. The results showed that Old_GAD exhibited higher power spectral density (PSD) value and FE value in beta rhythm compared to theta, alpha1, and alpha2 rhythms, and functional connectivity (FC) also demonstrated significant reorganization of brain function in beta rhythm. In addition, the accuracy of machine learning classification between Old_GAD and Young_GAD was 99.67%, further proving the feasibility of classifying GAD patients by age. The above findings provide an objective basis in the field of EEG for the age-specific diagnosis and treatment of GAD.
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Affiliation(s)
- Jie Wang
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
| | - Jiaqi Fang
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Engineering, Zhejiang Normal University, Jinhua, China
| | - Yanting Xu
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Engineering, Zhejiang Normal University, Jinhua, China
| | - Hongyang Zhong
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
| | - Jing Li
- College of Foreign Language, Zhejiang Normal University, Jinhua, China
| | - Huayun Li
- College of Teacher Education, Zhejiang Normal University, Jinhua, China,Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China,*Correspondence: Gang Li,
| | - Gang Li
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China,Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China,Huayun Li,
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Khare SK, Gaikwad N, Bokde ND. An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets. SENSORS (BASEL, SWITZERLAND) 2022; 22:8128. [PMID: 36365824 PMCID: PMC9657151 DOI: 10.3390/s22218128] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/15/2022] [Accepted: 10/21/2022] [Indexed: 06/01/2023]
Abstract
Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database.
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Affiliation(s)
- Smith K. Khare
- Department of Electrical & Computer Engineering, Aarhus University, 8000 Aarhus, Denmark
| | - Nikhil Gaikwad
- Department of Electrical & Computer Engineering, Aarhus University, 8000 Aarhus, Denmark
| | - Neeraj Dhanraj Bokde
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, Denmark
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Diagnosis of Parkinson’s disease using higher order statistical analysis of alpha and beta rhythms. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146967] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.
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Abidi A, Ben Khalifa K, Ben Cheikh R, Valderrama Sakuyama CA, Bedoui MH. Automatic Detection of Drowsiness in EEG Records Based on Machine Learning Approaches. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10858-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Dar MN, Akram MU, Yuvaraj R, Gul Khawaja S, Murugappan M. EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning. Comput Biol Med 2022; 144:105327. [PMID: 35303579 DOI: 10.1016/j.compbiomed.2022.105327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 01/30/2022] [Accepted: 02/14/2022] [Indexed: 01/04/2023]
Abstract
Electroencephalogram (EEG) based emotion classification reflects the actual and intrinsic emotional state, resulting in more reliable, natural, and meaningful human-computer interaction with applications in entertainment consumption behavior, interactive brain-computer interface, and monitoring of psychological health of patients in the domain of e-healthcare. Challenges of EEG-based emotion recognition in real-world applications are variations among experimental settings and cognitive health conditions. Parkinson's Disease (PD) is the second most common neurodegenerative disorder, resulting in impaired recognition and expression of emotions. The deficit of emotional expression poses challenges for the healthcare services provided to PD patients. This study proposes 1D-CRNN-ELM architecture, which combines one-dimensional Convolutional Recurrent Neural Network (1D-CRNN) with an Extreme Learning Machine (ELM), robust for the emotion detection of PD patients, also available for cross dataset learning with various emotions and experimental settings. In the proposed framework, after EEG preprocessing, the trained CRNN can use as a feature extractor with ELM as the classifier, and again this trained CRNN can be used for learning of new emotions set with fine-tuning of other datasets. This paper also applied cross dataset learning of emotions by training with PD patients datasets and fine-tuning with publicly available datasets of AMIGOS and SEED-IV, and vice versa. Random splitting of train and test data with 80 - 20 ratio resulted in an accuracy of 97.75% for AMIGOS, 83.20% for PD, and 86.00% for HC with six basic emotion classes. Fine-tuning of trained architecture with four emotions of the SEED-IV dataset results in 92.5% accuracy. To validate the generalization of our results, leave one subject (patient) out cross-validation is also incorporated with mean accuracies of 95.84% for AMIGOS, 75.09% for PD, 77.85% for HC, and 84.97% for SEED-IV is achieved. Only a 1 - sec segment of EEG signal from 14 channels is enough to detect emotions with this performance. The proposed method outperforms state-of-the-art studies to classify EEG-based emotions with publicly available datasets, provide cross dataset learning, and validate the robustness of the deep learning framework for real-world application of psychological healthcare monitoring of Parkinson's disease patients.
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Affiliation(s)
- Muhammad Najam Dar
- National University of Sciences and Technology, Islamabad, Postcode: 44000, Pakistan.
| | - Muhammad Usman Akram
- National University of Sciences and Technology, Islamabad, Postcode: 44000, Pakistan.
| | - Rajamanickam Yuvaraj
- Nanyang Technological University (NTU), 639798, Singapore; Science of Learning in Education (SoLE), Office of Education Research (OER), National Institute of Education (NIE), 637616, Singapore.
| | - Sajid Gul Khawaja
- National University of Sciences and Technology, Islamabad, Postcode: 44000, Pakistan.
| | - M Murugappan
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Al-Jahra, Kuwait.
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Shaban M, Amara AW. Resting-state electroencephalography based deep-learning for the detection of Parkinson's disease. PLoS One 2022; 17:e0263159. [PMID: 35202420 PMCID: PMC8870584 DOI: 10.1371/journal.pone.0263159] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/12/2022] [Indexed: 02/02/2023] Open
Abstract
Parkinson's disease (PD) is one of the most serious and challenging neurodegenerative disorders to diagnose. Clinical diagnosis on observing motor symptoms is the gold standard, yet by this point nerve cells are degenerated resulting in a lower efficacy of therapeutic treatments. In this study, we introduce a deep-learning approach based on a recently-proposed 20-Layer Convolutional Neural Network (CNN) applied on the visual realization of the Wavelet domain of a resting-state EEG. The proposed approach was able to efficiently and accurately detect PD as well as distinguish subjects with PD on medications from subjects who are off medication. The gradient-weighted class activation mapping (Grad-CAM) was used to visualize the features based on which the approach provided the predictions. A significantly high accuracy, sensitivity, specificity, AUC, and Weighted Kappa Score up to 99.9% were achieved and the visualization of the regions in the Wavelet images that contributed to the deep-learning approach decisions was provided. The proposed framework can then serve as an effective computer-aided diagnostic tool that will support physicians and scientists in further understanding the nature of PD and providing an objective and confident opinion regarding the clinical diagnosis of the disease.
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Affiliation(s)
- Mohamed Shaban
- Electrical and Computer Engineering, University of South Alabama, Mobile, AL, United States of America
| | - Amy W. Amara
- Neurology, University of Alabama at Birmingham, Birmingham, AL, United States of America
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Saeidi M, Karwowski W, Farahani FV, Fiok K, Taiar R, Hancock PA, Al-Juaid A. Neural Decoding of EEG Signals with Machine Learning: A Systematic Review. Brain Sci 2021; 11:1525. [PMID: 34827524 PMCID: PMC8615531 DOI: 10.3390/brainsci11111525] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/04/2021] [Accepted: 11/11/2021] [Indexed: 11/16/2022] Open
Abstract
Electroencephalography (EEG) is a non-invasive technique used to record the brain's evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.
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Affiliation(s)
- Maham Saeidi
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (F.V.F.); (K.F.)
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (F.V.F.); (K.F.)
| | - Farzad V. Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (F.V.F.); (K.F.)
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Krzysztof Fiok
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (F.V.F.); (K.F.)
| | - Redha Taiar
- MATIM, Moulin de la Housse, Université de Reims Champagne Ardenne, CEDEX 02, 51687 Reims, France;
| | - P. A. Hancock
- Department of Psychology, University of Central Florida, Orlando, FL 32816, USA;
| | - Awad Al-Juaid
- Industrial Engineering Department, Taif University, Taif 26571, Saudi Arabia;
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Novel automated PD detection system using aspirin pattern with EEG signals. Comput Biol Med 2021; 137:104841. [PMID: 34509880 DOI: 10.1016/j.compbiomed.2021.104841] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 11/20/2022]
Abstract
BACKGROUND AND OBJECTIVE Parkinson's disease (PD) is one of the most common diseases worldwide which reduces quality of life of patients and their family members. The electroencephalogram (EEG) signals coupled with various advanced machine-learning algorithms have been widely used to detect PD automatically. In this paper, we propose a novel aspirin pattern to detect PD accurately using EEG signals. METHOD In this research, the feature generation ability of a chemical graph is investigated. Therefore, this work presents a new graph-based aspirin model for automated PD detection using EEG signals. The proposed method consists of (i) multilevel feature generation phase involving new aspirin pattern, statistical moments, and maximum absolute pooling (MAP), (ii) selection of most discriminative features using neighborhood component analysis (NCA), and (iii) classification using k nearest neighbor (kNN) for automated detection of PD and (iv) iterative majority voting. RESULTS A public dataset has been used to develop the proposed model. Two cases are created, and these cases consisted of two classes. Leave one subject out (LOSO) validation have been used to calculate robust results. Our proposal achieved 93.57% and 95.48% classification accuracies for Case 1 and Case 2 respectively. CONCLUSION Our developed automated PD model is accurate and equipped to be tested with more diverse EEG datasets.
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Aslan Z. Migraine detection from EEG signals using tunable Q-factor wavelet transform and ensemble learning techniques. Phys Eng Sci Med 2021; 44:1201-1212. [PMID: 34505992 DOI: 10.1007/s13246-021-01055-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/31/2021] [Indexed: 10/20/2022]
Abstract
Migraine is one of the major neurovascular diseases that recur, can persist for a long time, cripple or weaken the brain. This study uses electroencephalogram (EEG) signals for the diagnosis of migraine, and a computer-aided diagnosis system is presented to support expert opinion. A tunable Q-factor wavelet transform (TQWT) based method is proposed for the analysis of the oscillatory structure of EEG signals. With TQWT, EEG signals are decomposed into sub bands. Then, the features are statistically calculated from these bands. The success of the obtained features in distinguishing between migraine patients and healthy control subjects was performed using the Kruskal Wallis test. Feature values obtained from each sub band were classified using well-known ensemble learning techniques and their classification performances were tested. Among the evaluated classifiers, the highest classification performance was achieved as 89.6% by using the Rotation Forest algorithm with the features obtained with Sub band 2. These results reveal the potential of the study as a tool that will support expert opinion in the diagnosis of migraine.
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Affiliation(s)
- Zülfikar Aslan
- Technical Sciences Vocational School, Gaziantep University, 27310, Gaziantep, Turkey.
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Khare SK, Bajaj V, Acharya UR. Detection of Parkinson’s disease using automated tunable Q wavelet transform technique with EEG signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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