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Pan Y, Ren H, Lan L, Li Y, Huang T. Review of Predicting Synergistic Drug Combinations. Life (Basel) 2023; 13:1878. [PMID: 37763281 PMCID: PMC10533134 DOI: 10.3390/life13091878] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 08/31/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
The prediction of drug combinations is of great clinical significance. In many diseases, such as high blood pressure, diabetes, and stomach ulcers, the simultaneous use of two or more drugs has shown clear efficacy. It has greatly reduced the progression of drug resistance. This review presents the latest applications of methods for predicting the effects of drug combinations and the bioactivity databases commonly used in drug combination prediction. These studies have played a significant role in developing precision therapy. We first describe the concept of synergy. we study various publicly available databases for drug combination prediction tasks. Next, we introduce five algorithms applied to drug combinatorial prediction, which include traditional machine learning methods, deep learning methods, mathematical methods, systems biology methods and search algorithms. In the end, we sum up the difficulties encountered in prediction models.
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Affiliation(s)
- Yichen Pan
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
| | - Haotian Ren
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
| | - Liang Lan
- Department of Interactive Media, Hong Kong Baptist University, Hong Kong, China;
| | - Yixue Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- Guangzhou Laboratory, Guangzhou 510005, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
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2
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Pandey A, Singh SK, Udmale SS, Shukla K. An intelligent optimized deep learning model to achieve early prediction of epileptic seizures. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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3
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Wijaya A, Setiawan NA, Ahmad AH, Zakaria R, Othman Z. Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA). AIMS Neurosci 2023; 10:154-171. [PMID: 37426780 PMCID: PMC10323261 DOI: 10.3934/neuroscience.2023012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/27/2023] [Accepted: 06/12/2023] [Indexed: 07/11/2023] Open
Abstract
Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) and early diagnosis may help improve treatment effectiveness. To identify accurate MCI biomarkers, researchers have utilized various neuroscience techniques, with electroencephalography (EEG) being a popular choice due to its low cost and better temporal resolution. In this scoping review, we analyzed 2310 peer-reviewed articles on EEG and MCI between 2012 and 2022 to track the research progress in this field. Our data analysis involved co-occurrence analysis using VOSviewer and a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework. We found that event-related potentials (ERP), EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the primary research themes. The study showed that ERP/EEG, QEEG, and EEG-based machine learning frameworks provide high-accuracy detection of seizure and MCI. These findings identify the main research themes in EEG and MCI and suggest promising avenues for future research in this field.
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Affiliation(s)
- Adi Wijaya
- Department of Health Information Management, Universitas Indonesia Maju, Jakarta, Indonesia
| | - Noor Akhmad Setiawan
- Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Asma Hayati Ahmad
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
| | - Rahimah Zakaria
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
| | - Zahiruddin Othman
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
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4
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Sun M, Zhou A, Yang N, Xu Y, Hou Y, Richardson AG, Liu X. Design of a Sleep Modulation System with FPGA-Accelerated Deep Learning for Closed-loop Stage-Specific In-Phase Auditory Stimulation. IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS 2023; 2023:10.1109/ISCAS46773.2023.10181356. [PMID: 38623583 PMCID: PMC11018328 DOI: 10.1109/iscas46773.2023.10181356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Closed-loop sleep modulation is an emerging research paradigm to treat sleep disorders and enhance sleep benefits. However, two major barriers hinder the widespread application of this research paradigm. First, subjects often need to be wire-connected to rack-mount instrumentation for data acquisition, which negatively affects sleep quality. Second, conventional real-time sleep stage classification algorithms give limited performance. In this work, we conquer these two limitations by developing a sleep modulation system that supports closed-loop operations on the device. Sleep stage classification is performed using a lightweight deep learning (DL) model accelerated by a low-power field-programmable gate array (FPGA) device. The DL model uses a single channel electroencephalogram (EEG) as input. Two convolutional neural networks (CNNs) are used to capture general and detailed features, and a bidirectional long-short-term memory (LSTM) network is used to capture time-variant sequence features. An 8-bit quantization is used to reduce the computational cost without compromising performance. The DL model has been validated using a public sleep database containing 81 subjects, achieving a state-of-the-art classification accuracy of 85.8% and a F1-score of 79%. The developed model has also shown the potential to be generalized to different channels and input data lengths. Closed-loop in-phase auditory stimulation has been demonstrated on the test bench.
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Affiliation(s)
- Mingzhe Sun
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada M5S 2E4
| | - Aaron Zhou
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada M5S 2E4
| | - Naize Yang
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada M5S 2E4
| | - Yaqian Xu
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada M5S 2E4
| | - Yuhan Hou
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada M5S 2E4
| | - Andrew G Richardson
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA 19104
| | - Xilin Liu
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada M5S 2E4
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5
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Nath S, Das Gupta S, Saha S. Deep learning-based common skin disease image classification. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-222773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Skin disease is currently considered to be one of the most common diseases in the globe. Most of the human population has experienced it at some point but not all skin illnesses are as severe as others. There are some diseases that are symptomless or show fewer symptoms. Skin cancer is a potentially fatal outcome of serious skin illnesses that might develop if they are not detected in time. Due to the fact that medical professionals aren’t always quick or reliable enough to make a proper diagnosis. There is a hefty price tag attached to employing sophisticated equipment. Therefore, we propose a system capable of classifying skin diseases using deep learning approaches, such as CNN architecture and six preset models including MobileNet, VGG19, ResNet, EfficientNet, Inception, and DenseNet. Acne, blisters, cold sores, psoriasis, and vitiligo are some of the most often seen skin conditions, thus we scoured the web resources for relevant photographs of these conditions. We have applied data augmentation methods to extend the size of the dataset and include more image variations. In the validation dataset, we achieved an accuracy rate of approx 99 percent, while in the test dataset; we achieved an accuracy rate of approx 90 percent. Our proposed method would help to diagnose skin diseases in a faster and more cost-effective way.
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Affiliation(s)
- Sudarshan Nath
- Department of Information Technology, JIS College of Engineering, Kalyani, West Bengal, India
| | - Suparna Das Gupta
- Department of Information Technology, JIS College of Engineering, Kalyani, West Bengal, India
| | - Soumyabrata Saha
- Department of Information Technology, JIS College of Engineering, Kalyani, West Bengal, India
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6
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Ferrera D, Gómez-Esquer F, Peláez I, Barjola P, Fernandes-Magalhaes R, Carpio A, De Lahoz ME, Martín-Buro MC, Mercado F. Working memory dysfunction in fibromyalgia is associated with genotypes of the catechol- O-methyltransferase gene: an event-related potential study. Eur Arch Psychiatry Clin Neurosci 2023; 273:25-40. [PMID: 36100778 PMCID: PMC9958168 DOI: 10.1007/s00406-022-01488-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 08/29/2022] [Indexed: 11/03/2022]
Abstract
Recent findings have associated different COMT genotypes with working memory capacity in patients with fibromyalgia. Although it is thought that the COMT gene may influence neural correlates (P2 and P3 ERP components) underlying working memory impairment in this chronic-pain syndrome, it has not yet been explored. Therefore, the aim of the present research was to investigate the potential effect of the COMT gene in fibromyalgia patients on ERP working memory indices (P2 and P3 components). For this purpose, 102 participants (51 patients and 51 healthy control participants) took part in the experiment. Event-related potentials and behavioral responses were recorded while participants performed a spatial n-back task. Participants had to decide if the stimulus coincided or not in the same location as the one presented one (1-back condition) or two (2-back condition) trials before. Genotypes of the COMT gene were determined through a saliva sample from all participants. Present results significantly showed lower working memory performance (p < 0.05) in patients with fibromyalgia as compared to control participants (higher rate of errors and slower reaction times). At neural level, we found that patients exhibited enhanced frontocentral and parieto-occipital P2 amplitudes compared to control participants (p < 0.05). Interestingly, we also observed that only fibromyalgia patients carrying the Val/Val genotype of the COMT gene showed higher frontocentral P2 amplitudes than control participants (p < 0.05). Current results (behavioral outcomes and P2 amplitudes) confirmed the presence of an alteration in working memory functioning in fibromyalgia. The enhancement of frontocentral P2 could be reflecting that these patients would manifest an inefficient way of activating executive attention processes, in carriers of the Val/Val genotype of COMT. To our knowledge, the present findings are the first linking neural indices of working memory dysfunctions and COMT genotypes in fibromyalgia. Applying a subgroup of patient's strategy based on this genetic marker could be useful to establish more tailored therapeutical approaches.
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Affiliation(s)
- David Ferrera
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - Francisco Gómez-Esquer
- grid.28479.300000 0001 2206 5938Emerging Research Group of Anatomical, Molecular and Human Development Bases, Department of Basic Health Sciences, School of Health Sciences, Rey Juan Carlos University, Madrid, Spain
| | - Irene Peláez
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - Paloma Barjola
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - Roberto Fernandes-Magalhaes
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - Alberto Carpio
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - María Eugenia De Lahoz
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - María Carmen Martín-Buro
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - Francisco Mercado
- Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain.
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7
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Kumar R, Singh D, Srinivasan K, Hu YC. AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions. Healthcare (Basel) 2022; 11:healthcare11010081. [PMID: 36611541 PMCID: PMC9819078 DOI: 10.3390/healthcare11010081] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/29/2022] Open
Abstract
Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work.
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Affiliation(s)
- Ritik Kumar
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Divyangi Singh
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Yuh-Chung Hu
- Department of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26047, Taiwan
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Naskath J, Sivakamasundari G, Begum AAS. A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN. WIRELESS PERSONAL COMMUNICATIONS 2022; 128:2913-2936. [PMID: 36276226 PMCID: PMC9579606 DOI: 10.1007/s11277-022-10079-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
Deep learning is a wildly popular topic in machine learning and is structured as a series of nonlinear layers that learns various levels of data representations. Deep learning employs numerous layers to represent data abstractions to implement various computer models. Deep learning approaches like generative, discriminative models and model transfer have transformed information processing. This article proposes a comprehensive review of various deep learning algorithms Multi layer perception, Self-organizing map and deep belief networks algorithms. It first briefly introduces historical and recent state-of-the-art reviews with suitable architectures and implementation steps. Moreover, the various applications of those algorithms in various fields such as wireless networks, Adhoc networks, Mobile ad-hoc and vehicular ad-hoc networks, speech recognition engineering, medical applications, natural language processing, material science and remote sensing applications, etc. are classified.
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Affiliation(s)
- J. Naskath
- Department of Computer Science and Engineering, National Engineering College, Kovilpatti, TamilNadu India
| | - G. Sivakamasundari
- Department of Computer Science and Engineering, National Engineering College, Kovilpatti, TamilNadu India
| | - A. Alif Siddiqua Begum
- Department of Computer Science and Engineering, National Engineering College, Kovilpatti, TamilNadu India
- Department of Computer Science and Engineering, S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamilnadu India
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9
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Basurto-Hurtado JA, Cruz-Albarran IA, Toledano-Ayala M, Ibarra-Manzano MA, Morales-Hernandez LA, Perez-Ramirez CA. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms. Cancers (Basel) 2022; 14:3442. [PMID: 35884503 PMCID: PMC9322973 DOI: 10.3390/cancers14143442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/02/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
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Affiliation(s)
- Jesus A. Basurto-Hurtado
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Irving A. Cruz-Albarran
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Manuel Toledano-Ayala
- División de Investigación y Posgrado de la Facultad de Ingeniería (DIPFI), Universidad Autónoma de Querétaro, Cerro de las Campanas S/N Las Campanas, Santiago de Querétaro 76010, Mexico;
| | - Mario Alberto Ibarra-Manzano
- Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, Division de Ingenierias Campus Irapuato-Salamanca (DICIS), Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico;
| | - Luis A. Morales-Hernandez
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
| | - Carlos A. Perez-Ramirez
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
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10
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Analysis of Physiological Signals for Stress Recognition with Different Car Handling Setups. ELECTRONICS 2022. [DOI: 10.3390/electronics11060888] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
When designing a car, the vehicle dynamics and handling are important aspects, as they can satisfy a purpose in professional racing, as well as contributing to driving pleasure and safety, real and perceived, in regular drivers. In this paper, we focus on the assessment of the emotional response in drivers while they are driving on a track with different car handling setups. The experiments were performed using a dynamic professional simulator prearranged with different car setups. We recorded various physiological signals, allowing us to analyze the response of the drivers and analyze which car setup is more influential in terms of stress arising in the subjects. We logged two skin potential responses (SPRs), the electrocardiogram (ECG) signal, and eye tracking information. In the experiments, three car setups were used (neutral, understeering, and oversteering). To evaluate how these affect the drivers, we analyzed their physiological signals using two statistical tests (t-test and Wilcoxon test) and various machine learning (ML) algorithms. The results of the Wilcoxon test show that SPR signals provide higher statistical significance when evaluating stress among different drivers, compared to the ECG and eye tracking signals. As for the ML classifiers, we count the number of positive or “stress” labels of 15 s SPR time intervals for each subject and each particular car setup. With the support vector machine classifier, the mean value of the number of positive labels for the four subjects is equal to 13.13% for the base setup, 44.16% for the oversteering setup, and 39.60% for the understeering setup. In the end, our findings show that the base car setup appears to be the least stressful, and that our system enables us to effectively recognize stress while the subjects are driving in the different car configurations.
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11
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Chabchoub S, Mansouri S, Ben Salah R. Signal processing techniques applied to impedance cardiography ICG signals - a review. J Med Eng Technol 2022; 46:243-260. [PMID: 35040738 DOI: 10.1080/03091902.2022.2026508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Over the last decade, Computer-Aided Diagnosis (CAD) systems have been provided significant research focus by researchers. CAD systems have been developed in order to minimise visual errors, to compensate manual interpretation, and to help medical staff to take decisions swiftly. These systems have been considered as powerful tools for a reliable, automatic, and low-cost monitoring and diagnosis. CAD systems are based on analysis and classification of several physiological signals for detecting and assessing different diseases related to the corresponding organ. The implementation of these systems requires the application of several advanced signal processing techniques. Specifically, in cardiology, CAD systems have achieved promising results in providing an accurate and rapid detection of cardiovascular diseases (CVDs). Particularly, the number of works on signal processing field for impedance cardiography (ICG) signals starts to grow slowly in recent years. This paper presents a review study of signal processing techniques applied to the ICG signal for the denoising, the analysis, the classification and the characterisation purposes. This review is intended to provide researchers with a broad overview of the currently used signal processing techniques for ICG signal analysis, as well as to improve future research by applying other recent advanced methods.
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Affiliation(s)
- Souhir Chabchoub
- Laboratory of Biophysics and Medical Technologies, University of Tunis El-Manar, ISTMT, Tunis, Tunisia
| | - Sofienne Mansouri
- Laboratory of Biophysics and Medical Technologies, University of Tunis El-Manar, ISTMT, Tunis, Tunisia.,Department of Medical Equipment Technology, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia
| | - Ridha Ben Salah
- Laboratory of Biophysics and Medical Technologies, University of Tunis El-Manar, ISTMT, Tunis, Tunisia
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12
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Ullah M, Han K, Hadi F, Xu J, Song J, Yu DJ. PScL-HDeep: image-based prediction of protein subcellular location in human tissue using ensemble learning of handcrafted and deep learned features with two-layer feature selection. Brief Bioinform 2021; 22:bbab278. [PMID: 34337652 PMCID: PMC8574991 DOI: 10.1093/bib/bbab278] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 06/30/2021] [Accepted: 07/01/2021] [Indexed: 01/17/2023] Open
Abstract
Protein subcellular localization plays a crucial role in characterizing the function of proteins and understanding various cellular processes. Therefore, accurate identification of protein subcellular location is an important yet challenging task. Numerous computational methods have been proposed to predict the subcellular location of proteins. However, most existing methods have limited capability in terms of the overall accuracy, time consumption and generalization power. To address these problems, in this study, we developed a novel computational approach based on human protein atlas (HPA) data, referred to as PScL-HDeep, for accurate and efficient image-based prediction of protein subcellular location in human tissues. We extracted different handcrafted and deep learned (by employing pretrained deep learning model) features from different viewpoints of the image. The step-wise discriminant analysis (SDA) algorithm was applied to generate the optimal feature set from each original raw feature set. To further obtain a more informative feature subset, support vector machine-based recursive feature elimination with correlation bias reduction (SVM-RFE + CBR) feature selection algorithm was applied to the integrated feature set. Finally, the classification models, namely support vector machine with radial basis function (SVM-RBF) and support vector machine with linear kernel (SVM-LNR), were learned on the final selected feature set. To evaluate the performance of the proposed method, a new gold standard benchmark training dataset was constructed from the HPA databank. PScL-HDeep achieved the maximum performance on 10-fold cross validation test on this dataset and showed a better efficacy over existing predictors. Furthermore, we also illustrated the generalization ability of the proposed method by conducting a stringent independent validation test.
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Affiliation(s)
- Matee Ullah
- Nanjing University of Science and Technology, China
| | - Ke Han
- School of Computer Science and Engineering, Nanjing University of Science and Technology, China
| | - Fazal Hadi
- Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan
| | - Jian Xu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, China
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13
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Sahani M, Rout SK, Dash PK. FPGA implementation of epileptic seizure detection using semisupervised reduced deep convolutional neural network. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107639] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Zheng M, Yang B. A deep neural network with subdomain adaptation for motor imagery brain-computer interface. Med Eng Phys 2021; 96:29-40. [PMID: 34565550 DOI: 10.1016/j.medengphy.2021.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND The nonstationarity problem of EEG is very serious, especially for spontaneous signals, which leads to the poor effect of machine learning related to spontaneous signals, especially in related tasks across time, which correspondingly limits the practical use of brain-computer interface (BCI). OBJECTIVE In this paper, we proposed a new transfer learning algorithm, which can utilize the labeled motor imagery (MI) EEG data at the previous time to achieve better classification accuracies for a small number of labeled EEG signals at the current time. METHODS We introduced an adaptive layer into the full connection layer of a deep convolution neural network. The objective function of the adaptive layer was designed to minimize the Local Maximum Mean Discrepancy (LMMD) and the prediction error while minimizing the distance within each class (DWC) and maximizing the distance between classes within each domain (DBCWD). We verified the effectiveness of the proposed algorithm on two public datasets. RESULTS The classification accuracy of the proposed algorithm was higher than other comparison algorithms, and the paired t-test results also showed that the performance of the proposed algorithm was significantly different from that of other algorithms. The results of the confusion matrix and feature visualization showed the effectiveness of the proposed algorithm. CONCLUSION Experimental results showed that the proposed algorithm can achieve higher classification accuracy than other algorithms when there was only a small amount of labeled MI EEG data at the current time. It can be promising to be applied to the field of BCI.
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Affiliation(s)
- Minmin Zheng
- School of Mechatronic Engineering and Automation, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China; School of Mechanical and Electrical Engineering, Putian University, Fujian, China
| | - Banghua Yang
- School of Mechatronic Engineering and Automation, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China.
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15
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Xiang K, Jiang B, Shang D. The overview of the deep learning integrated into the medical imaging of liver: a review. Hepatol Int 2021; 15:868-880. [PMID: 34264509 DOI: 10.1007/s12072-021-10229-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/24/2021] [Indexed: 12/13/2022]
Abstract
Deep learning (DL) is a recently developed artificial intelligent method that can be integrated into numerous fields. For the imaging diagnosis of liver disease, several remarkable outcomes have been achieved with the application of DL currently. This advanced algorithm takes part in various sections of imaging processing such as liver segmentation, lesion delineation, disease classification, process optimization, etc. The DL optimized imaging diagnosis shows a broad prospect instead of the pathological biopsy for the advantages of convenience, safety, and inexpensiveness. In this paper, we reviewed the published representative DL-related hepatic imaging works, described the general situation of this new-rising technology in medical liver imaging and explored the future direction of DL development.
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Affiliation(s)
- Kailai Xiang
- Department of General Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China.,Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China
| | - Baihui Jiang
- Department of Ophthalmology, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China
| | - Dong Shang
- Department of General Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China. .,Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China.
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16
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Deep learning based efficient epileptic seizure prediction with EEG channel optimization. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102767] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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17
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Zhao D, Jiang R, Feng M, Yang J, Wang Y, Hou X, Wang X. A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging. Technol Health Care 2021; 30:323-336. [PMID: 34180436 DOI: 10.3233/thc-212847] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection. OBJECTIVE This paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory. METHODS The algorithm can automatically divide sleep into 5 phases including awake period, non-rapid eye movement sleep period (N1 ∼ N3) and rapid eye movement using the electroencephalogram signals. The raw signal was processed by the wavelet transform. Then, the processed signal was directly input into the deep learning algorithm to obtain the staging result. RESULTS The accuracy of staging is 93.47% using the Fpz-Cz electroencephalogram signal. When using the Fpz-Cz and electroencephalogram signal, the algorithm can obtain the highest accuracy of 94.15%. CONCLUSION These results show that this algorithm is suitable for different physiological signals and can realize end-to-end automatic sleep staging without any manual feature extraction.
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Affiliation(s)
- Dechun Zhao
- College of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Renpin Jiang
- School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Mingyang Feng
- College of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Jiaxin Yang
- College of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yi Wang
- College of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xiaorong Hou
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Xing Wang
- College of Bioengineering, Chongqing University, Chongqing, China
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18
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Rjoob K, Bond R, Finlay D, McGilligan V, J Leslie S, Rababah A, Iftikhar A, Guldenring D, Knoery C, McShane A, Peace A. Reliable Deep Learning-Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation. JMIR Med Inform 2021; 9:e25347. [PMID: 33861205 PMCID: PMC8087970 DOI: 10.2196/25347] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 02/12/2021] [Accepted: 02/27/2021] [Indexed: 11/23/2022] Open
Abstract
Background A 12-lead electrocardiogram (ECG) is the most commonly used method to diagnose patients with cardiovascular diseases. However, there are a number of possible misinterpretations of the ECG that can be caused by several different factors, such as the misplacement of chest electrodes. Objective The aim of this study is to build advanced algorithms to detect precordial (chest) electrode misplacement. Methods In this study, we used traditional machine learning (ML) and deep learning (DL) to autodetect the misplacement of electrodes V1 and V2 using features from the resultant ECG. The algorithms were trained using data extracted from high-resolution body surface potential maps of patients who were diagnosed with myocardial infarction, diagnosed with left ventricular hypertrophy, or a normal ECG. Results DL achieved the highest accuracy in this study for detecting V1 and V2 electrode misplacement, with an accuracy of 93.0% (95% CI 91.46-94.53) for misplacement in the second intercostal space. The performance of DL in the second intercostal space was benchmarked with physicians (n=11 and age 47.3 years, SD 15.5) who were experienced in reading ECGs (mean number of ECGs read in the past year 436.54, SD 397.9). Physicians were poor at recognizing chest electrode misplacement on the ECG and achieved a mean accuracy of 60% (95% CI 56.09-63.90), which was significantly poorer than that of DL (P<.001). Conclusions DL provides the best performance for detecting chest electrode misplacement when compared with the ability of experienced physicians. DL and ML could be used to help flag ECGs that have been incorrectly recorded and flag that the data may be flawed, which could reduce the number of erroneous diagnoses.
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Affiliation(s)
- Khaled Rjoob
- Faculty of Computing, Engineering & Built Environment, Ulster University, Jordanstown, United Kingdom
| | - Raymond Bond
- Faculty of Computing, Engineering & Built Environment, Ulster University, Jordanstown, United Kingdom
| | - Dewar Finlay
- Faculty of Computing, Engineering & Built Environment, Ulster University, Jordanstown, United Kingdom
| | - Victoria McGilligan
- Faculty of Life & Health Sciences, Centre for Personalised Medicine, Ulster University, Londonderry, United Kingdom
| | - Stephen J Leslie
- Department of Diabetes & Cardiovascular Science, University of the Highlands and Islands, Inverness, United Kingdom
| | - Ali Rababah
- Faculty of Computing, Engineering & Built Environment, Ulster University, Jordanstown, United Kingdom
| | - Aleeha Iftikhar
- Faculty of Computing, Engineering & Built Environment, Ulster University, Jordanstown, United Kingdom
| | | | - Charles Knoery
- Department of Diabetes & Cardiovascular Science, University of the Highlands and Islands, Inverness, United Kingdom
| | - Anne McShane
- Emergency Department, Letterkenny University Hospital, Donegal, Ireland
| | - Aaron Peace
- Western Health and Social Care Trust, Londonderry, United Kingdom
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Li Z, Li W, Wei Y, Gui G, Zhang R, Liu H, Chen Y, Jiang Y. Deep learning based automatic diagnosis of first-episode psychosis, bipolar disorder and healthy controls. Comput Med Imaging Graph 2021; 89:101882. [PMID: 33684730 DOI: 10.1016/j.compmedimag.2021.101882] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 08/04/2020] [Accepted: 02/12/2021] [Indexed: 01/16/2023]
Abstract
Neuroimaging data driven machine learning based predictive modeling and pattern recognition has been attracted strongly attention in biomedical sciences. Machine learning based diagnosis techniques are widely applied in diagnosis of neurological diseases. However, machine learning techniques are difficult to effectively extract deep information in neuroimaging data, resulting in low classification accuracy of mental illnesses. To address this problem, we propose a deep learning based automatic diagnosis first-episode psychosis (FEP), bipolar disorder (BD) and healthy controls (HC) method. Specifically, we design a convolutional neural network (CNN) framework to automatically diagnosis based on structural magnetic functional imaging (sMRI). Our dataset consists of 89 FEP patients, 40 BD patients and 83 HC. A three-way classifier (FEP vs. BD vs. HC) and three binary classifiers (FEP vs. BD, FEP vs. HC, BD vs. HC) are trained based on their gray matter volume images. Experiment results show that the performance of CNN-based method outperforms the classic classifiers both in two and three categories classification task. Our research reveals that abnormal gray matter volume is one of the main characteristics for discriminating FEP, BD and HC.
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Affiliation(s)
- Zhuangzhuang Li
- College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Wenmei Li
- College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
| | - Yan Wei
- Department of Psychiatry Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China.
| | - Guan Gui
- College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
| | - Rongrong Zhang
- Department of Psychiatry Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Haiyan Liu
- College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Yuchen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China
| | - Yiqiu Jiang
- Department of Orthopedics, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China
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20
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A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN. SENSORS 2021; 21:s21051678. [PMID: 33804366 PMCID: PMC7957771 DOI: 10.3390/s21051678] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/19/2021] [Accepted: 02/23/2021] [Indexed: 12/31/2022]
Abstract
Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods to recognize emotions, including machine learning techniques like convolutional neural network (CNN). Since CNN has shown its potential in generalization to unseen subjects, manipulating CNN hyperparameters like the window size and electrode order might be beneficial. To our knowledge, this is the first work that extensively observed the parameter selection effect on the CNN. The temporal information in distinct window sizes was found to significantly affect the recognition performance, and CNN was found to be more responsive to changing window sizes than the support vector machine. Classifying the arousal achieved the best performance with a window size of ten seconds, obtaining 56.85% accuracy and a Matthews correlation coefficient (MCC) of 0.1369. Valence recognition had the best performance with a window length of eight seconds at 73.34% accuracy and an MCC value of 0.4669. Spatial information from varying the electrode orders had a small effect on the classification. Overall, valence results had a much more superior performance than arousal results, which were, perhaps, influenced by features related to brain activity asymmetry between the left and right hemispheres.
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21
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Peng Y, Qin F, Kong W, Ge Y, Nie F, Cichocki A. GFIL: A Unified Framework for the Importance Analysis of Features, Frequency Bands and Channels in EEG-based Emotion Recognition. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3082803] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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22
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Zhang T, El Ali A, Wang C, Hanjalic A, Cesar P. CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors. SENSORS 2020; 21:s21010052. [PMID: 33374281 PMCID: PMC7795677 DOI: 10.3390/s21010052] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 12/19/2020] [Accepted: 12/21/2020] [Indexed: 11/18/2022]
Abstract
Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.
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Affiliation(s)
- Tianyi Zhang
- Multimedia Computing Group, Delft University of Technology, 2600AA Delft, The Netherlands;
- Centrum Wiskunde & Informatica (CWI), 1098XG Amsterdam, The Netherlands;
- Correspondence: (T.Z.); (P.C.)
| | - Abdallah El Ali
- Centrum Wiskunde & Informatica (CWI), 1098XG Amsterdam, The Netherlands;
| | - Chen Wang
- Future Media and Convergence Institute, Xinhuanet & State Key Laboratory of Media Convergence Production Technology and Systems, Xinhua News Agency, Beijing 100000, China;
| | - Alan Hanjalic
- Multimedia Computing Group, Delft University of Technology, 2600AA Delft, The Netherlands;
| | - Pablo Cesar
- Multimedia Computing Group, Delft University of Technology, 2600AA Delft, The Netherlands;
- Centrum Wiskunde & Informatica (CWI), 1098XG Amsterdam, The Netherlands;
- Correspondence: (T.Z.); (P.C.)
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23
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Zontone P, Affanni A, Bernardini R, Piras A, Rinaldo R, Formaggia F, Minen D, Minen M, Savorgnan C. Car Driver's Sympathetic Reaction Detection Through Electrodermal Activity and Electrocardiogram Measurements. IEEE Trans Biomed Eng 2020; 67:3413-3424. [PMID: 32305889 DOI: 10.1109/tbme.2020.2987168] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE in this paper we propose a system to detect a subject's sympathetic reaction, which is related to unexpected or challenging events during a car drive. METHODS we use the Electrocardiogram (ECG) signal and the Skin Potential Response (SPR) signal, which has several advantages with respect to other Electrodermal (EDA) signals. We record one SPR signal for each hand, and use an algorithm that, selecting the smoother signal, is able to remove motion artifacts. We extract statistical features from the ECG and SPR signals in order to classify signal segments and identify the presence or absence of emotional events via a Supervised Learning Algorithm. The experiments were carried out in a company which specializes in driving simulator equipment, using a motorized platform and a driving simulator. Different subjects were tested with this setup, with different challenging events happening on predetermined locations on the track. RESULTS we obtain an Accuracy as high as 79.10% for signal blocks and as high as 91.27% for events. CONCLUSION results demonstrate the good performance of the presented system in detecting sympathetic reactions, and the effectiveness of the motion artifact removal procedure. SIGNIFICANCE our work demonstrates the possibility to classify the emotional state of the driver, using the ECG and EDA signals and a slightly invasive setup. In particular, the proposed use of SPR and of the motion artifact removal procedure are crucial for the effectiveness of the system.
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Zhang X, Yao L, Wang X, Monaghan JJM, Mcalpine D, Zhang Y. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J Neural Eng 2020; 18. [PMID: 33171452 DOI: 10.1088/1741-2552/abc902] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 11/10/2020] [Indexed: 12/25/2022]
Abstract
Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide a comprehensive survey of the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.
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Affiliation(s)
- Xiang Zhang
- Harvard University, Cambridge, Massachusetts, UNITED STATES
| | - Lina Yao
- University of New South Wales, Sydney, New South Wales, AUSTRALIA
| | - Xianzhi Wang
- Faculty of Engineering and IT, University of Technology Sydney, 81 Broadway, Ultimo, Sydney, New South Wales, 2007, AUSTRALIA
| | | | - David Mcalpine
- Macquarie University, Sydney, New South Wales, AUSTRALIA
| | - Yu Zhang
- Stanford University, Stanford, California, 94305-6104, UNITED STATES
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Abstract
In this research study, we investigate the ability of deep learning neural networks to provide a mapping between features of a parallel distributed discrete-event simulation (PDDES) system (software and hardware) to a time synchronization scheme to optimize speedup performance. We use deep belief networks (DBNs). DBNs, which due to their multiple layers with feature detectors at the lower layers and a supervised scheme at the higher layers, can provide nonlinear mappings. The mapping mechanism works by considering simulation constructs, hardware, and software intricacies such as simulation objects, concurrency, iterations, routines, and messaging rates with a particular importance level based on a cognitive approach. The result of the mapping is a synchronization scheme such as breathing time buckets, breathing time warp, and time warp to optimize speedup. The simulation-optimization technique outlined in this research study is unique. This new methodology could be realized within the current parallel and distributed simulation modeling systems to enhance performance.
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Wilaiprasitporn T, Ditthapron A, Matchaparn K, Tongbuasirilai T, Banluesombatkul N, Chuangsuwanich E. Affective EEG-Based Person Identification Using the Deep Learning Approach. IEEE Trans Cogn Dev Syst 2020. [DOI: 10.1109/tcds.2019.2924648] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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27
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A review on segmentation of knee articular cartilage: from conventional methods towards deep learning. Artif Intell Med 2020; 106:101851. [DOI: 10.1016/j.artmed.2020.101851] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 02/09/2020] [Accepted: 03/29/2020] [Indexed: 12/14/2022]
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28
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An N, Ding H, Yang J, Au R, Ang TFA. Deep ensemble learning for Alzheimer's disease classification. J Biomed Inform 2020; 105:103411. [PMID: 32234546 PMCID: PMC9760486 DOI: 10.1016/j.jbi.2020.103411] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 02/29/2020] [Accepted: 03/23/2020] [Indexed: 01/01/2023]
Abstract
Ensemble learning uses multiple algorithms to obtain better predictive performance than any single one of its constituent algorithms could. With the growing popularity of deep learning technologies, researchers have started to ensemble these technologies for various purposes. Few, if any, however, have used the deep learning approach as a means to ensemble Alzheimer's disease classification algorithms. This paper presents a deep ensemble learning framework that aims to harness deep learning algorithms to integrate multisource data and tap the 'wisdom of experts'. At the voting layer, two sparse autoencoders are trained for feature learning to reduce the correlation of attributes and diversify the base classifiers ultimately. At the stacking layer, a nonlinear feature-weighted method based on a deep belief network is proposed to rank the base classifiers, which may violate the conditional independence. The neural network is used as a meta classifier. At the optimizing layer, over-sampling and threshold-moving are used to cope with the cost-sensitive problem. Optimized predictions are obtained based on an ensemble of probabilistic predictions by similarity calculation. The proposed deep ensemble learning framework is used for Alzheimer's disease classification. Experiments with the clinical dataset from National Alzheimer's Coordinating Center demonstrate that the classification accuracy of our proposed framework is 4% better than six well-known ensemble approaches, including the standard stacking algorithm as well. Adequate coverage of more accurate diagnostic services can be provided by utilizing the wisdom of averaged physicians. This paper points out a new way to boost the primary care of Alzheimer's disease from the view of machine learning.
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Affiliation(s)
- Ning An
- Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, Hefei University of Technology, Hefei, China; School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China.
| | - Huitong Ding
- Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, Hefei University of Technology, Hefei, China; School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China; School of Medicine, Boston University, Boston, USA.
| | - Jiaoyun Yang
- Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, Hefei University of Technology, Hefei, China; School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China.
| | - Rhoda Au
- School of Medicine, Boston University, Boston, USA; School of Public Health, Boston University, Boston, USA.
| | - Ting F A Ang
- School of Medicine, Boston University, Boston, USA.
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Maiti S, Anamika, Mandal AS, Chaudhury S. Classification of Motor Imagery EEG Signal for Navigation of Brain Controlled Drones. INTELLIGENT HUMAN COMPUTER INTERACTION 2020:3-12. [DOI: 10.1007/978-3-030-44689-5_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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30
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Zeng H, Wu Z, Zhang J, Yang C, Zhang H, Dai G, Kong W. EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model. Brain Sci 2019; 9:E326. [PMID: 31739605 PMCID: PMC6895992 DOI: 10.3390/brainsci9110326] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 11/01/2019] [Accepted: 11/12/2019] [Indexed: 02/08/2023] Open
Abstract
Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.
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Affiliation(s)
- Hong Zeng
- School of Computer Science and Technology, Hangzhou Dianzi University, Hanghzhou 310018, China; (H.Z.); (Z.W.); (J.Z.); (C.Y.); (H.Z.); (G.D.)
- Industrial NeuroScience Lab, University of Rome “La Sapienza”, 00161 Rome, Italy
| | - Zhenhua Wu
- School of Computer Science and Technology, Hangzhou Dianzi University, Hanghzhou 310018, China; (H.Z.); (Z.W.); (J.Z.); (C.Y.); (H.Z.); (G.D.)
| | - Jiaming Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hanghzhou 310018, China; (H.Z.); (Z.W.); (J.Z.); (C.Y.); (H.Z.); (G.D.)
| | - Chen Yang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hanghzhou 310018, China; (H.Z.); (Z.W.); (J.Z.); (C.Y.); (H.Z.); (G.D.)
| | - Hua Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hanghzhou 310018, China; (H.Z.); (Z.W.); (J.Z.); (C.Y.); (H.Z.); (G.D.)
| | - Guojun Dai
- School of Computer Science and Technology, Hangzhou Dianzi University, Hanghzhou 310018, China; (H.Z.); (Z.W.); (J.Z.); (C.Y.); (H.Z.); (G.D.)
| | - Wanzeng Kong
- School of Computer Science and Technology, Hangzhou Dianzi University, Hanghzhou 310018, China; (H.Z.); (Z.W.); (J.Z.); (C.Y.); (H.Z.); (G.D.)
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Hua C, Wang H, Chen J, Zhang T, Wang Q, Chang W. Novel functional brain network methods based on CNN with an application in proficiency evaluation. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.05.088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Villalba-Diez J, Zheng X, Schmidt D, Molina M. Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2019; 19:E2841. [PMID: 31247966 PMCID: PMC6651207 DOI: 10.3390/s19132841] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 06/09/2019] [Accepted: 06/21/2019] [Indexed: 01/04/2023]
Abstract
Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners' brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals.
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Affiliation(s)
- Javier Villalba-Diez
- Fakultät Management und Vertrieb, Hochschule Heilbronn Campus Schwäbisch Hall, 74523 Schwäbisch Hall, Germany.
- Departament of Artificial Intelligence, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Madrid, Spain.
| | - Xiaochen Zheng
- Departament of Business Intelligence, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 2006 Madrid, Spain
| | - Daniel Schmidt
- Saueressig GmbH + Co. KG, Gutenbergstr. 1-3, 48691 Vreden, Germany
| | - Martin Molina
- Departament of Artificial Intelligence, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Madrid, Spain
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Fusion of Motif- and Spectrum-Related Features for Improved EEG-Based Emotion Recognition. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:3076324. [PMID: 30800157 PMCID: PMC6360048 DOI: 10.1155/2019/3076324] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 12/11/2018] [Accepted: 12/12/2018] [Indexed: 11/21/2022]
Abstract
Emotion recognition is a burgeoning field allowing for more natural human-machine interactions and interfaces. Electroencephalography (EEG) has shown to be a useful modality with which user emotional states can be measured and monitored, particularly primitives such as valence and arousal. In this paper, we propose the use of ordinal pattern analysis, also called motifs, for improved EEG-based emotion recognition. Motifs capture recurring structures in time series and are inherently robust to noise, thus are well suited for the task at hand. Several connectivity, asymmetry, and graph-theoretic features are proposed and extracted from the motifs to be used for affective state recognition. Experiments with a widely used public database are conducted, and results show the proposed features outperforming benchmark spectrum-based features, as well as other more recent nonmotif-based graph-theoretic features and amplitude modulation-based connectivity/asymmetry measures. Feature and score-level fusion suggest complementarity between the proposed and benchmark spectrum-based measures. When combined, the fused models can provide up to 9% improvement relative to benchmark features alone and up to 16% to nonmotif-based graph-theoretic features.
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Sejdić E, Malandraki GA, Coyle JL. Computational deglutition: Signal and image processing methods to understand swallowing and associated disorders. IEEE SIGNAL PROCESSING MAGAZINE 2019; 36:138-146. [PMID: 31631954 PMCID: PMC6800740 DOI: 10.1109/msp.2018.2875863] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
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Zhang S, Bamakan SMH, Qu Q, Li S. Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective. IEEE Rev Biomed Eng 2019; 12:194-208. [DOI: 10.1109/rbme.2018.2864254] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Boylan GB, Kharoshankaya L, Mathieson SR. Diagnosis of seizures and encephalopathy using conventional EEG and amplitude integrated EEG. HANDBOOK OF CLINICAL NEUROLOGY 2019; 162:363-400. [PMID: 31324321 DOI: 10.1016/b978-0-444-64029-1.00018-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Seizures are more common in the neonatal period than at any other time of life, partly due to the relative hyperexcitability of the neonatal brain. Brain monitoring of sick neonates in the NICU using either conventional electroencephalography or amplitude integrated EEG is essential to accurately detect seizures. Treatment of seizures is important, as evidence increasingly indicates that seizures damage the brain in addition to that caused by the underlying etiology. Prompt treatment has been shown to reduce seizure burden with the potential to ameliorate seizure-mediated damage. Neonatal encephalopathy most commonly caused by a hypoxia-ischemia results in an alteration of mental status and problems such as seizures, hypotonia, apnea, and feeding difficulties. Confirmation of encephalopathy with EEG monitoring can act as an important adjunct to other investigations and the clinical examination, particularly when considering treatment strategies such as therapeutic hypothermia. Brain monitoring also provides useful early prognostic indicators to clinicians. Recent use of machine learning in algorithms to continuously monitor the neonatal EEG, detect seizures, and grade encephalopathy offers the exciting prospect of real-time decision support in the NICU in the very near future.
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Affiliation(s)
- Geraldine B Boylan
- Department of Paediatrics and Child Health, Irish Centre for Fetal and Neonatal Translational Research (INFANT), University College Cork, Cork, Ireland.
| | - Liudmila Kharoshankaya
- Department of Paediatrics and Child Health, Irish Centre for Fetal and Neonatal Translational Research (INFANT), University College Cork, Cork, Ireland
| | - Sean R Mathieson
- Department of Paediatrics and Child Health, Irish Centre for Fetal and Neonatal Translational Research (INFANT), University College Cork, Cork, Ireland
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Wei X, Zhou L, Chen Z, Zhang L, Zhou Y. Automatic seizure detection using three-dimensional CNN based on multi-channel EEG. BMC Med Inform Decis Mak 2018; 18:111. [PMID: 30526571 PMCID: PMC6284363 DOI: 10.1186/s12911-018-0693-8] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Automated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain experts, which are inflexible for the detection of a variety of patterns in a large amount of patients' EEG data. Moreover, conventional machine learning algorithms for seizure detection cannot accommodate multi-channel Electroencephalogram (EEG) data effectively, which contains both temporal and spatial information. Recently, deep learning technology has been widely applied to perform image processing tasks, which could learns useful features from data and process multi-channel data automatically. To provide an effective system for automatic seizure detection, we proposed a new three-dimensional (3D) convolutional neural network (CNN) structure, whose inputs are multi-channel EEG signals. METHODS EEG data of 13 patients were collected from one center hospital, which has already been inspected by experts. To represent EEG data in CNN, firstly time series of each channel of EEG data was converted into the two-dimensional image. Then all channel images were combined into 3D images according to the mutual correlation intensity between different electrodes. Finally, a CNN was constructed using 3D kernels to predict different stages of EEG data, including inter-ictal, pre-ictal, and ictal stages. The system performance was evaluated and compared with the traditional feature-based classifier and the two-dimensional (2D) deep learning method. RESULTS It demonstrated that multi-channel EEG data could provide more information for increasing the specificity and sensitivity in cpmparison result between the single and multi-channel. And the 3D CNN based on multi-channel outperformed the 2D CNN and traditional signal processing methods with an accuracy of more than 90%, an sensitivity of 88.90% and an specificity of 93.78%. CONCLUSIONS This is the first effort to apply 3D CNN in detecting seizures from EEG. It provides a new way of learning patterns simultaneously from multi-channel EEG signals, and demonstrates that deep neural networks in combination with 3D kernels can establish an effective system for seizure detection.
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Affiliation(s)
- Xiaoyan Wei
- Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080 Guangdong Province China
| | - Lin Zhou
- Software Engineering, School of Computer and Data Science, Sun Yat-sen University, Guangzhou, 510006 Guangdong Province China
| | - Ziyi Chen
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080 Guangdong Province China
| | - Liangjun Zhang
- Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080 Guangdong Province China
| | - Yi Zhou
- Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080 Guangdong Province China
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Abstract
OBJECTIVES Deep learning models such as convolutional neural networks (CNNs) have been applied successfully to medical imaging, but biomedical signal analysis has yet to fully benefit from this novel approach. Our survey aims at (i) reviewing deep learning techniques for biosignal analysis in computer- aided diagnosis; and (ii) deriving a taxonomy for organizing the growing number of applications in the field. METHODS A comprehensive literature research was performed using PubMed, Scopus, and ACM. Deep learning models were classified with respect to the (i) origin, (ii) dimension, and (iii) type of the biosignal as input to the deep learning model; (iv) the goal of the application; (v) the size and (vi) type of ground truth data; (vii) the type and (viii) schedule of learning the network; and (ix) the topology of the model. RESULTS Between January 2010 and December 2017, a total 71 papers were published on the topic. The majority (n = 36) of papers are on electrocariography (ECG) signals. Most applications (n = 25) aim at detection of patterns, while only a few (n = 6) at predection of events. Out of 36 ECG-based works, many (n = 17) relate to multi-lead ECG. Other biosignals that have been identified in the survey are electromyography, phonocardiography, photoplethysmography, electrooculography, continuous glucose monitoring, acoustic respiratory signal, blood pressure, and electrodermal activity signal, while ballistocardiography or seismocardiography have yet to be analyzed using deep learning techniques. In supervised and unsupervised applications, CNNs and restricted Boltzmann machines are the most and least frequently used, (n = 34) and (n = 15), respectively. CONCLUSION Our key-code classification of relevant papers was used to cluster the approaches that have been published to date and demonstrated a large variability of research with respect to data, application, and network topology. Future research is expected to focus on the standardization of deep learning architectures and on the optimization of the network parameters to increase performance and robustness. Furthermore, application-driven approaches and updated training data from mobile recordings are needed.
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Affiliation(s)
- Nagarajan Ganapathy
- Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig — Institute of Technology and Hannover Medical School, Braunschweig, Germany
- Indian Institute of Technology Madras, Chennai, India
| | | | - Thomas M. Deserno
- Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig — Institute of Technology and Hannover Medical School, Braunschweig, Germany
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