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Tore U, Abilgazym A, Asunsolo-del-Barco A, Terzic M, Yemenkhan Y, Zollanvari A, Sarria-Santamera A. Diagnosis of Endometriosis Based on Comorbidities: A Machine Learning Approach. Biomedicines 2023; 11:3015. [PMID: 38002015 PMCID: PMC10669733 DOI: 10.3390/biomedicines11113015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/26/2023] Open
Abstract
Endometriosis is defined as the presence of estrogen-dependent endometrial-like tissue outside the uterine cavity. Despite extensive research, endometriosis is still an enigmatic disease and is challenging to diagnose and treat. A common clinical finding is the association of endometriosis with multiple diseases. We use a total of 627,566 clinically collected data from cases of endometriosis (0.82%) and controls (99.18%) to construct and evaluate predictive models. We develop a machine learning platform to construct diagnostic tools for endometriosis. The platform consists of logistic regression, decision tree, random forest, AdaBoost, and XGBoost for prediction, and uses Shapley Additive Explanation (SHAP) values to quantify the importance of features. In the model selection phase, the constructed XGBoost model performs better than other algorithms while achieving an area under the curve (AUC) of 0.725 on the test set during the evaluation phase, resulting in a specificity of 62.9% and a sensitivity of 68.6%. The model leads to a quite low positive predictive value of 1.5%, but a quite satisfactory negative predictive value of 99.58%. Moreover, the feature importance analysis points to age, infertility, uterine fibroids, anxiety, and allergic rhinitis as the top five most important features for predicting endometriosis. Although these results show the feasibility of using machine learning to improve the diagnosis of endometriosis, more research is required to improve the performance of predictive models for the diagnosis of endometriosis. This state of affairs is in part attributed to the complex nature of the condition and, at the same time, the administrative nature of our features. Should more informative features be used, we could possibly achieve a higher AUC for predicting endometriosis. As a result, we merely perceive the constructed predictive model as a tool to provide auxiliary information in clinical practice.
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Affiliation(s)
- Ulan Tore
- School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan; (U.T.); (A.A.)
| | - Aibek Abilgazym
- School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan; (U.T.); (A.A.)
| | - Angel Asunsolo-del-Barco
- Department of Surgery, Medical and Social Sciences, Faculty of Medicine, University of Alcalá, 288871 Alcalá de Henares, Spain;
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY 10028, USA
- Ramón y Cajal Institute of Healthcare Research (IRYCIS), 28034 Madrid, Spain
| | - Milan Terzic
- Department of Surgery, School of Medicine, Nazarbayev University, Astana 010000, Kazakhstan;
- Clinical Academic Department of Women’s Health, CF “University Medical Center”, Astana 010000, Kazakhstan
- Department of Obstetrics, Gynecology and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Yerden Yemenkhan
- Department of Medicine, School of Medicine, Nazarbayev University, Astana 010000, Kazakhstan;
| | - Amin Zollanvari
- School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan; (U.T.); (A.A.)
| | - Antonio Sarria-Santamera
- Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana 010000, Kazakhstan;
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Amin HU, Ullah R, Reza MF, Malik AS. Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques. J Neuroeng Rehabil 2023; 20:70. [PMID: 37269019 DOI: 10.1186/s12984-023-01179-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 04/19/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task. METHODS EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects. RESULTS The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers. CONCLUSION The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.
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Affiliation(s)
- Hafeez Ullah Amin
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham, Jalan Broga, 43500, Semenyih, Malaysia
| | - Rafi Ullah
- Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Malaysia
| | - Mohammed Faruque Reza
- Department of Neurosciences, School of Medical Sciences, Hospital Universiti Sains Malaysia, Kubang Kerian, 16150, Kota Bharu, Malaysia
| | - Aamir Saeed Malik
- Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic.
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Habashi AG, Azab AM, Eldawlatly S, Aly GM. Generative adversarial networks in EEG analysis: an overview. J Neuroeng Rehabil 2023; 20:40. [PMID: 37038142 PMCID: PMC10088201 DOI: 10.1186/s12984-023-01169-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 03/30/2023] [Indexed: 04/12/2023] Open
Abstract
Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as engineering applications. However, one of the challenges associated with recording EEG data is the difficulty of recording large amounts of data. Consequently, data augmentation is a potential solution to overcome this challenge in which the objective is to increase the amount of data. Inspired by the success of Generative Adversarial Networks (GANs) in image processing applications, generating artificial EEG data from the limited recorded data using GANs has seen recent success. This article provides an overview of various techniques and approaches of GANs for augmenting EEG signals. We focus on the utility of GANs in different applications including Brain-Computer Interface (BCI) paradigms such as motor imagery and P300-based systems, in addition to emotion recognition, epileptic seizures detection and prediction, and various other applications. We address in this article how GANs have been used in each study, the impact of using GANs on the model performance, the limitations of each algorithm, and future possibilities for developing new algorithms. We emphasize the utility of GANs in augmenting the limited EEG data typically available in the studied applications.
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Affiliation(s)
- Ahmed G Habashi
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt
| | - Ahmed M Azab
- Biomedical Engineering Department, Technical Research Center, Cairo, Egypt
| | - Seif Eldawlatly
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt.
- Computer Science and Engineering Department, The American University in Cairo, Cairo, Egypt.
| | - Gamal M Aly
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt
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Bianchi L, Ferrante R, Hu Y, Sahonero-Alvarez G, Zenia NZ. Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls. FRONTIERS IN NEUROERGONOMICS 2022; 3:1045653. [PMID: 38235475 PMCID: PMC10790887 DOI: 10.3389/fnrgo.2022.1045653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/24/2022] [Indexed: 01/19/2024]
Abstract
Background In the last decades, the P300 Speller paradigm was replicated in many experiments, and collected data were released to the public domain to allow research groups, particularly those in the field of machine learning, to test and improve their algorithms for higher performances of brain-computer interface (BCI) systems. Training data is needed to learn the identification of brain activity. The more training data are available, the better the algorithms will perform. The availability of larger datasets is highly desirable, eventually obtained by merging datasets from different repositories. The main obstacle to such merging is that all public datasets are released in various file formats because no standard way is established to share these data. Additionally, all datasets necessitate reading documents or scientific papers to retrieve relevant information, which prevents automating the processing. In this study, we thus adopted a unique file format to demonstrate the importance of having a standard and to propose which information should be stored and why. Methods We described our process to convert a dozen of P300 Speller datasets and reported the main encountered problems while converting them into the same file format. All the datasets are characterized by the same 6 × 6 matrix of alphanumeric symbols (characters and numbers or symbols) and by the same subset of acquired signals (8 EEG sensors at the same recording sites). Results and discussion Nearly a million stimuli were converted, relative to about 7000 spelled characters and belonging to 127 subjects. The converted stimuli represent the most extensively available platform for training and testing new algorithms on the specific paradigm - the P300 Speller. The platform could potentially allow exploring transfer learning procedures to reduce or eliminate the time needed for training a classifier to improve the performance and accuracy of such BCI systems.
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Affiliation(s)
- Luigi Bianchi
- Dipartimento di Ingegneria Civile ed Ingegneria Informatica, Tor Vergata University, Rome, Italy
| | - Raffaele Ferrante
- Dipartimento di Ingegneria Civile ed Ingegneria Informatica, Tor Vergata University, Rome, Italy
| | - Yaoping Hu
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada
| | - Guillermo Sahonero-Alvarez
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Nusrat Z. Zenia
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada
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Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units. Brain Sci 2022; 12:brainsci12111552. [DOI: 10.3390/brainsci12111552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 10/18/2022] [Accepted: 10/26/2022] [Indexed: 11/17/2022] Open
Abstract
The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain–computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.
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A Review of the Role of Machine Learning Techniques towards Brain–Computer Interface Applications. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2021. [DOI: 10.3390/make3040042] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
This review article provides a deep insight into the Brain–Computer Interface (BCI) and the application of Machine Learning (ML) technology in BCIs. It investigates the various types of research undertaken in this realm and discusses the role played by ML in performing different BCI tasks. It also reviews the ML methods used for mental state detection, mental task categorization, emotion classification, electroencephalogram (EEG) signal classification, event-related potential (ERP) signal classification, motor imagery categorization, and limb movement classification. This work explores the various methods employed in BCI mechanisms for feature extraction, selection, and classification and provides a comparative study of reviewed methods. This paper assists the readers to gain information regarding the developments made in BCI and ML domains and future improvements needed for improving and designing better BCI applications.
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Sahonero-Alvarez G, Singh AK, Sayrafian K, Bianchi L, Roman-Gonzalez A. A Functional BCI Model by the P2731 Working Group: Transducer. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1968633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
| | | | - Kamran Sayrafian
- Information Technology Laboratory, National Institute of Standards & Technology, Gaithersburg, USA
| | - Luigi Bianchi
- Civil Engineering and Computer Science Engineering Dept. Tor Vergata University of Rome, Rome, Italy
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Kumar S, Sharma R, Sharma A. OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals. PeerJ Comput Sci 2021; 7:e375. [PMID: 33817023 PMCID: PMC7959638 DOI: 10.7717/peerj-cs.375] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/06/2021] [Indexed: 06/12/2023]
Abstract
A human-computer interaction (HCI) system can be used to detect different categories of the brain wave signals that can be beneficial for neurorehabilitation, seizure detection and sleep stage classification. Research on developing HCI systems using brain wave signals has progressed a lot over the years. However, real-time implementation, computational complexity and accuracy are still a concern. In this work, we address the problem of selecting the appropriate filtering frequency band while also achieving a good system performance by proposing a frequency-based approach using long short-term memory network (LSTM) for recognizing different brain wave signals. Adaptive filtering using genetic algorithm is incorporated for a hybrid system utilizing common spatial pattern and LSTM network. The proposed method (OPTICAL+) achieved an overall average classification error rate of 30.41% and a kappa coefficient value of 0.398, outperforming the state-of-the-art methods. The proposed OPTICAL+ predictor can be used to develop improved HCI systems that will aid in neurorehabilitation and may also be beneficial for sleep stage classification and seizure detection.
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Affiliation(s)
- Shiu Kumar
- School of Electrical and Electronic Engineering, Fiji National University, Suva, Fiji
| | - Ronesh Sharma
- School of Electrical and Electronic Engineering, Fiji National University, Suva, Fiji
| | - Alok Sharma
- STEMP, University of the South Pacific, Suva, Fiji
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
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Effects of Visual Attention on Tactile P300 BCI. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:6549189. [PMID: 32148471 PMCID: PMC7049858 DOI: 10.1155/2020/6549189] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 01/17/2020] [Accepted: 02/01/2020] [Indexed: 11/29/2022]
Abstract
Objective. Tactile P300 brain-computer interfaces (BCIs) can be manipulated by users who only need to focus their attention on a single-target stimulus within a stream of tactile stimuli. To date, a multitude of tactile P300 BCIs have been proposed. In this study, our main purpose is to explore and investigate the effects of visual attention on a tactile P300 BCI. Approach. We designed a conventional tactile P300 BCI where vibration stimuli were provided by five stimulators and two of them were fixed on target locations on the participant's left and right wrists. Two conditions (one condition with visual attention and the other condition without visual attention) were tested by eleven healthy participants. Main Results. Our results showed that, when participants visually attended to the location of target stimulus, significantly higher classification accuracies and information transfer rates were obtained (both for p < 0.05). Furthermore, participants reported that visually attending to the stimulus made it easier to identify the target stimulus in random sequences of vibration stimuli. Significance. These findings suggest that visual attention has positive effects on both tactile P300 BCI performance and user-evaluation.
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Habelt B, Arvaneh M, Bernhardt N, Minev I. Biomarkers and neuromodulation techniques in substance use disorders. Bioelectron Med 2020; 6:4. [PMID: 32232112 PMCID: PMC7098236 DOI: 10.1186/s42234-020-0040-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 01/29/2020] [Indexed: 01/10/2023] Open
Abstract
Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders.
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Affiliation(s)
- Bettina Habelt
- Department of Psychiatry and Psychotherapy, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Mahnaz Arvaneh
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
| | - Nadine Bernhardt
- Department of Psychiatry and Psychotherapy, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ivan Minev
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
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