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Aspiotis V, Miltiadous A, Kalafatakis K, Tzimourta KD, Giannakeas N, Tsipouras MG, Peschos D, Glavas E, Tzallas AT. Assessing Electroencephalography as a Stress Indicator: A VR High-Altitude Scenario Monitored through EEG and ECG. Sensors (Basel) 2022; 22:s22155792. [PMID: 35957348 PMCID: PMC9371026 DOI: 10.3390/s22155792] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/28/2022] [Accepted: 08/01/2022] [Indexed: 05/28/2023] [Imported: 08/29/2023]
Abstract
Over the last decade, virtual reality (VR) has become an increasingly accessible commodity. Head-mounted display (HMD) immersive technologies allow researchers to simulate experimental scenarios that would be unfeasible or risky in real life. An example is extreme heights exposure simulations, which can be utilized in research on stress system mobilization. Until recently, electroencephalography (EEG)-related research was focused on mental stress prompted by social or mathematical challenges, with only a few studies employing HMD VR techniques to induce stress. In this study, we combine a state-of-the-art EEG wearable device and an electrocardiography (ECG) sensor with a VR headset to provoke stress in a high-altitude scenarios while monitoring EEG and ECG biomarkers in real time. A robust pipeline for signal clearing is implemented to preprocess the noise-infiltrated (due to movement) EEG data. Statistical and correlation analysis is employed to explore the relationship between these biomarkers with stress. The participant pool is divided into two groups based on their heart rate increase, where statistically important EEG biomarker differences emerged between them. Finally, the occipital-region band power changes and occipital asymmetry alterations were found to be associated with height-related stress and brain activation in beta and gamma bands, which correlates with the results of the self-reported Perceived Stress Scale questionnaire.
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Affiliation(s)
- Vasileios Aspiotis
- Human Computer Interaction Laboratory (HCILab), Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece; (V.A.); (A.M.); (K.K.); (K.D.T.); (N.G.); (E.G.)
- Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece;
| | - Andreas Miltiadous
- Human Computer Interaction Laboratory (HCILab), Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece; (V.A.); (A.M.); (K.K.); (K.D.T.); (N.G.); (E.G.)
| | - Konstantinos Kalafatakis
- Human Computer Interaction Laboratory (HCILab), Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece; (V.A.); (A.M.); (K.K.); (K.D.T.); (N.G.); (E.G.)
- Institute of Health Science Education, Barts and the London School of Medicine & Dentistry, Queen Mary University of London (Malta Campus), VCT 2520 Victoria, Malta
| | - Katerina D. Tzimourta
- Human Computer Interaction Laboratory (HCILab), Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece; (V.A.); (A.M.); (K.K.); (K.D.T.); (N.G.); (E.G.)
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50100 Kozani, Greece;
| | - Nikolaos Giannakeas
- Human Computer Interaction Laboratory (HCILab), Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece; (V.A.); (A.M.); (K.K.); (K.D.T.); (N.G.); (E.G.)
| | - Markos G. Tsipouras
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50100 Kozani, Greece;
| | - Dimitrios Peschos
- Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece;
| | - Euripidis Glavas
- Human Computer Interaction Laboratory (HCILab), Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece; (V.A.); (A.M.); (K.K.); (K.D.T.); (N.G.); (E.G.)
| | - Alexandros T. Tzallas
- Human Computer Interaction Laboratory (HCILab), Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece; (V.A.); (A.M.); (K.K.); (K.D.T.); (N.G.); (E.G.)
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Miltiadous A, Tzimourta KD, Giannakeas N, Tsipouras MG, Afrantou T, Ioannidis P, Tzallas AT. Alzheimer's Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods. Diagnostics (Basel) 2021; 11:diagnostics11081437. [PMID: 34441371 PMCID: PMC8391578 DOI: 10.3390/diagnostics11081437] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/01/2021] [Accepted: 08/07/2021] [Indexed: 11/16/2022] [Imported: 08/29/2023] Open
Abstract
Dementia is the clinical syndrome characterized by progressive loss of cognitive and emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 50-70% of total dementia cases. Another dementia type is frontotemporal dementia (FTD), which is associated with circumscribed degeneration of the prefrontal and anterior temporal cortex and mainly affects personality and social skills. With the rapid advancement in electroencephalogram (EEG) sensors, the EEG has become a suitable, accurate, and highly sensitive biomarker for the identification of neuronal and cognitive dynamics in most cases of dementia, such as AD and FTD, through EEG signal analysis and processing techniques. In this study, six supervised machine-learning techniques were compared on categorizing processed EEG signals of AD and FTD cases, to provide an insight for future methods on early dementia diagnosis. K-fold cross validation and leave-one-patient-out cross validation were also compared as validation methods to evaluate their performance for this classification problem. The proposed methodology accuracy scores were 78.5% for AD detection with decision trees and 86.3% for FTD detection with random forests.
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Affiliation(s)
- Andreas Miltiadous
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47 100 Arta, Greece; (A.M.); (N.G.)
| | - Katerina D. Tzimourta
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50 100 Kozani, Greece; (K.D.T.); (M.G.T.)
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47 100 Arta, Greece; (A.M.); (N.G.)
| | - Markos G. Tsipouras
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50 100 Kozani, Greece; (K.D.T.); (M.G.T.)
| | - Theodora Afrantou
- 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, GR54636 Thessaloniki, Greece; (T.A.); (P.I.)
| | - Panagiotis Ioannidis
- 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, GR54636 Thessaloniki, Greece; (T.A.); (P.I.)
| | - Alexandros T. Tzallas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47 100 Arta, Greece; (A.M.); (N.G.)
- Correspondence:
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Violaris IG, Kalafatakis K, Zavala E, Tsoulos IG, Lampros T, Lightman SL, Tsipouras MG, Giannakeas N, Tzallas A, Russell GM. Modelling Hydrocortisone Pharmacokinetics on a Subcutaneous Pulsatile Infusion Replacement Strategy in Patients with Adrenocortical Insufficiency. Pharmaceutics 2021; 13:pharmaceutics13060769. [PMID: 34064165 PMCID: PMC8224376 DOI: 10.3390/pharmaceutics13060769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 11/16/2022] [Imported: 08/29/2023] Open
Abstract
In the context of glucocorticoid (GC) therapeutics, recent studies have utilised a subcutaneous hydrocortisone (HC) infusion pump programmed to deliver multiple HC pulses throughout the day, with the purpose of restoring normal circadian and ultradian GC rhythmicity. A key challenge for the advancement of novel HC replacement therapies is the calibration of infusion pumps against cortisol levels measured in blood. However, repeated blood sampling sessions are enormously labour-intensive for both examiners and examinees. These sessions also have a cost, are time consuming and are occasionally unfeasible. To address this, we developed a pharmacokinetic model approximating the values of plasma cortisol levels at any point of the day from a limited number of plasma cortisol measurements. The model was validated using the plasma cortisol profiles of 9 subjects with disrupted endogenous GC synthetic capacity. The model accurately predicted plasma cortisol levels (mean absolute percentage error of 14%) when only four plasma cortisol measurements were provided. Although our model did not predict GC dynamics when HC was administered in a way other than subcutaneously or in individuals whose endogenous capacity to produce GCs is intact, it was found to successfully be used to support clinical trials (or practice) involving subcutaneous HC delivery in patients with reduced endogenous capacity to synthesize GCs.
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Affiliation(s)
- Ioannis G. Violaris
- Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece; (I.G.V.); (M.G.T.)
| | - Konstantinos Kalafatakis
- Laboratories of Integrative Neuroscience and Endocrinology, School of Clinical Sciences, University of Bristol, Bristol BS1 3NY, UK; (S.L.L.); (G.M.R.)
- Department of Informatics & Telecommunications, School of Informatics & Telecommunications, University of Ioannina, 47100 Arta, Greece; (I.G.T.); (T.L.); (N.G.); (A.T.)
- Correspondence: or ; Tel.: +30-2107288264
| | - Eder Zavala
- Centre for Systems Modelling and Quantitative Biomedicine, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Ioannis G. Tsoulos
- Department of Informatics & Telecommunications, School of Informatics & Telecommunications, University of Ioannina, 47100 Arta, Greece; (I.G.T.); (T.L.); (N.G.); (A.T.)
| | - Theodoros Lampros
- Department of Informatics & Telecommunications, School of Informatics & Telecommunications, University of Ioannina, 47100 Arta, Greece; (I.G.T.); (T.L.); (N.G.); (A.T.)
| | - Stafford L. Lightman
- Laboratories of Integrative Neuroscience and Endocrinology, School of Clinical Sciences, University of Bristol, Bristol BS1 3NY, UK; (S.L.L.); (G.M.R.)
| | - Markos G. Tsipouras
- Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece; (I.G.V.); (M.G.T.)
| | - Nikolaos Giannakeas
- Department of Informatics & Telecommunications, School of Informatics & Telecommunications, University of Ioannina, 47100 Arta, Greece; (I.G.T.); (T.L.); (N.G.); (A.T.)
| | - Alexandros Tzallas
- Department of Informatics & Telecommunications, School of Informatics & Telecommunications, University of Ioannina, 47100 Arta, Greece; (I.G.T.); (T.L.); (N.G.); (A.T.)
| | - Georgina M. Russell
- Laboratories of Integrative Neuroscience and Endocrinology, School of Clinical Sciences, University of Bristol, Bristol BS1 3NY, UK; (S.L.L.); (G.M.R.)
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Antoniou E, Bozios P, Christou V, Tzimourta KD, Kalafatakis K, G Tsipouras M, Giannakeas N, Tzallas AT. EEG-Based Eye Movement Recognition Using Brain-Computer Interface and Random Forests. Sensors (Basel) 2021; 21:2339. [PMID: 33801663 DOI: 10.3390/s21072339] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 03/22/2021] [Accepted: 03/25/2021] [Indexed: 11/24/2022] [Imported: 08/29/2023]
Abstract
Discrimination of eye movements and visual states is a flourishing field of research and there is an urgent need for non-manual EEG-based wheelchair control and navigation systems. This paper presents a novel system that utilizes a brain–computer interface (BCI) to capture electroencephalographic (EEG) signals from human subjects while eye movement and subsequently classify them into six categories by applying a random forests (RF) classification algorithm. RF is an ensemble learning method that constructs a series of decision trees where each tree gives a class prediction, and the class with the highest number of class predictions becomes the model’s prediction. The categories of the proposed random forests brain–computer interface (RF-BCI) are defined according to the position of the subject’s eyes: open, closed, left, right, up, and down. The purpose of RF-BCI is to be utilized as an EEG-based control system for driving an electromechanical wheelchair (rehabilitation device). The proposed approach has been tested using a dataset containing 219 records taken from 10 different patients. The BCI implemented the EPOC Flex head cap system, which includes 32 saline felt sensors for capturing the subjects’ EEG signals. Each sensor caught four different brain waves (delta, theta, alpha, and beta) per second. Then, these signals were split in 4-second windows resulting in 512 samples per record and the band energy was extracted for each EEG rhythm. The proposed system was compared with naïve Bayes, Bayes Network, k-nearest neighbors (K-NN), multilayer perceptron (MLP), support vector machine (SVM), J48-C4.5 decision tree, and Bagging classification algorithms. The experimental results showed that the RF algorithm outperformed compared to the other approaches and high levels of accuracy (85.39%) for a 6-class classification are obtained. This method exploits high spatial information acquired from the Emotiv EPOC Flex wearable EEG recording device and examines successfully the potential of this device to be used for BCI wheelchair technology.
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Tzimourta KD, Christou V, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Tsalikakis D, Tsipouras MG. Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review. Int J Neural Syst 2021; 31:2130002. [PMID: 33588710 DOI: 10.1142/s0129065721300023] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] [Imported: 08/29/2023]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.
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Affiliation(s)
- Katerina D Tzimourta
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GR50100, Greece.,Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Vasileios Christou
- Q Base R&D, Science & Technology Park of Epirus, University of Ioannina Campus, Ioannina GR45110, Greece.,Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Alexandros T Tzallas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Loukas G Astrakas
- Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Pantelis Angelidis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Dimitrios Tsalikakis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Markos G Tsipouras
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
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Koritsoglou K, Christou V, Ntritsos G, Tsoumanis G, Tsipouras MG, Giannakeas N, Tzallas AT. Improving the Accuracy of Low-Cost Sensor Measurements for Freezer Automation. Sensors (Basel) 2020; 20:s20216389. [PMID: 33182354 PMCID: PMC7664904 DOI: 10.3390/s20216389] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 11/02/2020] [Accepted: 11/04/2020] [Indexed: 11/16/2022] [Imported: 08/29/2023]
Abstract
In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).
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Affiliation(s)
- Kyriakos Koritsoglou
- Department of Informatics and Telecommunications, University of Ioannina, GR-47100 Arta, Greece; (K.K.); (V.C.); (G.N.); (G.T.); (N.G.)
| | - Vasileios Christou
- Department of Informatics and Telecommunications, University of Ioannina, GR-47100 Arta, Greece; (K.K.); (V.C.); (G.N.); (G.T.); (N.G.)
- Q Base R&D, Science & Technology Park of Epirus, University of Ioannina Campus, GR45110 Ioannina, Greece
| | - Georgios Ntritsos
- Department of Informatics and Telecommunications, University of Ioannina, GR-47100 Arta, Greece; (K.K.); (V.C.); (G.N.); (G.T.); (N.G.)
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, GR-45110 Ioannina, Greece
| | - Georgios Tsoumanis
- Department of Informatics and Telecommunications, University of Ioannina, GR-47100 Arta, Greece; (K.K.); (V.C.); (G.N.); (G.T.); (N.G.)
| | - Markos G. Tsipouras
- Department of Electrical and Computer Engineering, University of Western Macedonia, GR-50100 Kozani, Greece;
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, University of Ioannina, GR-47100 Arta, Greece; (K.K.); (V.C.); (G.N.); (G.T.); (N.G.)
| | - Alexandros T. Tzallas
- Department of Informatics and Telecommunications, University of Ioannina, GR-47100 Arta, Greece; (K.K.); (V.C.); (G.N.); (G.T.); (N.G.)
- Correspondence:
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Christou V, Tsipouras MG, Giannakeas N, Tzallas AT, Brown G. Hybrid extreme learning machine approach for heterogeneous neural networks. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.04.092] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] [Imported: 08/29/2023]
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Christou V, Tsipouras MG, Giannakeas N, Tzallas AT. Hybrid extreme learning machine approach for homogeneous neural networks. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.05.064] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] [Imported: 08/29/2023]
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Giannakeas N, Tsipouras MG, Tzallas AT, Vavva MG, Tsimplakidou M, Karvounis EC, Forlano R, Manousou P. Measuring Steatosis in Liver Biopsies Using Machine Learning and Morphological Imaging. 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) 2017. [DOI: 10.1109/cbms.2017.98] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023] [Imported: 08/29/2023]
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Tsipouras MG, Giannakeas N, Tzallas AT, Tsianou ZE, Manousou P, Hall A, Tsoulos I, Tsianos E. A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques. Comput Methods Programs Biomed 2017; 140:61-68. [PMID: 28254091 DOI: 10.1016/j.cmpb.2016.11.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2016] [Revised: 11/12/2016] [Accepted: 11/22/2016] [Indexed: 06/06/2023] [Imported: 08/29/2023]
Abstract
BACKGROUND AND OBJECTIVE Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. METHODS The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. RESULTS For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. CONCLUSIONS The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time.
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Affiliation(s)
- Markos G Tsipouras
- Division of Gastroenterology, Faculty of Medicine, School of Health Sciences, University of Ioannina, GR45110 Ioannina, Greece; Department of Computer Engineering, School of Applied Technology, Technological Educational Institute of Epirus, Kostakioi, GR47100, Arta, Greece.
| | - Nikolaos Giannakeas
- Division of Gastroenterology, Faculty of Medicine, School of Health Sciences, University of Ioannina, GR45110 Ioannina, Greece; Department of Computer Engineering, School of Applied Technology, Technological Educational Institute of Epirus, Kostakioi, GR47100, Arta, Greece.
| | - Alexandros T Tzallas
- Division of Gastroenterology, Faculty of Medicine, School of Health Sciences, University of Ioannina, GR45110 Ioannina, Greece; Department of Computer Engineering, School of Applied Technology, Technological Educational Institute of Epirus, Kostakioi, GR47100, Arta, Greece.
| | - Zoe E Tsianou
- Division of Gastroenterology, Faculty of Medicine, School of Health Sciences, University of Ioannina, GR45110 Ioannina, Greece.
| | - Pinelopi Manousou
- Liver Unit, St Mary's Hospital, Imperial College NHS Trust, London, UK.
| | - Andrew Hall
- Department of Histopathology, UCL Medical School, Royal Free Campus, Rowland Hill Street, London NW3 2QG, UK.
| | - Ioannis Tsoulos
- Department of Computer Engineering, School of Applied Technology, Technological Educational Institute of Epirus, Kostakioi, GR47100, Arta, Greece.
| | - Epameinondas Tsianos
- Division of Gastroenterology, Faculty of Medicine, School of Health Sciences, University of Ioannina, GR45110 Ioannina, Greece.
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