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Fujita Y, Yanagisawa T, Fukuma R, Ura N, Oshino S, Kishima H. Abnormal phase-amplitude coupling characterizes the interictal state in epilepsy. J Neural Eng 2022; 19. [PMID: 35385832 DOI: 10.1088/1741-2552/ac64c4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 04/05/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Diagnosing epilepsy still requires visual interpretation of electroencephalography and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from electroencephalography and MEG, such as relative power (Power) and functional connectivity. However, the usefulness of interictal phase-amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesized that resting-state PAC would be different for patients with epilepsy in the interictal state and for healthy participants such that it would improve discrimination between the groups. METHODS We obtained resting-state MEG and magnetic resonance imaging in 90 patients with epilepsy during their preoperative evaluation and in 90 healthy participants. We used the cortical currents estimated from MEG and magnetic resonance imaging to calculate Power in the δ (1-3 Hz), θ (4-7 Hz), α (8-13 Hz), β (13-30 Hz), low γ (35-55 Hz), and high γ (65-90 Hz) bands and functional connectivity in the θ band. PAC was evaluated using the synchronization index (SI) for eight frequency band pairs: the phases of δ, θ, α, and β and the amplitudes of low and high γ. First, we compared the mean SI values for the patients with epilepsy and the healthy participants. Then, using features such as PAC, Power, functional connectivity, and features extracted by deep learning individually or combined, we tested whether PAC improves discrimination accuracy for the two groups. RESULTS The mean SI values were significantly different for the patients with epilepsy and the healthy participants. The SI value difference was highest for θ/low γ in the temporal lobe. Discrimination accuracy was the highest, at 90%, using the combination of PAC and deep learning. SIGNIFICANCE Abnormal PAC characterized the patients with epilepsy in the interictal state compared with the healthy participants, potentially improving the discrimination of epilepsy.
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Affiliation(s)
- Yuya Fujita
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Takufumi Yanagisawa
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Ryohei Fukuma
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Natsuko Ura
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Satoru Oshino
- Department of Neurosurgery, Osaka University Faculty of Medicine Graduate School of Medicine, 2-2 Yamadaoka, suita, Osaka, Japan, Osaka University Graduate School of Medicine, Dept of Neurosurgery, Osaka, Osaka, 5670871, JAPAN
| | - Haruhiko Kishima
- Department of neurosurgery, Osaka University, 2-2, Yamadaoka, Suita, Suita, Osaka, 5650871, JAPAN
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Dong A, Li Z, Zheng Q. Transferred Subspace Learning Based on Non-negative Matrix Factorization for EEG Signal Classification. Front Neurosci 2021; 15:647393. [PMID: 33841089 PMCID: PMC8024531 DOI: 10.3389/fnins.2021.647393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 02/22/2021] [Indexed: 11/13/2022] Open
Abstract
EEG signal classification has been a research hotspot recently. The combination of EEG signal classification with machine learning technology is very popular. Traditional machine leaning methods for EEG signal classification assume that the EEG signals are drawn from the same distribution. However, the assumption is not always satisfied with the practical applications. In practical applications, the training dataset and the testing dataset are from different but related domains. How to make best use of the training dataset knowledge to improve the testing dataset is critical for these circumstances. In this paper, a novel method combining the non-negative matrix factorization technology and the transfer learning (NMF-TL) is proposed for EEG signal classification. Specifically, the shared subspace is extracted from the testing dataset and training dataset using non-negative matrix factorization firstly and then the shared subspace and the original feature space are combined to obtain the final EEG signal classification results. On the one hand, the non-negative matrix factorization can assure to obtain essential information between the testing and the training dataset; on the other hand, the combination of shared subspace and the original feature space can fully use all the signals including the testing and the training dataset. Extensive experiments on Bonn EEG confirmed the effectiveness of the proposed method.
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Affiliation(s)
- Aimei Dong
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Science), Jinan, China
| | - Zhigang Li
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Science), Jinan, China
| | - Qiuyu Zheng
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Science), Jinan, China
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Ashraf H, Waris A, Gilani SO, Kashif AS, Jamil M, Jochumsen M, Niazi IK. Evaluation of windowing techniques for intramuscular EMG-based diagnostic, rehabilitative and assistive devices. J Neural Eng 2021; 18. [PMID: 33217750 DOI: 10.1088/1741-2552/abcc7f] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 11/20/2020] [Indexed: 11/12/2022]
Abstract
Objective.Intramuscular electromyography (iEMG) signals, invasively recorded, directly from the muscles are used to diagnose various neuromuscular disorders/diseases and to control rehabilitative and assistive robotic devices. iEMG signals are potentially being used in neurology, kinesiology, rehabilitation and ergonomics, to detect/diagnose various diseases/disorders. Electromyography-based classification and analysis systems are being designed and tested for the classification of various neuromuscular disorders and to control rehabilitative and assistive robotic devices. Many studies have explored parameters such as the pre-processing, feature extraction and selection of classifiers that can affect the performance and efficacy of iEMG-based classification systems. The pre-processing stage includes the removal of any unwanted noise from the original signal and windowing of the signal.Approach.This study investigated and presented the optimum windowing configurations for robust control and better performance results of an iEMG-based analysis system based on the stationarity rate (SR) and classification accuracy. Both disjoint and overlap, windowing techniques with varying window and overlap sizes have been investigated using a machine learning-based classification algorithm called linear discriminant analysis.Main results.The optimum window size ranges are from 200-300 ms for the disjoint and 225-300 ms for the overlap windowing technique, respectively. The inferred results show that for the overlap windowing technique the optimum range of overlap size is from 10%-30% of the length of window size. The mean classification accuracy (MCA) and mean stationarity rate (MSR) were found to be lower in the disjoint windowing technique compared to overlap windowing at all investigated overlap sizes. Statistical analysis (two-way analysis of variance test) showed that the MSR and MCA of the overlap windowing technique was significantly different at overlap sizes of 10%-30% (p-values < 0.05).Significance.The presented results can be used to achieve the best possible classification results and SR for any iEMG-based real-time diagnosis, detection and control system, which can enhance the performance of the system significantly.
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Affiliation(s)
- Hassan Ashraf
- Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), 44000 Islamabad, Pakistan
| | - Asim Waris
- Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), 44000 Islamabad, Pakistan
| | - Syed Omer Gilani
- Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), 44000 Islamabad, Pakistan
| | - Amer Sohail Kashif
- Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), 44000 Islamabad, Pakistan
| | - Mohsin Jamil
- Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), 44000 Islamabad, Pakistan.,Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, 240 Prince Phillip Drive, St John's NL A1B 3X5, Canada
| | - Mads Jochumsen
- Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark
| | - Imran Khan Niazi
- Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark.,Center of Chiropractic Research, New Zealand College of Chiropractic, 1149 Auckland, New Zealand.,Faculty of Health and Environmental Sciences, Health and Rehabilitation Research Institute, AUT University, Auckland 0627, New Zealand
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Nair P, Aghoram R, Khilari M. Applications of artificial intelligence in epilepsy. INTERNATIONAL JOURNAL OF ADVANCED MEDICAL AND HEALTH RESEARCH 2021. [DOI: 10.4103/ijamr.ijamr_94_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:5046315. [PMID: 32831900 PMCID: PMC7422481 DOI: 10.1155/2020/5046315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Accepted: 07/15/2020] [Indexed: 11/18/2022]
Abstract
Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, causing transient brain dysfunction. The seizures of epilepsy have the characteristics of being sudden and repetitive, which has seriously endangered patients' health, cognition, etc. In the current condition, EEG plays a vital role in the diagnosis, judgment, and qualitative location of epilepsy among the clinical diagnosis of various epileptic seizures and is an indispensable means of detection. The study of the EEG signals of patients with epilepsy can provide a strong basis and useful information for in-depth understanding of its pathogenesis. Although, intelligent classification technologies based on machine learning have been widely used to the classification of epilepsy EEG signals and show the effectiveness. In fact, it is difficult to ensure that there is always enough EEG data available for training the model in real life, which will affect the performance of the algorithms. In view of this, to reduce the impact of insufficient data on the detection performance of the algorithms, a novel discriminate least squares regression- (DLSR-) based inductive transfer learning method was introduced which is on the basis of DLSR and the inductive transfer learning. And, it is applied to promote the adaptability and accuracy of the epilepsy EEG signal recognition. The proposed method inherits the advantages of DLSR; it can be more suitable for classification scenarios by expanding the interval between different classes. Meanwhile, it can simultaneously use the data of the target domain and the knowledge of the source domain, which is helpful for getting better performance. The results show that the improved method has more advantages in EEG signal recognition comparing to several other representative methods.
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Krzemiński D, Masuda N, Hamandi K, Singh KD, Routley B, Zhang J. Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy. Netw Neurosci 2020; 4:374-396. [PMID: 32537532 PMCID: PMC7286306 DOI: 10.1162/netn_a_00125] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 01/07/2020] [Indexed: 12/13/2022] Open
Abstract
Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME patients and controls by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the front-oparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta, and gamma bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multistability in selective functional networks and frequency bands in the fronto-parietal cortices.
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Affiliation(s)
- Dominik Krzemiński
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Bethany Routley
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
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Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia 2019; 60:2037-2047. [PMID: 31478577 PMCID: PMC9897263 DOI: 10.1111/epi.16333] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 07/25/2019] [Accepted: 08/12/2019] [Indexed: 02/05/2023]
Abstract
Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre-surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.
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Affiliation(s)
- Bardia Abbasi
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA 02215
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Artuğ NT, Goker I, Bolat B, Osman O, Orhan EK, Baslo MB. New features for scanned bioelectrical activity of motor unit in health and disease. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.11.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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9
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An Imbalanced Learning based MDR-TB Early Warning System. J Med Syst 2016; 40:164. [DOI: 10.1007/s10916-016-0517-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 05/03/2016] [Indexed: 12/29/2022]
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10
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A Comparison of Classification Methods for Telediagnosis of Parkinson’s Disease. ENTROPY 2016. [DOI: 10.3390/e18040115] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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11
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A Prognosis Tool Based on Fuzzy Anthropometric and Questionnaire Data for Obstructive Sleep Apnea Severity. J Med Syst 2016; 40:110. [PMID: 26932370 DOI: 10.1007/s10916-016-0464-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Accepted: 02/04/2016] [Indexed: 01/16/2023]
Abstract
Obstructive sleep apnea (OSA) are linked to the augmented risk of morbidity and mortality. Although polysomnography is considered a well-established method for diagnosing OSA, it suffers the weakness of time consuming and labor intensive, and requires doctors and attending personnel to conduct an overnight evaluation in sleep laboratories with dedicated systems. This study aims at proposing an efficient diagnosis approach for OSA on the basis of anthropometric and questionnaire data. The proposed approach integrates fuzzy set theory and decision tree to predict OSA patterns. A total of 3343 subjects who were referred for clinical suspicion of OSA (eventually 2869 confirmed with OSA and 474 otherwise) were collected, and then classified by the degree of severity. According to an assessment of experiment results on g-means, our proposed method outperforms other methods such as linear regression, decision tree, back propagation neural network, support vector machine, and learning vector quantization. The proposed method is highly viable and capable of detecting the severity of OSA. It can assist doctors in pre-diagnosis of OSA before running the formal PSG test, thereby enabling the more effective use of medical resources.
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Bielza C, Larrañaga P. Bayesian networks in neuroscience: a survey. Front Comput Neurosci 2014; 8:131. [PMID: 25360109 PMCID: PMC4199264 DOI: 10.3389/fncom.2014.00131] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Accepted: 09/26/2014] [Indexed: 12/29/2022] Open
Abstract
Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.
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Affiliation(s)
- Concha Bielza
- *Correspondence: Concha Bielza, Departamento de Inteligencia Artificial, Universidad Politecnica de Madrid, Campus de Montegancedo, Boadilla del Monte, 28660 Madrid, Spain e-mail:
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Korkmaz SA, Poyraz M. A new method based for diagnosis of breast cancer cells from microscopic images: DWEE--JHT. J Med Syst 2014; 38:92. [PMID: 25023651 DOI: 10.1007/s10916-014-0092-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Accepted: 06/04/2014] [Indexed: 10/25/2022]
Abstract
In these days, there are many various diseases, whose diagnosis is very hardly. Breast cancer is one of these type diseases. In this paper, accuracy diagnosis of normal, benign, and malign breast cancer cell were found by combining mean success rates Jensen Shannon, Hellinger, and Triangle measure which connected with each other. In this article, an diagnostic method based on feature extraction Discrete Wavelet Entropy Energy (DWEE) and Jensen Shannon, Hellinger, Triangle Measure (JHT) Classifier for diagnosis of breast cancer. This diagnosis method is called as DWEE--JHT this paper. With this diagnosis method have found optimal feature subset using discrete wavelet transform feature extraction. Then these convenient features are given to Jensen Shannon, Hellinger, Triangle Measure (JHT) classifier. Then, between classifiers which are Jensen Shannon, Hellinger, and triangle distance have been validated the measures via relationships. Afterwards, breast cancer cells are classified using Jensen Shannon, Hellinger, and Triangle distance. Mean success rate of 16 feature vector with Jensen Shannon classifier is found % 97.81. Mean success rate of 16 feature vector with Hellinger classifier is found % 97.75. Mean success rate of 16 feature vector with Triangle classifier is found % 97.87. By averaging of results obtained from these 3 classifiers are found as 97.81 % average of accuracy diagnosis.
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Affiliation(s)
- S Aytac Korkmaz
- Department of Electrical-Electronic Engineering, Engineering Faculty, Firat University, 23100, Elazığ, Turkey,
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Avci D, Leblebicioglu MK, Poyraz M, Dogantekin E. A New Method Based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for Recognition of Urine Cells from Microscopic Images Independent of Rotation and Scaling. J Med Syst 2014; 38:7. [PMID: 24493072 DOI: 10.1007/s10916-014-0007-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2013] [Accepted: 01/02/2014] [Indexed: 12/29/2022]
Affiliation(s)
- Derya Avci
- Engineering Faculty, Department of Electrical-Electronic Engineering, Firat University, 23119, Elazig, Turkey,
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