1
|
Lone AW, Aydin N. Wavelet Scattering Transform based Doppler signal classification. Comput Biol Med 2023; 167:107611. [PMID: 37913613 DOI: 10.1016/j.compbiomed.2023.107611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/07/2023] [Accepted: 09/29/2023] [Indexed: 11/03/2023]
Abstract
Normal blood supply to the human brain may be marred by the presence of a clot inside the blood vessels. This clot structure called emboli inhibits normal blood flow to the brain. It is considered as one of the main sources of stroke. Presence of emboli in human's can be determined by the analysis of transcranial Doppler signal. Different signal processing and machine learning algorithms have been used for classifying the detected signal as an emboli, Doppler speckle, and an artifact. In this paper, we sought to make use of the wavelet transform based algorithm called Wavelet Scattering Transform, which is translation invariant and stable to deformations for classifying different Doppler signals. With its architectural resemblance to Convolutional Neural Network, Wavelet Scattering Transform works well on small datasets and subsequently was trained on a dataset consisting of 300 Doppler signals. To check the effectiveness of extracted Scattering transform based features for Doppler signal classification, learning algorithms that included multi-class Support vector machine, k-nearest neighbor and Naive Bayes algorithms were trained. Comparative analysis was done with respect to the handcrafted Continuous wavelet transform features extracted from samples and Wavelet scattering with Support vector machine achieved an accuracy of 98.89%. Also, with set of extracted scattering coefficients, Gaussian process regression was performed and a regression model was trained on three different sets of scattering coefficients with zero order scattering coefficients providing least prediction loss of 34.95%.
Collapse
Affiliation(s)
- Ab Waheed Lone
- Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey.
| | - Nizamettin Aydin
- Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey.
| |
Collapse
|
2
|
Meng LB, Zou YF, Shan MJ, Zhang M, Qi RM, Yu ZM, Guo P, Zheng QW, Gong T. Computer-assisted prediction of atherosclerotic intimal thickness based on weight of adrenal gland, interleukin-6 concentration, and neural networks. J Int Med Res 2019; 48:300060519839625. [PMID: 31039661 PMCID: PMC7140207 DOI: 10.1177/0300060519839625] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Objective Methods Results Conclusions
Collapse
Affiliation(s)
- Ling-Bing Meng
- Neurology Department, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China.,*These authors contributed equally to this work
| | - Yang-Fan Zou
- Department of Neurosurgery, Chinese PLA General Hospital-Sixth Medical Center, Beijing, P.R. China.,*These authors contributed equally to this work
| | - Meng-Jie Shan
- MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China
| | - Meng Zhang
- School of Energy Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, P.R. China
| | - Ruo-Mei Qi
- MOH Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China
| | - Ze-Mou Yu
- Department of Neurology, Peking University First Hospital, Beijing, P. R. China
| | - Peng Guo
- Department of Orthopedics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, P.R. China
| | - Qian-Wei Zheng
- Neurology Department, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China
| | - Tao Gong
- Neurology Department, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China
| |
Collapse
|
3
|
Classification of sonar echo signals in their reduced sparse forms using complex-valued wavelet neural network. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3920-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
4
|
Seera M, Lim CP, Tan KS, Liew WS. Classification of transcranial Doppler signals using individual and ensemble recurrent neural networks. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.05.117] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
5
|
|
6
|
Peker M. A new approach for automatic sleep scoring: Combining Taguchi based complex-valued neural network and complex wavelet transform. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 129:203-216. [PMID: 26787511 DOI: 10.1016/j.cmpb.2016.01.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Revised: 12/04/2015] [Accepted: 01/06/2016] [Indexed: 06/05/2023]
Abstract
Automatic classification of sleep stages is one of the most important methods used for diagnostic procedures in psychiatry and neurology. This method, which has been developed by sleep specialists, is a time-consuming and difficult process. Generally, electroencephalogram (EEG) signals are used in sleep scoring. In this study, a new complex classifier-based approach is presented for automatic sleep scoring using EEG signals. In this context, complex-valued methods were utilized in the feature selection and classification stages. In the feature selection stage, features of EEG data were extracted with the help of a dual tree complex wavelet transform (DTCWT). In the next phase, five statistical features were obtained. These features are classified using complex-valued neural network (CVANN) algorithm. The Taguchi method was used in order to determine the effective parameter values in this CVANN. The aim was to develop a stable model involving parameter optimization. Different statistical parameters were utilized in the evaluation phase. Also, results were obtained in terms of two different sleep standards. In the study in which a 2nd level DTCWT and CVANN hybrid model was used, 93.84% accuracy rate was obtained according to the Rechtschaffen & Kales (R&K) standard, while a 95.42% accuracy rate was obtained according to the American Academy of Sleep Medicine (AASM) standard. Complex-valued classifiers were found to be promising in terms of the automatic sleep scoring and EEG data.
Collapse
Affiliation(s)
- Musa Peker
- Department of Information Systems Engineering, Faculty of Technology, Mugla Sitki Kocman University, 48000 Mugla, Turkey.
| |
Collapse
|
7
|
Gürüler H. A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method. Neural Comput Appl 2016. [DOI: 10.1007/s00521-015-2142-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
8
|
Peker M, Sen B, Delen D. A Novel Method for Automated Diagnosis of Epilepsy Using Complex-Valued Classifiers. IEEE J Biomed Health Inform 2015; 20:108-18. [PMID: 25576585 DOI: 10.1109/jbhi.2014.2387795] [Citation(s) in RCA: 138] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. To carry out this study, first the features of EEG data are extracted using a dual-tree complex wavelet transformation at different levels of granularity to obtain size reduction. In subsequent phases, five features (based on statistical measurements maximum value, minimum value, arithmetic mean, standard deviation, median value) are obtained by using the feature vectors, and are presented as the input dimension to the complex-valued neural networks. The evaluation of the proposed method is conducted using the k-fold cross-validation methodology, reporting on classification accuracy, sensitivity, and specificity. The proposed method is tested using a benchmark EEG dataset, and high accuracy rates were obtained. The stated results show that the proposed method can be used to design an accurate classification system for epilepsy diagnosis.
Collapse
|
9
|
Simões PW, Izumi NB, Casagrande RS, Venson R, Veronezi CD, Moretti GP, da Rocha EL, Cechinel C, Ceretta LB, Comunello E, Martins PJ, Casagrande RA, Snoeyer ML, Manenti SA. Classification of images acquired with colposcopy using artificial neural networks. Cancer Inform 2014; 13:119-24. [PMID: 25374454 PMCID: PMC4213185 DOI: 10.4137/cin.s17948] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2014] [Revised: 09/09/2014] [Accepted: 09/15/2014] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVE To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. PURPOSE Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. RESULTS After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. CONCLUSION Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.
Collapse
Affiliation(s)
- Priscyla W Simões
- Curso de Medicina, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
- Research Group of Tecnologia da Informação e Comunicação na Saúde, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
| | - Narjara B Izumi
- Curso de Medicina, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
- Research Group of Tecnologia da Informação e Comunicação na Saúde, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
| | - Ramon S Casagrande
- Research Group of Tecnologia da Informação e Comunicação na Saúde, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
| | - Ramon Venson
- Research Group of Tecnologia da Informação e Comunicação na Saúde, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
| | - Carlos D Veronezi
- Curso de Medicina, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
- Research Group of Tecnologia da Informação e Comunicação na Saúde, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
| | - Gustavo P Moretti
- Research Group of Tecnologia da Informação e Comunicação na Saúde, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
| | - Edroaldo L da Rocha
- Graduate Program in Materials Science and Engineering, Federal University of Santa Catarina, Florianópolis, Brazil
| | | | - Luciane B Ceretta
- Research Group of Gestão do Cuidado, Integralidade e Educação na Saúde, Laboratory of Direito Sanitário e Saúde Coletiva, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
| | - Eros Comunello
- INCoD – National Institute for Digital Convergence, Universidade Federal de Santa Catarina (UFSC), Florianópolis, Brazil
| | - Paulo J Martins
- Research Group of Tecnologia da Informação e Comunicação na Saúde, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
| | - Rogério A Casagrande
- Research Group of Tecnologia da Informação e Comunicação na Saúde, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
| | - Maria L Snoeyer
- Curso de Medicina, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
| | - Sandra A Manenti
- Curso de Medicina, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
- Research Group of Tecnologia da Informação e Comunicação na Saúde, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil
| |
Collapse
|
10
|
Seddik AF, Shawky DM. A low-cost screening method for the detection of the carotid artery diseases. Knowl Based Syst 2013. [DOI: 10.1016/j.knosys.2013.08.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
11
|
Choi S, Jiang Z. Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique. Comput Biol Med 2009; 40:8-20. [PMID: 19926081 DOI: 10.1016/j.compbiomed.2009.10.003] [Citation(s) in RCA: 96] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2008] [Revised: 07/04/2009] [Accepted: 10/07/2009] [Indexed: 11/29/2022]
Abstract
In this paper, a novel cardiac sound spectral analysis method using the normalized autoregressive power spectral density (NAR-PSD) curve with the support vector machine (SVM) technique is proposed for classifying the cardiac sound murmurs. The 489 cardiac sound signals with 196 normal and 293 abnormal sound cases acquired from six healthy volunteers and 34 patients were tested. Normal sound signals were recorded by our self-produced wireless electric stethoscope system where the subjects are selected who have no the history of other heart complications. Abnormal sound signals were grouped into six heart valvular disorders such as the atrial fibrillation, aortic insufficiency, aortic stenosis, mitral regurgitation, mitral stenosis and split sounds. These abnormal subjects were also not included other coexistent heart valvular disorder. Considering the morphological characteristics of the power spectral density of the heart sounds in frequency domain, we propose two important diagnostic features Fmax and Fwidth, which describe the maximum peak of NAR-PSD curve and the frequency width between the crossed points of NAR-PSD curve on a selected threshold value (THV), respectively. Furthermore, a two-dimensional representation on (Fmax, Fwidth) is introduced. The proposed cardiac sound spectral envelope curve method is validated by some case studies. Then, the SVM technique is employed as a classification tool to identify the cardiac sounds by the extracted diagnostic features. To detect abnormality of heart sound and to discriminate the heart murmurs, the multi-SVM classifiers composed of six SVM modules are considered and designed. A data set was used to validate the classification performances of each multi-SVM module. As a result, the accuracies of six SVM modules used for detection of abnormality and classification of six heart disorders showed 71-98.9% for THVs=10-90% and 81.2-99.6% for THVs=10-50% with respect to each of SVM modules. With the proposed cardiac sound spectral analysis method, the high classification performances were achieved by 99.9% specificity and 99.5% sensitivity in classifying normal and abnormal sounds (heart disorders). Consequently, the proposed method showed relatively very high classification efficiency if the SVM module is designed with considering THV values. And the proposed cardiac sound murmurs classification method with autoregressive spectral analysis and multi-SVM classifiers is validated for the classification of heart valvular disorders.
Collapse
Affiliation(s)
- Samjin Choi
- Department of Biomedical Engineering, College of Medicine, Kyung Hee University, Seoul, Republic of Korea.
| | | |
Collapse
|
12
|
Latifoğlu F, Kara S, Imal E. Comparison of short-time Fourier transform and Eigenvector MUSIC methods using discrete wavelet transform for diagnosis of atherosclerosis. J Med Syst 2009; 33:189-97. [PMID: 19408452 DOI: 10.1007/s10916-008-9179-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
In this paper, a more effective use of Doppler techniques is presented for the purpose of diagnosing atherosclerosis in its early stages using the carotid artery Doppler signals. The power spectral density (PSD) graphics are obtained by applying the short-time Fourier transform (STFT)-Welch and the Eigenvector MUSIC methods to the discrete wavelet transform (DWT) of Doppler signals. The PSDs for the fourth approximation component (A4) of both methods estimated that the patients with atherosclerosis in its early phase had lower maximum frequency components. On the other hand, the healthy subjects had higher maximum frequency components. The area under the curve (AUC), which belongs to the receiver operating characteristic (ROC) curve for the frequency level of the maximum PSDs of the A4 approximation obtained from the STFT modeling, is computed as 0.97. The AUC for the MUSIC modeling is computed as 0.996. The AUC belonging to the ROC curve for the higher maximum frequency component is computed as 0.87. The AUC belonging to the ROC curve for the test parameter of the frequency level of the maximum PSDs derived from the MUSIC modeling is determined to be 0.882. The results of this study clearly demonstrate that it is possible to distinguish between the healthy people and the patients with atherosclerosis by using the frequency level of the maximum PSDs for the A4 approximation. Furthermore, it is concluded that the power of Eigenvector-MUSIC method in terms of the resolution of the high frequencies is better than that of the STFT methods.
Collapse
Affiliation(s)
- Fatma Latifoğlu
- Department of Biomedical Engineering, Erciyes University, 38039 Kayseri, Turkey.
| | | | | |
Collapse
|
13
|
Özbay Y. A New Approach to Detection of ECG Arrhythmias: Complex Discrete Wavelet Transform Based Complex Valued Artificial Neural Network. J Med Syst 2008; 33:435-45. [DOI: 10.1007/s10916-008-9205-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
14
|
Ceylan M, Ceylan R, Özbay Y, Kara S. Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network. Artif Intell Med 2008; 44:65-76. [PMID: 18650074 DOI: 10.1016/j.artmed.2008.05.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2007] [Revised: 04/14/2008] [Accepted: 05/24/2008] [Indexed: 01/04/2023]
|