Detection of femoral artery occlusion from spectral density of Doppler signals using the artificial neural network
This research is concentrated on the diagnosis of occlusion disease through the analysis of femoral artery Doppler signals with the help of Artificial Neural Network (ANN). Doppler femoral artery signals belong to occlusion patient and healthy subjects were recorded. Afterwards, power spectral densi...
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| Published in | Expert systems with applications Vol. 29; no. 4; pp. 945 - 952 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.11.2005
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2005.06.010 |
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| Abstract | This research is concentrated on the diagnosis of occlusion disease through the analysis of femoral artery Doppler signals with the help of Artificial Neural Network (ANN). Doppler femoral artery signals belong to occlusion patient and healthy subjects were recorded. Afterwards, power spectral densities (PSD) of these signals were obtained using Welch method and Autoregressive (AR) modeling. Multilayer feed forward ANN trained with a Levenberg Marquart (LM) backpropagation algorithm was implemented to these PSD. The designed classification structure has about 98% sensitivity, 97–100% specifity and correct classification is calculated to be 98–99% (for AR modeling and Welch method respectively). The end results are classified as healthy and diseased. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation. |
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| AbstractList | This research is concentrated on the diagnosis of occlusion disease through the analysis of femoral artery Doppler signals with the help of Artificial Neural Network (ANN). Doppler femoral artery signals belong to occlusion patient and healthy subjects were recorded. Afterwards, power spectral densities (PSD) of these signals were obtained using Welch method and Autoregressive (AR) modeling. Multilayer feed forward ANN trained with a Levenberg Marquart (LM) backpropagation algorithm was implemented to these PSD. The designed classification structure has about 98% sensitivity, 97-100% specifity and correct classification is calculated to be 98-99% (for AR modeling and Welch method respectively). The end results are classified as healthy and diseased. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation. |
| Author | Güven, Ayşegül Kemaloğlu, Semra Kara, Sadik |
| Author_xml | – sequence: 1 givenname: Sadik surname: Kara fullname: Kara, Sadik email: kara@erciyes.edu.tr organization: Erciyes Univ., Department of Electronics Engineering, 38039, Kayseri, Turkey – sequence: 2 givenname: Semra surname: Kemaloğlu fullname: Kemaloğlu, Semra organization: Erciyes Univ., Department of Biomedical Devices Technology, 38039, Kayseri, Turkey – sequence: 3 givenname: Ayşegül surname: Güven fullname: Güven, Ayşegül organization: Erciyes Univ., Civil Aviation School, Department of Electric-Electronics, 38039, Kayseri, Turkey |
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| CitedBy_id | crossref_primary_10_1016_j_neucom_2016_05_117 crossref_primary_10_1016_j_compbiomed_2009_10_003 crossref_primary_10_1007_s00521_012_1220_y crossref_primary_10_1007_s10916_008_9142_z crossref_primary_10_1016_j_eswa_2011_02_025 |
| Cites_doi | 10.1016/S0301-5629(99)00015-0 10.1016/S0301-5629(97)00264-0 10.1016/0010-4825(95)00024-X 10.1002/bjs.1800710114 10.1016/S0010-4825(03)00060-X 10.1148/radiology.192.3.7914706 10.1016/1350-4533(95)95713-K 10.1007/BF02414891 10.1016/0169-2607(95)01702-X 10.1067/mva.2001.118581 10.1007/BF02442176 10.1016/S0301-5629(97)00011-2 10.1002/bjs.1800820923 10.1007/BF02915843 10.1142/S0218488595000219 10.1016/0301-5629(90)90089-U 10.1109/10.284961 10.1109/TAC.1974.1100705 10.1016/1350-4533(95)00032-1 10.1006/cbmr.1996.0022 10.1016/S0010-4825(03)00011-8 10.1001/archsurg.1980.01380110054008 10.1016/S0933-3657(99)00040-8 10.1177/016173460202400201 10.1109/PROC.1981.12184 |
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| Keywords | Femoral artery occlusion Autoregressive Welch Artificial neural network Back propagation algorithm |
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| SubjectTerms | Artificial neural network Autoregressive Back propagation algorithm Femoral artery occlusion Welch |
| Title | Detection of femoral artery occlusion from spectral density of Doppler signals using the artificial neural network |
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