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Pattern Detection of Atherosclerosis from Carotid Artery Doppler Signals using Fuzzy Weighted Pre-Processing and Least Square Support Vector Machine (LSSVM)

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Abstract

Carotid Artery Doppler Signals were recorded from 114 subjects, 60 of whom had Atherosclerosis disease while the rest were healthy controls. Diagnosis of Atherosclerosis from Carotid Artery Doppler Signals was conducted using Fuzzy weighted pre-processing and Least Square Support Vector Machine (LSSVM). First, in order to determine the LSSVM inputs, spectral analysis of Carotid Artery Doppler Signals was performed via Autoregressive (AR) modeling. Then, fuzzy weighted pre-processing based is proposed expert system, applied to inputs obtained from spectral analysis of Carotid Artery Doppler Signals. LSSVM was used to detect Atherosclerosis from Carotid Artery Doppler Signals. All data set were obtained from Carotid Artery Doppler Signals of healthy subjects and subjects suffering from Atherosclerosis disease. The employed expert system has achieved 100% classification accuracy using a 10-fold Cross Validation (CV) method.

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Acknowledgment

This study is supported by the Scientific Research Projects of Selcuk University (project no. 05401069).

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Authors and Affiliations

  1. Department of Electrical & Electronics Engineering, Selcuk University, 42075, Konya, Turkey

    Kemal Polat & Salih Güneş

  2. Department of Electronics Engineering, Erciyes University, 38039, Kayseri, Turkey

    Sadık Kara & Fatma Latifoğlu

Authors
  1. Kemal Polat
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  2. Sadık Kara
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  3. Fatma Latifoğlu
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  4. Salih Güneş
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Correspondence to Sadık Kara.

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Polat, K., Kara, S., Latifoğlu, F. et al. Pattern Detection of Atherosclerosis from Carotid Artery Doppler Signals using Fuzzy Weighted Pre-Processing and Least Square Support Vector Machine (LSSVM). Ann Biomed Eng 35, 724–732 (2007). https://doi.org/10.1007/s10439-007-9289-7

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  • DOI: https://doi.org/10.1007/s10439-007-9289-7

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