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Currently, the one-dimensional signal processing method for heart-sound analysis and recognition is the mainstream in researches. In order to gain more intuitive features in manifestation, to improve the effect of classification, and to endarge the heart-sound recognition field, this paper puts forward a heart-sound texture feature extraction and recognition algoriithm, which is based on heart-sound window function and the combination of heart-sound and image processing technology. Firstly, we give a heart-sound model, a definition of heart-sound time-frequency diagram, and a heart-sound texture map; we also discuss how to utilize heart-sound window function and short-time Fourier transform to obtain a two-dimensional heart-sound time-frequency diagram. After that, in the light of the characteristics of heart-sound, we mainly study the structure principle and implementation method of a heart-sound window function Finally, the heart-sound texture feature extraction and identification are realized by the improved pulse-coupled neural network model (IPCNN). Simulation experiments show that compared with the traditional window function, the heart-sound time-frequency diagram obtained using heart-sound window function has a clearer and noise well suppressed texture. Furthermore, compared with other three kinds of typical recognition methods, IPCNN has the lower computational cost and higher recognition rate. So, we can arrive at the conclusion that the method for heart-sound feature extraction and recognition based on image processing techniques is the effective one.
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Keywords:
- heart sounds time-frequency diagram /
- heart-sounds texture map /
- heart-sounds window function /
- improved pulse coupled neural network
[1] Cheng X F, Ma Y, Liu C, Zhang X J, Guo Y F 2012 Sci. China Inf. Sci. 55 281
[2] Phau K, Chen J F, Dat T H, Shue L 2008 Pattern Recognition 41 906
[3] Liu X Y, Pei L Q, Wang Y 2011 Chin. Phys. B 20 047401
[4] Schwerin B, Paliwal K 2014 Speech Communication 58 49
[5] Guo X, Ding X, Lei M 2012 Acta Phys. Hungar. 99 382
[6] Cheng X F, Zhang Z 2013 Acta Phys. Sin. 62 168701 (in Chinese) [成谢锋, 张正 2013 物理学报 62 168701]
[7] Fan J, Lv C, Zhang H 2008 J. Vibrat. Engin. 21 381 (in Chinese) [樊剑, 吕超, 张辉 2008 振动工程学报 21 381]
[8] Ma Y, Cheng X F 2014 Acta Phys. Sin. 63 068703 (in Chinese) [马勇, 成谢锋 2014 物理学报 63 068703]
[9] Liao C J, Li X J, Liu D S 2008 Chin. J. Sci. Instrum. 29 1862 (in Chinese) [廖传军, 李学军, 刘德顺 2008 仪器仪表学报 29 1862]
[10] Ma Y D, Yuan M, Qi C L, Liu Y, Liu Y J 2005 Comp. Engin. Appl. 41 81 (in Chinese) [马义德, 袁敏, 齐春亮, 刘悦, 刘映杰 2005 计算机工程与应用 41 81]
[11] Liu K, Jin W B 2008 Chong Qing Univ. Posts and Telecommun. 20 217 (in Chinese) [刘琨, 金文标 2008 重庆邮电大学学报 20 217]
[12] Wei L X, Zhang M, Zhong Y C, Han G 2012 Comp. Engin. Appl. 48 133 (in Chinese) [韦丽兴, 张淼, 钟映春, 韩光 2012 计算机工程与应用 48 133]
[13] Lindbad T, Kinser J M (translated by Ma Y D, Zhang K, Wang Z B) 2008 Pulse Coupled Neural Network Image Processing (Beijing: Higher Education Press) pp10-29 (in Chinese) [林德布莱德T, 凯泽J M 著(马义德, 绽琨, 王兆滨译) 2008脉冲耦合神经网络图像处理(高等教育出版社)第10–29页]
[14] Cheng X F, Ma Y, Tao Y W 2010 Chin. J. Sci. Instrum. 8 1712 (in Chinese) [成谢锋, 马勇, 陶冶薇 2010 仪器仪表学报 8 1712]
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[1] Cheng X F, Ma Y, Liu C, Zhang X J, Guo Y F 2012 Sci. China Inf. Sci. 55 281
[2] Phau K, Chen J F, Dat T H, Shue L 2008 Pattern Recognition 41 906
[3] Liu X Y, Pei L Q, Wang Y 2011 Chin. Phys. B 20 047401
[4] Schwerin B, Paliwal K 2014 Speech Communication 58 49
[5] Guo X, Ding X, Lei M 2012 Acta Phys. Hungar. 99 382
[6] Cheng X F, Zhang Z 2013 Acta Phys. Sin. 62 168701 (in Chinese) [成谢锋, 张正 2013 物理学报 62 168701]
[7] Fan J, Lv C, Zhang H 2008 J. Vibrat. Engin. 21 381 (in Chinese) [樊剑, 吕超, 张辉 2008 振动工程学报 21 381]
[8] Ma Y, Cheng X F 2014 Acta Phys. Sin. 63 068703 (in Chinese) [马勇, 成谢锋 2014 物理学报 63 068703]
[9] Liao C J, Li X J, Liu D S 2008 Chin. J. Sci. Instrum. 29 1862 (in Chinese) [廖传军, 李学军, 刘德顺 2008 仪器仪表学报 29 1862]
[10] Ma Y D, Yuan M, Qi C L, Liu Y, Liu Y J 2005 Comp. Engin. Appl. 41 81 (in Chinese) [马义德, 袁敏, 齐春亮, 刘悦, 刘映杰 2005 计算机工程与应用 41 81]
[11] Liu K, Jin W B 2008 Chong Qing Univ. Posts and Telecommun. 20 217 (in Chinese) [刘琨, 金文标 2008 重庆邮电大学学报 20 217]
[12] Wei L X, Zhang M, Zhong Y C, Han G 2012 Comp. Engin. Appl. 48 133 (in Chinese) [韦丽兴, 张淼, 钟映春, 韩光 2012 计算机工程与应用 48 133]
[13] Lindbad T, Kinser J M (translated by Ma Y D, Zhang K, Wang Z B) 2008 Pulse Coupled Neural Network Image Processing (Beijing: Higher Education Press) pp10-29 (in Chinese) [林德布莱德T, 凯泽J M 著(马义德, 绽琨, 王兆滨译) 2008脉冲耦合神经网络图像处理(高等教育出版社)第10–29页]
[14] Cheng X F, Ma Y, Tao Y W 2010 Chin. J. Sci. Instrum. 8 1712 (in Chinese) [成谢锋, 马勇, 陶冶薇 2010 仪器仪表学报 8 1712]
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