论文信息 - Detection of Pathological Condition of Heart Using Texture Complexity of the Heart Sound Signals in Kernel Space

Detection of Pathological Condition of Heart Using Texture Complexity of the Heart Sound Signals in Kernel Space

The heart sound signal is produced due to the mechanical activity of cardiovascular system and hence, it carries relevant information regarding the pathological and non-pathological condition of heart. Physicians listen to heart sounds using a stethoscope device and make interpretation regarding the diseases if any. Hence, its performance depends on the medical practitioners knowledge and experience. In this study, a very simple alternative approach of auscultation technique is introduced for early detection of cardiac abnormality based on a combined framework of kernel space decomposition and textural analysis. The experimental results are validated by physician's diagnosis and statistical analysis. The proposed diagnostic tool can be used in combination with audio clue of heart sound signal to train inexperienced physicians and as an assistive device for experienced doctors.

Ramaswamy Palaniappan | Ashok Mondal

[1] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.

[2] Messaoud Benidir,et al. Wavelet based segmentation of some abnormal heart sound signal , 2012 .

[3] Ashok Mondal,et al. A Noise Reduction Technique Based on Nonlinear Kernel Function for Heart Sound Analysis , 2018, IEEE Journal of Biomedical and Health Informatics.

[4] Christian Brandt,et al. Shannon Entropy based on the S-Transform Spectrogram applied on the classification of heart sounds , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5] Ahmet Arslan,et al. An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks , 2003, Comput. Biol. Medicine.

[6] John L. Semmlow,et al. Robust detection of background noise in phonocardiograms , 2011, 2011 1st Middle East Conference on Biomedical Engineering.

[7] Darya Aleinikava,et al. Automated classification of normal and abnormal heart sounds using support vector machines , 2016, 2016 Computing in Cardiology Conference (CinC).

[8] Said Boussakta,et al. Signal analysis of medical acoustic sounds with applications to chest medicine , 2007, J. Frankl. Inst..

[9] Alan V. Sahakian,et al. Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome , 2007, IEEE Transactions on Biomedical Engineering.