Email this article 
Open Access
Subscription or Fee Access
A Review: Sources Extraction from Mixed Sound Signals
Abstract
The sound observed by physician from chest cavity consists of mixed lung and heart sound. They observe the changes in sound for detection of abnormality. Now a day’s digital stethoscope is used in hospitals for patients monitoring. Its waveform is mixed with ambient noise and overlapped by vibrations from movement of body. The Fast ICA (independent component analysis) is source division procedure known as blind source separation technique. It is iterative and capable of dividing heart and lung sound giving physically important sources whenever physical mixing of two or more independent sources takes place. Noise reduction will be the very important initial stage in this algorithm as raw captured data contains most of the surface contact noise between stethoscope and chest surface. This algorithm will function as sound extractor from mixed heart and lung sound for its advanced examination which can be utilized to perceive diseases.
Keywords
References
Fatma Ayari and Ali T. Alouani, “Lung Sound extraction from mixed lung and heart sound FASTICA algorithm”, Electrotechnical Conference (MELECON), 2012 16th IEEE Mediterranean.
Jen-Chien Chien, Ming-Chuan Huang, Yue-Der Lin,Fok-ching Chong, “A Study of Heart Sound and Lung Sound Separation by Independent Component Analysis Technique”, Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE.
Vivek Nigam and Roland Priemer, “A procedure to extact the Aortic and Pumonary sounds from the Phonocardiogram”, Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Daniel Flores-Tapia, Zahra M. K. Moussavi and Gabriel Thomas, “Heart Sound Cancallation Based on Multiscale Products and Linear Prediction”, Biomedical Engineering, IEEE Transactions Volume: 54, Issue:2, Feb. 2007.
Tiago H. Falk and Wai-Yip Chan, “Modulation Filtering for Heart and Lung Sound Separation from Breath Sound Recordings”, Conf Proc IEEE Eng Med Biol Soc. 2008.
T. Tsalaile, S. M. Naqvi, K. Nazarpour, S. Sanei and J. A. Chambers, “Blind source extraction of heart sound signals from lung sound recordings exploiting periodicity of the heart sound” Acoustics, Speech and Signal Processing, ICASSP IEEE International Conference, 2008.
Bahador Makkiabadi, Delaram Jarchi, and Saeid Sanei, “A New Time Domain Convolutive BSS of Heart and Lung Sounds”, Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference at Kyoto.
K.Sathesh and Dr. N. J. R. Muniraj, “Real Time Heart And Lung Sound Separation Using Adaptive Line Enhancer With NLMS”, Journal of Theoretical and Applied Information Technology, 2014, India.
Sudo T., Tanaka H., Sugimoto C. and Kohno R, “A Study on Single-channel Non-stationary Noise Suppression for Cardiac Sound”, Medical Information and Communication Technology (ISMICT), 2014 8th International Symposium.
Sepideh Babaei and Amir Geranmayeh, “Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals”, Computers in Biology and Medicine 39 (2009), ELSEVIER.
I. Turkoglua, A. Arslanb and E. Ilkayc, “An expert system for diagnosis of the heart valve diseases”, Expert Systems with Applications 23 (2002) 229–236, ELSEVIER.
Sunita Chauhan, Ping Wang, Chu Sing Lim and V. Anantharaman, “A computer-aided MFCC-based HMM system for automatic auscultation”, Computers in Biology and Medicine 38 (2008) 221 – 233, ELSEVIER.
G.D. Clifford, “Singular Value Decomposition & Independent Component Analysis for Blind Source Separation”, HST582J/6.555J/16.456J Biomedical Signal and Image Processing Spring 2005.
A.Wims Magdalene Mary, Anto Prem Kumar, Anish Abraham Chacko, “BLIND SOURCE SEPARATION USING WAVELETS,” IEEE International Conference on Computational Intelligence and Computing Research, 2010.
Hongjuan Zhang, Guinan Wang, Pingmei Cai, Zikai Wu and Shuxue Ding, “A fast blind source separation algorithm based on the temporal structure of signals”, Neurocomputing 139 (2014) 261–271, ELSEVIER.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.