Frequency shifting approach towards textual transcription of heartbeat sounds

Auscultation is an approach for diagnosing many cardiovascular problems. Automatic analysis of heartbeat sounds and extraction of its audio features can assist physicians towards diagnosing diseases. Textual transcription allows recording a continuous heart sound stream using a text format which can be stored in very small memory in comparison with other audio formats. In addition, a text-based data allows applying indexing and searching techniques to access to the critical events. Hence, the transcribed heartbeat sounds provides useful information to monitor the behavior of a patient for the long duration of time. This paper proposes a frequency shifting method in order to improve the performance of the transcription. The main objective of this study is to transfer the heartbeat sounds to the music domain. The proposed technique is tested with 100 samples which were recorded from different heart diseases categories. The observed results show that, the proposed shifting method significantly improves the performance of the transcription.

[1]  T. Modegi,et al.  Proposals of MIDI coding and its application for audio authoring , 1998, Proceedings. IEEE International Conference on Multimedia Computing and Systems (Cat. No.98TB100241).

[2]  Farshad Arvin,et al.  Real-time segmentation of heart sound pattern with amplitude reconstruction , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[3]  Mark D. Plumbley,et al.  Automatic Music Transcription and Audio Source Separation , 2002, Cybern. Syst..

[4]  Toshio Modegi MIDI encoding method based on variable frame-length analysis and its evaluation of coding precision , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[5]  Toshio Modegi XML Transcription Method for Biomedical Acoustic Signals , 2001, MedInfo.

[6]  Shankar M. Krishnan,et al.  Neural network classification of homomorphic segmented heart sounds , 2007, Appl. Soft Comput..

[7]  Shyamala Doraisamy,et al.  Heart sound musical transcription technique using multi-Level preparation , 2010 .

[8]  Zhongwei Jiang,et al.  The moment segmentation analysis of heart sound pattern , 2010, Comput. Methods Programs Biomed..

[9]  Shyamala Doraisamy,et al.  Real-Time Pitch Extraction of Acoustical Signals Using Windowing Approach , 2009 .

[10]  Zhongwei Jiang,et al.  A cardiac sound characteristic waveform method for in-home heart disorder monitoring with electric stethoscope , 2006, Expert Syst. Appl..

[11]  Tran Huy Dat,et al.  Heart sound as a biometric , 2008, Pattern Recognit..

[12]  Sepideh Babaei,et al.  Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals , 2009, Comput. Biol. Medicine.

[13]  Thierry Dutoit,et al.  A novel method for pediatric heart sound segmentation without using the ECG , 2010, Comput. Methods Programs Biomed..

[14]  P Ask,et al.  A method for accurate localization of the first heart sound and possible applications , 2008, Physiological measurement.

[15]  Tamer Ölmez,et al.  Heart sound classification using wavelet transform and incremental self-organizing map , 2008, Digit. Signal Process..

[16]  Mark Sandler,et al.  BLACKBOARD SYSTEM AND TOP-DOWN PROCESSING FOR THE TRANSCRIPTION OF SIMPLE POLYPHONIC MUSIC , 2000 .

[17]  Shyamala C. Doraisamy,et al.  Polyphonic music retrieval: the n-gram approach , 2005, SIGF.

[18]  Stefan M. Rüger,et al.  Robust Polyphonic Music Retrieval with N-grams , 2003, Journal of Intelligent Information Systems.

[19]  Rafael E. Riveros,et al.  Studies in Health Technology and Informatics , 2005 .

[20]  M.P. Ryynanen,et al.  Polyphonic music transcription using note event modeling , 2005, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2005..

[21]  Shyamala Doraisamy,et al.  An Approach for Heartbeat Sound Transcription , 2009, 2009 International Conference on Computer Technology and Development.

[22]  Tamer Ölmez,et al.  Classification of heart sounds using an artificial neural network , 2003, Pattern Recognit. Lett..

[23]  Constantin F. Aliferis,et al.  Studies in Health Technology and Informatics , 2007 .