Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/4866
標題: 利用時域相關特性之自動心音偵測演算法
Automatic heart sound detection employing temporal correlation information
作者: 林正森
Lin, Cheng-Sen
關鍵字: first heart sound(S1);心音偵測;second heart sound(S2);heart sound;murmur;wavelet transform;extra heart sound;auscultation;心音;小波轉換;心雜音;附加心音;第一心音;第二心音
出版社: 通訊工程研究所
引用: 中文參考文獻 [1]周明加、錢宗良、何杏枌、李玉菁、彭幸櫻、李亭輝著,“解剖生理學”,華杏機構叢書,2001 。 [2]黃群耀編譯,“輕鬆掌握心音聽診 第二版” ,ELSEVIER TAIWAN LLC。 [3]謝美玲、李業英、彭孃慧、蕭伃伶、江錦玲、李家琦、孫宜孜、傅淑瑩、陳美君、周雨樺、陸秀芳譯,“護理健康評估”,五南圖書,2008。 [4]陳金山、徐淑媛譯,“彩色圖解基礎人體解剖與生理學”,合記圖書,2001。 西文參考文獻 [5]M.B. Malarvili, I. Kamarulafizam, S. Hussain, D. Helmi,“Heart Sound Segmentation Algorithm Based on Instantaneous Energy of Electrocardiogram”, Computers in Cardiology, 327-330, 2003. [6]M. El-Segaier, O. Lilja, S. Lukkarinen, L. Srnmo, R. Sepponen and E. Pesonen, ”Computer-Based Detection and Analysis of Heart Sound Murmur”, Annals of Biomedical Engineering, vol. 33, no. 7, 937-942, July, 2005. [7]G. Saha, P. Kumar, “An Efficient Heart Sound Segmentation Algorithm for Cardiac Disease”, IEEE INDIA ANNUAL CONFERENCE, 2004. [8]H. Liang, S.Lukkarinen and I. Hartimo,“Heart sound segmentation algorithm based on heart sound envelogram”, Computers in Cardiology, vol. 24, 105-108 , 1997. [9]S.M. Debbal, F. Bereksi-Reguig,“Computerized heart sounds analysis”, Computers in Biology and Medicine 38, 263-280, 2008. [10]A. Djebbari, F. Bereksi-Reguig, “Short-Time Fourier Transform analysis of the phonocardiogram signal”, Electronics, Circuits and Systems, vol. 2, 844-847, 2000. [11]H. Liang, S. Lukkarinen and I. Hartimo,“A heart sound segmentation algorithm using wavelet decomposition and reconstruction“, Proceedings - 19th International Conference IEEE-EMBS, Chicago, USA, October 30- November 2,1630-1633,1997. [12]H. Liang, I. Hartimo,“A heart sound feature extraction algorithm based on wavelet decomposition and reconstruction”, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 20, no3, 1998. [13]M. Yamacli, Z. Dokur, T. Olmez,“Segmentation of S1-S2 Sounds in Phonocardiogram Records Using Wavelet Energies”, Digital Object Identifier: 10.1109/ISCIS.2008.4717964 , 1-6, 2008. [14]Z. Dokur, T. Olmez,“Feature determination for heart sounds based on divergence analysis”, ScienceDirect, Digital Signal Processing19 , 521-531, 2009. [15]S. Omran, M. Tayel,“A heart sound segmentation and feature extraction algorithm using wavelet ”, Identifier :10.1109/MWSCAS.2003.1562301, vol. 1, 392 – 395, 2003. [16]F. Beritelli, S. Serrano, “Biometric Identification Based on Frequency Analysis of Cardiac Sounds”, IEEE transations on Information Forensics and Security, vol. 2, no. 3, September, 2007. [17]http://big5.wiki8.com/xindongzhouqi_48312/ [18]http://www.cprworks.com/Cardiology%20Heart%20Sounds.html [19]http://www.meddean.luc.edu/templates/lumen/search_engine/multimedia/controled_results.cfm [20]http://www.medstudents.com.br/cardio/heartsounds/heartsou.htm [21]http://www.med.umich.edu/lrc/psb/heartsounds/index.htm [22]http://depts.washington.edu/physdx/heart/demo.html [23]http://www.monroecc.edu/depts/pstc/backup/parashs.htm [24]http://courses.kcumb.edu/physio/CVHtSnds-03/HtSnds-04.htm
摘要: 
自動心音分析透過處理心音訊號,來判別是否有任何的異常情形存在,可避免傳統心音聽診需要技術與經驗。
在自動心音分析處理中,首先必需找出第一心音與第二心音在一個心動週期的發生位置,以利異常心音的偵測,進而判斷是何種類型的心臟疾病。本文提出利用時域相關性的自動心音偵測演算法,不需要額外的同步參考訊號的輔助,利用小波轉換移除部份的心雜音以強化正常心音成份,並依據心音在心動週期內的週期性特徵,來找出正常心音發生位置。在含有心雜音的情況下,本文所提演算法對於第一心音及第二心音的偵測正確率分別為87.81%與87.46%;在含有附加心音的情況下,本文所提演算法對於第一心音及第二心音偵測正確率皆為100%。

Analysis of heart sound signals by auscultation helps diagnosing abnormal heart sounds effectively. The key to achieve that is to locate the normal heart sounds in a
cardiac cycle, from which a medical practitioner can predict the type of cardiac diseasein an early stage.
In this research, we propose an algorithm for automatic heart sound analysis which exploits the temporal
correlation property to detect heart sounds without
synchronic reference signals.
It firstly uses wavelet transform to reduce the amount of cardiac murmurs. Then,the temporal correlation property which results from the periodic pattern of heart sound is explored to help identify the locations the first heart sound(S1) and the second heart sound(S2), from which the existence of murmurs or extra heart sounds can be detected. Simulation results show that the correct detection rate is 87.81% for S1 and 87.46% for S2 when the heart sound is embedded with murmur. When there exists extra heart sounds, the proposed algorithm can achieve 100% detection of both
S1 and S2.
URI: http://hdl.handle.net/11455/4866
其他識別: U0005-2505201214423400
Appears in Collections:通訊工程研究所

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