|
|
Application of Heart Sound Feature in the Typing Aided Diagnosis of Chronic Heart Failure |
Sun Wei,Guo Xingming#*,Zheng Yineng |
College of Bioengineering, Chongqing University, Chongqing Engineering Research Center for Medical Electronic Technology, Chongqing 400044, China) |
|
|
Abstract This article aimed to analyze the relationship between heart sound features extracted from time domain and time-frequency domain in heart failure patients with reduced ejection fraction (HFrEF) and heart failure patients with preserved ejection fraction (HFpEF). The heart sound signal lasting 20 minutes was recorded totally from the HFrEF patients(n=72)and HFpEF patients(n=172), the time ratio of the first to second heart sound(TS1/TS2), the amplitude ratio of the first to second heart sound(S1/S2), standard deviation of the ratio of diastolic to systolic duration(SDDS) and standard deviation of S1and S1interval (SDDSI) were extracted. Then S transformation was performed on the heart sound signal to analyze its characteristics in time-frequency domain, and the energy ratio of the first to second heart sound(ES1/ES2), the energy fraction of heart sound signal with low frequency(EF-LF), the energy fraction of heart sound signal with high frequency(EF-HF), the energy fraction of heart sound signal with low and high frequency in cardiac systole respectively(EF-SLF,EF-SHF), the energy fraction of heart sound signal with low and high frequency in cardiac diastole respectively(EF-DLF,EF-DHF) were extracted as well. Statistical results demonstrated that significant difference exited forTS1/TS2,S1/S2,SDDS,SDSSI,ES1/ES2,EF-SLF, and EF-DLF between two groups(P<0.05), while EF-LF,EF-HF,EF-SHF,and EF-DHF had no statisticcal significance(P>0.05). ISODATA was performed with four relatively independent features, the sensitivity and specificity for discriminating HFrEF patients and HFpEF patients reached 93.06% and 84.88% respectively. The features extracted from heart sound signal described the significant difference between two groups, which provided the basis of aided typing diagnosis for chronic heart failure.
|
Received: 17 January 2018
|
|
|
|
|
[1] 国家心血管病中心.中国心血管病报告2015[R].北京:中国大百科全书出版社, 2016. [2] Ponikowski P, Anker SD, Bueno H, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure [J]. European Journal of Heart Failure, 2016,18(8):2129-2200. [3] Lee DS, Gona P, Vasari RS, et al. Relation of disease patho-genesis and risk factors to heartfailure with preserved or reduced ejection fraction: insights from the framing ham heart study of the national heart, lung, and blood institute [J]. Circulation, 2009, 119:3070-3077. [4] Steinberg B, Xin Z, Fonarow G, et al. Trends in patients hospitalized with heart failure and preserved left ventricular ejection fraction: prevalence, therapies, and outcomes [J]. Circulation, 2012,126(1):65-75. [5] 葛均波,徐永健.内科学[M].北京:人民卫生出版社, 2013:165-170. [6] 成谢锋,陈亚敏.S1和S2共振峰频率在心音分类识别中的应用[J]. 南京邮电大学学报(自然科学版), 2017, 37(5): 7-12. [7] Leung SK, Lau CP, Lam CT, et al. Automatic optimization of resting and exercise atrioventricular interval using a peak endocardial acceleration sensor: validation with doppler echocardiography and direct cardiacoutput measurements[J]. Pacing Clin Electrophysiol, 2000, 23(11): 1762-1766. [8] Hansen PB, Luisada AA, Miletich DJ, et al. Phonocardiographyas amonitor of cardiac performanceduringanesthesia[J]. Anesthesia and Analgesia, 1989,68(3):385-387. [9] Durand LG, Langlois YE, Lanthier T,et al. Spectral analysis and acoustic transmission of mitral and aortic valve closure sounds in dogs[J]. Medical &Biological Engineering & Computing,1990, 28(5): 439-445. [10] Xiao Shouzhong, Guo Xingming, Wang Fanglu, et al. Evaluating two newindicators of cardiac reserve [J]. IEEE Engineering in Medicine & Biology Magazine, 2003,22(4):147-152. [11] Hsieh BP, Unver K, McNulty E, et al. the amplitude ratio of the first to second heart sound is reduced in left ventricular systolic dysfunction[J]. International Journal of Cardiology, 2010, 145(1):133-135. [12] 张文波. 心音图在慢性收缩性心力衰竭患者中诊断价值的研究[D].杭州:浙江大学, 2013. [13] 吴晓军,秦俭,郭兴明,等.心脏储备功能指标评估慢性心力衰竭严重程度的研究[J].重庆医学,2013,42(2):143-145. [14] 张国华,袁中凡,李彬彬.心音信号特征提取小波包算法研究[J].振动与冲击, 2008, 27(7): 47-49. [15] Cheng Xiefeng, Zhang Zheng. Denoising method of heart sound signals based on self-construct heart sound wavelet [J]. AIP Advances, 2014, 4(8): 087108-087108-9. [16] 郭兴明,蒋鸿,郑伊能.基于改进的维奥拉积分方法提取心音信号包络[J].仪器仪表学报, 2016, 37(10): 2352-2358. [17] 郭兴明,郭玮珍,周承文,等.心脏储备无创监测系统的研究[J].仪器仪表学报, 2008, 29(4): 690-695. [18] 李战明,韩阳,韦哲,等.基于S变换的心音信号特征提取[J]. 振动与冲击, 2012, 32(21): 179-183. [19] 孙即祥.现代模式识别[M].长沙: 国防科技大学出版社, 2002:25-44. [20] 邓海,覃华,孙欣. 一种优化初始中心的K-means聚类算法[J].计算机技术与发展, 2013, 23(11):42-45. [21] 赵彦涛,付美玲,王斌,等.基于主成分分析和重叠直方图统计的视频信号心率测量[J].中国生物医学工程学报, 2016, 35(3):284-291. [22] Shah AM, Solomon SD. Myocardial deformation imaging current status and future directions[J]. Circulation, 2012, 125(2):e244-e248. [23] 易诗欣,黎励文.射血分数保留的心力衰竭是射血分数降低的心力衰竭的早期表现[J].中华心血管病杂志, 2017, 45(6):480-481. [24] 边长勇,尹宗宪,李涛,等.射血分数保留与射血分数降低的充血性心力衰竭患者左室结构和功能比较[J].心脏杂志, 2015,27(5): 588-591. [25] 刘娜娜.射血分数保留和降低的慢性心力衰竭临床研究[D].乌鲁木齐:新疆医科大学, 2014. [26] Kehan Z, Jun H, Mingchui D. WhiteGaussian noise energy estimation and wavelet multi-threshold de-noising for heart sound signals[J]. Circuits, Systems, and Signal Processing,2014,33(9):2987-3002. |
|
|
|