Automated Heart Sound Diagnosis Using Feature Extraction Model for Bio-signals

被引：0
|
作者：
Rajakumar, P. S. [1 ]
Ravi, S. [2 ]
Suresh, R. M. [3 ]
机构：
[1] JNT Univ, Hyderabad, Andhra Pradesh, India
[2] Dr MGR Educ & Res Inst, Dept ECE, Madras, Tamil Nadu, India
[3] RMD Engn Coll, Dept CSE, Madras, Tamil Nadu, India
来源：
GLOBAL TRENDS IN INFORMATION SYSTEMS AND SOFTWARE APPLICATIONS, PT 2 | 2012年 / 270卷
关键词：
AHSC; Murmur; Feature Extraction; Mel Frequency Cepstral Coefficient (MFCC); Continuous wavelet Coefficient (CWTC); ROBUST SPEECH RECOGNITION; SPECTRUM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper a model that can quantitatively predict human performance in typical spectral and temporal masking situations and be immediately applicable in the field of Automatic heart sound classifier (AHSC) is presented. The various features extracted in the experiments were studied in the presence and absence of noise and for various biomedical signals and the system reported to be robust. The developed model shall be used as front end for AHSC.
引用
收藏
页码：103 / +
页数：3
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