Performance Evaluation of Fetal ECG Extraction Algorithms

被引：2
|
作者：
Kaleem, Abdullah Mohammed [1 ]
Kokate, Rajendra D. [2 ]
机构：
[1] MPGI Sch Engn, Dept Elect & Telecommun, Nanded, Maharashtra, India
[2] Govt Coll Engn, Dept Instrumentat Engn, Jalgaon, Maharashtra, India
来源：
EMERGING RESEARCH IN ELECTRONICS, COMPUTER SCIENCE AND TECHNOLOGY, ICERECT 2018 | 2019年 / 545卷
关键词：
Fetal ECG; Abdominal ECG; Adaptive filter; Neural network;
D O I：
10.1007/978-981-13-5802-9_17
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Fetal ECG contains possibly exact information that may support clinicians in making apt decisions during pregnancy and labor. Hence, extraction of clean fetal ECG is extremely crucial for fetal surveillance. This is done by placing electrodes on mother's abdomen. However, it is tainted with various sources of noise. This paper compares LMS adaptive filter for FECG extraction with neural network-based adaptive filter. Twenty cases of clinical data were used. Experimental results showed superiority of later scheme in terms of SNR and MSE.
引用
收藏
页码：187 / 194
页数：8
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