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首页> 外文期刊>Journal of medical systems >Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques
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Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques

机译：使用自适应噪声消除和小波变换技术的胎儿心电图提取和分析

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Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods. The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram. Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.
机译：与生育缺陷相关的消亡主要是由于先天性心脏缺陷。在怀孕的早期阶段，通过查找有关胎儿的信息来识别胎儿问题，以避免死产。用于监测胎儿健康状况的黄金标准由Cardiotachography（CTG），不能用于长持续时间和连续监测。需要使用便携式设备的胎儿ECG信号的连续和长时间监测胎儿的逐行健康状态。心电图记录的非侵入性方法是用于诊断胎儿心脏问题而不是侵入性方法的最佳方法之一。对FECG的监控需要开发小型化硬件和有效的信号处理算法，以提取嵌入在母ECG中的FECG。本文讨论了开发的原型硬件，用于监控，并记录包含FECG的原始母电子ECG信号和信号处理算法，以提取胎儿电磁孔仪信号。我们提出了两种信号处理方法，首先基于最小均方（LMS）自适应噪声消除技术，另一种方法基于小波变换技术。设计并开发了一种原型硬件以获取包含母亲和胎儿ECG的原始ECG信号，并且使用信号处理技术来消除噪声并提取胎儿ECG，研究胎儿心率变异性。通过从胎儿ECG模拟器中获取的信号从物理体数据库和从主题获取的信号评估这两种方法。通过寻找心率及其可变性，幅度谱和提取的胎儿ECG的平均值来评估这两种方法。此外，还针对胎儿QRS检测技术确定了准确性，灵敏度和阳性预测值。本文自适应滤波技术采用符号LMS算法和小波技术与Daubechies小波一起使用，以及用于提取胎儿心电图的De Noising技术。这两种方法都具有良好的敏感性和准确性。在自适应方法中，灵敏度为96.83，精度为89.87，小波灵敏度为95.97，准确度为88.5。另外，分析了来自母亲和胎儿的心率变异性曲线图的时域参数。

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