Department of Biomedical Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India.
Department of Electronics and Communication, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India.
J Med Syst. 2017 Dec 8;42(1):21. doi: 10.1007/s10916-017-0868-3.
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.
出生缺陷相关的死亡主要是由于先天性心脏缺陷。在妊娠早期,可以通过寻找有关胎儿的信息来识别胎儿问题,从而避免死产。监测胎儿健康状况的金标准是通过心电图(CTG),但不能长时间连续监测。需要使用便携式设备连续长时间监测胎儿心电图信号,以研究胎儿的渐进健康状况。使用非侵入性方法记录心电图是诊断胎儿心脏问题的最佳方法之一,而不是侵入性方法。监测胎儿心电图需要开发一种小型化的硬件和高效的信号处理算法,以从母亲心电图中提取胎儿心电图。本文讨论了一种原型硬件的开发,用于监测和记录包含胎儿心电图的原始母亲心电图信号,以及一种信号处理算法,用于提取胎儿心电图信号。我们提出了两种信号处理方法,一种是基于最小均方(LMS)自适应噪声消除技术,另一种是基于小波变换技术。设计并开发了一种原型硬件,用于获取包含母亲和胎儿心电图的原始心电图信号,并使用信号处理技术消除噪声并提取胎儿心电图,研究了胎儿心率变异性。从胎儿心电图模拟器、Physionet 数据库和受试者获得的信号评估了这两种方法。通过寻找心率及其变异性、提取胎儿心电图的幅度谱和平均值来评估这两种方法。还确定了胎儿 QRS 检测技术的准确性、灵敏度和阳性预测值。本文自适应滤波技术使用 Sign-sign LMS 算法和 Daubechies 小波的小波技术,并结合去噪技术用于提取胎儿心电图。两种方法都具有良好的灵敏度和准确性。在自适应方法中,灵敏度为 96.83,准确性为 89.87,小波灵敏度为 95.97,准确性为 88.5。此外,还分析了来自母亲和胎儿心率变异性图的时域参数。