**Nonlinear Adaptive Signal Processing Improves the Diagnostic Quality of Transabdominal Fetal Electrocardiography**

Radek Martinek, Radana Kahankova, Hana Skukova, Jaromir Konecny, Petr Bilik, Jan Zidek and Homer Nazeran

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/64068

#### **Abstract**

The abdominal fetal electrocardiogram (fECG) conveys valuable information that can aid clinicians with the diagnosis and monitoring of a potentially at risk fetus during pregnancy and in childbirth. This chapter primarily focuses on noninvasive (external and indirect) transabdominal fECG monitoring. Even though it is the preferred monitoring method, unlike its classical invasive (internal and direct) counterpart (transvaginal monitoring), it may be contaminated by a variety of undesirable signals that deteriorate its quality and reduce its value in reliable detection of hypoxic conditions in the fetus. A stronger maternal electrocardiogram (the mECG signal) along with technical and biological artifacts constitutes the main interfering signal components that diminish the diagnostic quality of the transabdominal fECG recordings. Currently, transabdominal fECG monitoring relies solely on the determination of the fetus' pulse or heart rate (FHR) by detecting RR intervals and does not take into account the morphology and duration of the fECG waves (P, QRS, T), intervals, and segments, which collectively convey very useful diagnostic information in adult cardiology. The main reason for the exclusion of these valuable pieces of information in the determination of the fetus' status from clinical practice is the fact that there are no sufficiently reliable and well-proven techniques for accurate extraction of fECG signals and robust derivation of these informative features. To address this shortcoming in fetal cardiology, we focus on adaptive signal processing methods and pay particular attention to nonlinear approaches that carry great promise in improving the quality of transabdominal fECG monitoring and consequently impacting fetal cardiolo‐ gy in clinical practice. Our investigation and experimental results by using clinicalquality synthetic data generated by our novel fECG signal generator suggest that adaptive neuro-fuzzy inference systems could produce a significant advancement in fetal monitoring during pregnancy and childbirth. The possibility of using a single device to

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leverage two advanced methods of fetal monitoring, namely noninvasive cardiotocog‐ raphy (CTG) and ST segment analysis (STAN) simultaneously, to detect fetal hypoxic conditions is very promising.

**Keywords:** adaptive signal processing, adaptive neuro-fuzzy inference system, fetal electrocardiogram, transabdominal monitoring, noninvasive cardiotocography, non‐ invasive ST segment analysis (STAN)

## **1. Introduction**

The fetal electrocardiogram (fECG) is a recoding of the electrical activity of the fetal heart and provides clinically significant information about the physiological state of a fetus during pregnancy and labor. Early detection of hypoxic states (hypoxemia, hypoxia, and asphyxia) achieved by fECG signal monitoring can ensure the fetus' well-being during these stages. For greater detail please see [1–3].

**Figure 1.** Real recordings of ECG signals by using the invasive and noninvasive techniques (f—fetal QRS, m—maternal QRS).

In clinical practice two methods are used to record fECG signals: invasive and noninvasive. The first one is direct and is performed transvaginally by using an Invasive Scalp Electrode (ISE). This approach is considered to be accurate as the fECG signals are recorded directly from the fetal's scalp without interference from the maternal heart (see **Figure 1**, the upper trace). However, it poses problems and risks to both the mother and the child (such as infections). In the noninvasive technique, multichannel skin bioelectrodes are placed on the mother's abdomen, and the simultaneous maternal (mECG) and fetal (fECG) signals, called the transabdominal or abdominal ECG (aECG), is acquired (See **Figure 1**, the lower 4 traces). This approach is convenient, noninvasive, and can be used during pregnancy and labor. However, there is a significant amount of overlap between fECG and mECG signals in addition to other undesirable signals such as bioelectric potentials (maternal muscle activity-mEMG, fetal movement activity, potentials generated by respiration and gastric activity, as well as power line interference [4,5], that deteriorate the quality of the afECG signals. **Figure 1** shows examples of fECG signals acquired by the invasive (VDIR) and noninvasive (VABD1–VABD4) approaches.

We observe that the strong mECG and the weak fECG signals overlap in both time-domain (**Figure 1**) and frequency-domain (**Figure 2**). Therefore, filtering the mECG component from the composite aECG signal to produce diagnostic quality fECG is a very challenging signal processing task. Currently only a fraction of the vast amount of diagnostic information in the aECG is available to be used in clinical practice. Therefore, maximizing information extraction from aECG (in addition to cardiotocography—CTG) signals for the timely and reliable detection of fetal hypoxia is of tremendous clinical interest and could significantly impact the advancements in obstetrics.

**Figure 2.** Abdominally recorded fECG and mECG in the frequency domain.

leverage two advanced methods of fetal monitoring, namely noninvasive cardiotocog‐ raphy (CTG) and ST segment analysis (STAN) simultaneously, to detect fetal hypoxic

**Keywords:** adaptive signal processing, adaptive neuro-fuzzy inference system, fetal electrocardiogram, transabdominal monitoring, noninvasive cardiotocography, non‐

The fetal electrocardiogram (fECG) is a recoding of the electrical activity of the fetal heart and provides clinically significant information about the physiological state of a fetus during pregnancy and labor. Early detection of hypoxic states (hypoxemia, hypoxia, and asphyxia) achieved by fECG signal monitoring can ensure the fetus' well-being during these stages. For

**Figure 1.** Real recordings of ECG signals by using the invasive and noninvasive techniques (f—fetal QRS, m—maternal

In clinical practice two methods are used to record fECG signals: invasive and noninvasive. The first one is direct and is performed transvaginally by using an Invasive Scalp Electrode (ISE). This approach is considered to be accurate as the fECG signals are recorded directly from the fetal's scalp without interference from the maternal heart (see **Figure 1**, the upper trace). However, it poses problems and risks to both the mother and the child (such as infections). In the noninvasive technique, multichannel skin bioelectrodes are placed on the mother's abdomen, and the simultaneous maternal (mECG) and fetal (fECG) signals, called the transabdominal or abdominal ECG (aECG), is acquired (See **Figure 1**, the lower 4 traces). This approach is convenient, noninvasive, and can be used during pregnancy and labor. However,

conditions is very promising.

56 Advanced Biosignal Processing and Diagnostic Methods

**1. Introduction**

QRS).

greater detail please see [1–3].

invasive ST segment analysis (STAN)

To address this signal processing challenge, many different approaches have been proposed to reliably detect fECG signals, but with varying degrees of success [4]. The holy grail of research in fetal electrocardiography is to fully recover the fECG signal and analyze its *morphology*, which produces valuable information on the fetus' status and health. The majority of recent techniques are mainly focused on the detection of the fetal heart rate (the intervals between R waves) with only a small portion being able to fully isolate the clinically useful ST interval and consequently perform accurate ST segment analysis (STAN) along with CTG. The only commercially available unit that has a built-in ability to perform STAN is Neoventa Medical's STAN S31. For a detailed description of this device, please see Ref. [6]. **Figure 3** shows an example of a real-time STAN. Fetal heart rate (fHR) and T/QRS are continuously displayed on the screen. These parameters are important in diagnosing hypoxic states. An increase in the ST segment and T wave as quantified by the ratio of the T wave to the QRS complex amplitude (T/QRS) has been associated with the different forms of the physiological responses expected in hypoxia (metabolic acidosis, myocardial glycogenolysis, etc.). For a more detailed explanation please see Reference [1].

**Figure 3.** Real-time ST Analysis (Fetal Heart Rate, T wave and QRS complex ratio) using Matlab application.

A critical review of the current signal processing literature reveals that adaptive signal processing and soft computing methods are rapidly growing research areas and offer great promise to address some of the most challenging signal separation, pattern recognition, and classification problems in different areas of medicine including Obstetrics.

Driven by these advancements and promises, in the methods section we will first present a theoretical overview of the advanced signal processing (both nonadaptive and adaptive) techniques that have been applied to the extraction (separation) of fECG from aECG signals, and will choose a subset based upon their advantages. We will mainly focus on the Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms [62]. Secondly, we will look at soft computing methods and describe how they have the ability to enhance the performance of adaptive signal processing algorithms in achieving better outcomes when processing biomedical signals. Then we will pay special attention to adaptive neuro-fuzzy inference systems (ANIFS) [60,63], which are considered to be the most significant in fetal electrocar‐ diography research.

To provide a comparative analysis of the performance of our selected adaptive algorithms and their enhanced realizations using soft computing approaches, in the results section, we will report the outcomes of a number of experiments that we devised by using aECG (identical to clinical) signals generated by our novel LabVIEW-Based Multi-Channel Noninvasive Ab‐ dominal Maternal-Fetal Electrocardiogram Signal Generator [7,8]. This abdominal fECG signal generator allows us to realistically simulate all types of signal contaminations (both biological and nonbiological) affecting the quality of aECG signals.

Our experimental results were evaluated using both subjective and objective criteria. For the objective evaluation, we used the SNR values before and after processing, the RMSE value, and the required processing time for the selected data samples. These performance metrics (parameters) are defined in separate subsections of the methods section.

In conclusion, our experimental results using synthetically-generated (identical to clinical) data produced by our novel system revealed that it was possible to effectively extract fECG signals and significantly refine their diagnostic quality to enable reliable ST segment and CTG signal analysis. Refined aECG monitoring systems with built-in STAN and CTG analysis capabilities will pave the way for the timely and reliable detection of fetal hypoxia during pregnancy and labor, which is of tremendous clinical interest. These enhanced fECG moni‐ toring systems will significantly impact the future advancements in Obstetrics.
