2017年SIRS国际会议信号处理与智能识别系统精选论文集

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《第三届国际信号处理与智能识别系统会议论文集》是一本汇集了2017年9月13日至16日在印度曼尼普尔举行的第三次国际信号处理与智能识别系统（SIRS'17）会议上精选的经过同行评审和修订的论文。该书汇编涵盖了生物特征识别、数字水印、识别系统、图像和视频处理、信号与语音处理、模式识别、机器学习以及基于知识的系统等多个领域。作者包括Sabu M. Thampi、Sri Krishnan、Juan Manuel Corchado Rodríguez、Swagatam Das和Michal Wozniak等人，共同编辑了这一宝贵的资源。 "Advances in Intelligent Systems and Computing" 是一个系列，由波兰科学院华沙分院的 Janusz Kacprzyk 博士担任系列编辑，这个系列专注于智能系统和智能计算的理论、应用及设计方法。该系列覆盖了工程、自然科学、计算机与信息科学、信息技术、经济学、商务、电子商务、环境、健康医疗和生命科学等众多学科，旨在为现代智能系统和计算领域的研究者提供全面的内容。这本书作为系列的第678卷，不仅包含了深入探讨的理论知识，也收录了会议上的最新研究成果和实践案例。会议论文旨在推动信号处理领域的前沿进展，尤其是在智能识别技术方面，这些技术包括但不限于深度学习算法在人脸识别中的应用、声纹识别系统的优化、以及图像分析中的智能决策支持系统。通过阅读这些论文，读者能够了解到当前信号处理技术如何结合人工智能技术，提升系统性能，解决实际问题，并且探索未来可能的发展方向。《Proceedings of the Third International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS-2017)》为信号处理及其相关领域的研究者提供了一个全面了解和交流最新研究成果的平台，是所有对此领域感兴趣的专业人士不可或缺的学习资料和参考来源。无论是对技术开发者、工程师还是学者，这本书都为他们在探索信号处理与智能识别系统的理论与实践提供了丰富的洞见和灵感。

Removal of BW and Respiration Noise

in abdECG for fECG Extraction

Jeffy Joseph

1(&)

, J. Rolant Gini

, and K.I. Ramachandran

Department of Electronics and Communication Engineering,

Amrita School of Engineering Coimbatore, Amrita Vishwa Vidyapeetham,

Amrita University, Coimbatore, India

jeffyjoseph2007@gmail.com, j_rolantgini@cb.amrita.edu

Center for Computational Engineering and Networking,

Amrita School of Engineering Coimbatore, Amrita Vishwa Vidyapeetham,

Amrita University, Coimbatore, India

ki_ram@cb.amrita.edu

Abstract. Electrocardiogram (ECG) signals are one of the most important

diagnostic tools for any doctor, especially a cardiologist. It is important that the

fetus present inside the abdomen undergoes a fetal ECG recording to assess the

health of the fetus. Complications like disturbance because of movement of

abdominal muscles are usually present during the recording and leads to the

wrong diagnosis of the fetus ECG. In this paper, the signal in dispute had been

altered in the proposed method so as to eliminate the wandering of the baseline,

respiration noise and also expel the noise from other sources. The acquired

abdominal ECG signal in a noninvasive manner had been considered for

extracting the fetal ECG after eliminating the noise. The windowed zero mean

method is used where the ﬁrst step is segmentation. In segmentation, the

abdominal ECG signal is divided into set of samples based on window size.

Zero mean is applied across each of the windowed abdominal ECG signals to

address the issue of baseline wandering and respiration noise. This is followed

by the application of a bandpass ﬁlter to cancel the high-frequency noise

component. This process results in an ECG signal that almost has no compli-

cations as present before. The fetal ECG signal that is procured using such a

method is now easier to diagnose as compared to the acquired signal which

contains noise. Thus, for a fetus, this can help in proper diagnosis. It is further

noted that this method is very reliant on using and is lucid. It can be used to

augment and alter signals where such complications arise in the ﬁeld of medi-

cine and clinical diagnosis.

Keywords: fetal ECG (fECG)

 abdominal ECG (abdECG)  Baseline wander

(BW)

 Respiration noise  Windowed Zero Mean (WZM)

1 Introduction

The simplicity of ECG waves is what makes them easy to comprehend and understand

when compared to other wave signals. From an ordinary heart check-up to the detection

of an arrhythmia we need to use the ECG [1]. The disturbance in an ECG due to

S.M. Thampi et al. (eds.), Advances in Signal Processing and Intelligent

Recognition Systems, Advances in Intelligent Systems and Computing 678,

DOI 10.1007/978-3-319-67934-1_1

various noise can cause the wrong diagnosis due to bereavement of information. Some

problems that arise in ECG signal meas urement are due to noise such as baseline

wandering which is also usually accompanied by high-frequency noise components.

Baseline wandering can arise due to respiration and sweat that affects the parameters of

the electrode such as impedance. High-frequency noise components are caused by

movement of the body during the procedure.

Welfare of the fetus is the prime concern in pregnancy. Though the fetal mortality

rate [2] has decreased in developed countries, it is still a grave issue in developing and

underdeveloped nations. Genetic diseases always pose a large looming threat on the

fetus. Congenital malformations, membrane complications, malaria, and pre-eclampsia

are some of the disease responsible for fetal deaths. In a study in England and Wales in

the year 2007, it was found that congenital anomalies are the second highest cause of

infant deaths [3]. Bradycardia [4] has led to the invention of the micropacemaker [5]

which when used in a fetus helps forestall the need for exigent removal from its womb

in case of any medical emergency.

There are many ways to monitor the fetus using techniques like ultrasound which is

based on the usage of sensors. Non-stress test and contraction stress test are also other

ways of externally monitoring the state of the fetus. Presumptuous methods like

abdECG also help in relatively accurate measurement. Fetal ECG (fECG) is obtained

using electrodes which are planted on the head of the fetus. Such a prying procedure is

very cumbersome and invasive, which calls for the requisite for procedures that are

non-invasive. Observation of the fECG can help in having an insight of ailments and

anomalies that could be present in the fetus. One of the latest developments is the use of

Raspberry Pi microcontroller to monitor fECG [6]. There is various noise usually

present in the ECG that can arise from various sources such as interference of the

power line, noise due to contact established between electrodes, electrosurgical noise,

instrumentation noise and electromyography (EMG) noise that occurs due to muscle

movement in the vicinity of the heart. Since the fetus is present near the abdomen, there

is the possibility of much clamor that can occur due to the position and movement of

the fetus. The abdomen is in an anterior part of the body which contains important

organs like stomach and liver.

The abdominal muscles present abet in the process of breathing as adjunct support.

During this process, there is disturbance because of the process of respiration as there is

the movement of abdominal muscles present near the electrode. This neighboring signal

gets acquired as a part of the reading and is more prominent when compared to the actual

signal obtained from the abdomen . The noise created due to these such as high-frequency

noise components due to muscle movement and the wandering of the baseline have to be

addressed. Electromyography noise arises due to muscle movement that can affect the

changes in fECG especially if it occurs adjacent to the heart of the fetus. These noises are

inevitable and induce fallacious results. Wandering of the baseline can occur due to

improper placement of electrodes and this results in the ECG wave wandering above and

below the reference due to variation in amplitude. Some of these disturbances cause the

signal to get lost amidst these undesirable perturbations. These problems need to be

adhered to as the welfare of the fetus depends on it amidst pregnancy. This demands for

elementary met hods to deal with it. The paper has been fabricated in the following order:

Sect. 2 provides an accord about the literature survey that gives requisite background

4 J. Joseph et al.

information. This is followed by Sect. 3 which presents the method with which the

problem had been approached. This is followed by Sect. 4 gives a discourse about the

results achieved through the Windowed Zero Mean (WZM) method applied in this

paper. The Sect. 5 gives the conclusion inferred from this method.

2 Literature Review

For a fetus, especially, the ECG signal is taken from the abdomen of the mother and as

seen previously there is a lot of disturbance because of noise and this can lead to a

misdiagnosis. The primary reason for this is that the ECG signal masquerades as noise

in certain parts of the signal and only upon close observation it is realized that it is not

noise but rather indispensable information that is being lost. The usage of such vital

information can aid in augmenting the process of diagnosis. Extraction of respiration

signal from ECG signal (EDR) is carried out using methods like single lead ECG [7]

where EDR is estimated by cubic-spline interpolation; conductive textile electrodes

which use the concept of instantaneous frequency estimation [ 8 ].

Removal of noise from Surface respiratory EMG signal is also effectuated using

methods such as lean mean square widrow adapti ve structure [9], butterw orth ﬁltering

[10] and adaptive ﬁltering [11]. The Heart Rate Variability method is proposed as a

more accurate method when using the single lead ECG [7] whereas the conductive

textile electrode [8] method indicates a closer alternation with the actual respiration

signal. The lean mean square widrow adaptive structure [9] removes noise without the

need for additional electrodes. In butterworth ﬁltering method [10], the noise due to the

electrode is nulliﬁ ed using a butterworth ﬁlter of corner freque ncy 20 Hz. Recursive

least squares algorithm is used in the adaptive ﬁltering method [11] to eliminate Power

Line Interference and ECG noise. An EDR extracted signal accommodates plenty of

noise and needs to undergo pre-processing in order to obtain a clean ECG. This incites

the requisite for unsophisticated methods that need to be devised in order to deal with

it. Some of the acclaimed methods that had been used to address this problem of the

ECG until now are Empirical Mode Decomposition (EMD) [12] based on

Hilbert-Huang transform [13], linear phase ﬁltering [14], moving average ﬁlter [15] and

discrete wavelet transform [16]. EMD [12] which is based on Huang transform [13]

relies purely on data which might be a disadvantage in certain cases where the data

alone will not sufﬁce. Summation of Intrinsic mode functions is generated upon

decomposition of the EMD [12] which is the motive of the EMD [12] method.

Linear phase ﬁltering method [14] primarily focuses on reduction in the number of

computations needed to achieve de-noising of signal and elimination of baseline

wandering. This, however, can cause signal distortion as there will be delay variation

created due to the propagation of the signal through devices which may lead to

deprivation of crucial information. The moving average ﬁlter [15] is applied to the

ECG and is smoo thened using polynomial curve ﬁtting. However, this requires

pre-processing which might not be suitable for all cases. Discrete Wavelet Transform

[16] is used to choose wavelets and the depth to attain the level of decomposition is

also decided. Following this, the wavelet is shrunk using Empirical Bayes posterior

median. The problem with a wavelet-based approach is that the accuracy of this method

Removal of BW and Respiration Noise in abdECG for fECG Extraction 5

will not sufﬁce and lead to bereavement of information. While all these methods are

used to deal with ECG, none of them indicate a method to expel noise in a fECG. The

Windowed Zero Mean (WZM) method proposed has ensured that there is no loss of

information and uses a relatively lucid approach. Using MATLAB, this method ensures

smooth de-noising and reinstatement of the original fECG signal without any com-

promise on the original signal.

3 Methodology

The current method is used to addres s the issues stated above such as Electromyog-

raphy noise and baseline wandering. The procedure on how the method is ingrained is

stated in the block diagram present below (Fig. 1). An abdECG signal which consists

of N values from x(1) to x(N) is considered as input to the segmentation stage of the

block diagram. In the ﬁrst stage, the signal is segmented and segregated into different

windows. The segmented signal comprises of N windows with each window of length

n1. So, taking the ﬁrst window, elements of the signal from x(1) to x(n1) are denoted as

W1; the second window, elements of the signal from x(n1 + 1) to x(2 * n1 + 1) are

denoted as W2; and so on until the N

window where elements from x(k * n1 + 1) to

x(N) are present. Then, zero mean of each segmented window on using the formula

given below is taken to deprive the signal of the undesired deviation of baseline.

XkðÞ¼WkðÞmean W

:ðÞðÞ ð1Þ

Here, X(k) [where k is an arbitrary number] is the zero mean value obtained for a

window W

and W(k) is an element in the window. The same is to be done for all the

windows before concatenation. The signals after application of zero mean are identiﬁed

and grouped accordingly from X(1) to X(N). Concatenation of all these segmented

windows gives the resultant signal. In the post-processing stage, the signal with ele-

ments from X(1) to X(N) is passed through a bandpass ﬁlter with lower cut-off

X(2)

abdECG

x(k*n1+1)

x(N)

X(N)

(with BW

and

Respiration

noise)

X(1)

Concatenation

x(n1

+1)

x(2*n1+1)

BW and respira-

-tion noise

removed

abdECG

Segmentation

abdECG

(Window

size=n1)

Band Pass

Filter with

fc1 and

fc2

X(1):X(N)

Zero Mean

of W

Zero Mean

of W

Zero Mean

of W

x(1):x(n1)

|||

Fig. 1. Block diagram of Windowed Zero Mean Method

6 J. Joseph et al.

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