Engineering, 2013, 5, 63-66
doi:10.4236/eng.2013.55B013 Published Online May 2013 (http://www.scirp.org/journal/eng)
Continuously Monitoring Foetal ECG using Mobile
Telemedicine Sensor Network
Mahmoud Ahmed Suliman Ali
Department of electrical & electronic Engineering , faculty of Engineering science, Nyala University, Nyala, Republic of the Sudan
Received 2013
ABSTRACT
Foetus ECG monitoring based on Bluetooth portable devices promise to provide an efficient, accurate, and economic
way to monitor foetus health outside the hospital. In this paper we discuss a new idea in biomedical field may be useful
for Medical services. The idea is deliver the status of patent to any location within the coverage of cellular networks,
such as the global system for mobile (GSM) communications. Pregnancy women from a rural area just like rural in Su-
dan (where they haven’t transportation to a hospital), could be given a routine check by mobile phone without having to
commute regularly to a hospital. Routine inspections and monitoring could be done while the pregnancy women is at
home, traveling(nomadic), at work, or at leisure, thereby relieving resources for more demanding hospital cases. Ad-
vances in mobile technology have made wireless telemedicine more practical both within hospitals and globally. The
Bluetooth system with low cost the poor women can use and also has low electromagnetic waves, it is healthy in use
(no risk).
Keywords: FECG; Bluetooth; Mobile System and GPRS
1. Introduction
Foetal electrocardiogram is a very useful technique for
monitoring foetus in side the mother’s abdominal. Re-
cently there is some research expected the Continuous
foetal heart rate (FHR) monitoring during labor may
utilized 85% of labor episodes in the United States and
represents the standard of care[1], although there is scant
evidence to demonstrate that the use of the technology
improves newb orn or mate rnal outc omes.
Encouraging data demonstrate that intrapartum foetal
electrocardiogram (fECG) analysis can reduce newborn
acidemia, hypoxic ischemic encephalopathy, and cesarean
deliveries. However, the only clinically available device
for fECG analysis—the STAN monitor from Neoventa
(Moindal, Sweden)—requires an invasive foetal scalp
electrode (FSE), limiting its use to a subset of pregnant
women who are laboring with ruptured membranes and a
dilated cervix. The potential utility of noninvasive fECG
for foetal evaluation is significant. However, to date [2],
there has been no systematic study proving that fECG
can be extracted non-invasively without distorting im-
portant clinical parameters, such as the ST segment. Prior
reports have shown the capacity to measure FHR using
electrodes on the maternal abdomen, but none have dem-
onstrated the cap acity to accurately record the fECG wave-
form with sufficient fidelity to evaluate the morphology.
FECG is project may be developed to be continuously
mentoring in small portable devices and cheap, suitable
for the poor family. Recently Sensor Mobile telemedicine
is a newly emerging branch of medical service, means
delivered Medical services to any location within the
coverage of cellular networks. This project review on
Sensor mobile telemedicine used for Sensing of Electro-
cardiogram (ECG) data, Electrocardiogram is informa-
tion about human heart, and then the information de-
tected by sensor is electrical signal generated by heart.
Known as The standard FECG signal consists of six peak
signals each defined with a different letter, the P, Q, R, S,
T and U peaks [1]. The Sensor Mobile telemedicine has
the potential to improve patients’ quality of life by al-
lowing them to move around freely while undergoing
continuous heart monitoring and to reduce healthcare
costs associated with prolonged hospitalization, treat-
ment and monitoring.
2. Literature Review
Foetus ECG monitoring is a technique for obtaining im-
portant information’s about the condition of the foetus
during pregnancy and labour by measuring electrical
signals generated by the foetal heart as measured from
multi-channel potential recordings on the mother body
surface or hearing foetus heart beat, because Being born
is one of the most crucial events in our life. Historically

M. A. S. ALI
64
In 1958 using an electrode placed through the maternal
abdomen on the foetus. [2]. There are two situations for
which FHR provides important information about the
condition of the foetus. It is kn own that FHR monitoring
is able to distinguish between the so called reactive foe-
tus and the so called non-reactive. To achieve the goal
using advance signal processing to extract FOETAL
signal from maternal signal & other noise to gain a bur
FECG Signal .
The normal Electrocardiogram signal is (PQRST) signal.
The PQRST complex is an electric signal.
Produced by the contraction of the heart’s muscle
called myocardium. It is composed of three parts: the P-
wave reflects the contraction of the auricles; the QRS-
complex is associated with the contraction of the ventri-
cles. Due to the magnitude of the R-wave, it is extremely
reliable; the T-wave, which corresponds to the depolari-
zation phase which follows each heart contraction. The
delay associated to the R-R interval leads to the heart-
beats frequency. Traditionally way of providing medicine
services is to transmit pregnancy women to a hospital or
to transmit biomedical signals from a pregnancy women
to a hospital using “landlines,” such as the Public
Switched Telephony, the last is advance for transmit the
signal to hospital called telemedicine. [3] Network (PSTN)
and the Integrated Serv ices Digital Network Most current
telemedicine applications are limited to communications
between fixed locations, often with conventional hand-
sets. The adoption of mobile technology has led to new
m-Health applications in health-care provision [4]. Al-
though face-to-face consultation s between a clinician and
pregnancy women will never be replaced, there are medi-
cal cases that can be managed more efficiently by adopt-
ing wireless telemedicine. Potential mobile applications
include remote routine checkups, emergency and rescue
situations, and sports science physiological measure-
ments [10]. Medical services can now be delivered to any
location within the cov erage of cellular netwo rks, su ch as
the global system for mobile (GSM) communications[9].
Pregnancy women from a rural area just like rural in Su-
dan could be given a routine check by mobile phone
without having to commute regularly to a hospital. Rou-
tine inspections and monitoring could be done while the
pregnancy women is at home, traveling, at work, or at
leisure, thereby relieving resources for more demanding
hospital cases. Advances in mobile technology have
made wireless telemedicine more practical both within
hospitals and globally [3].
3. Design Considerations
The desired end product is a sensor mobile telemedicine
processor to collect sample of combine ECG signal
(FECG+MECG+Noise ) and transmit up to four channels
of biomedical signals via a cellular network.
The design of the processor must be “future-proofed,”
so that future processing and memory upgrades can be
achieved simply by software changes. The hardware is,
therefore, implemented with a programmable logic de-
vice (PLD), and a programmable, multichannel analog-
to-digital converter (ADC) allows data to be handled for
high-band wi dth third- generation (3 G) networks.
The connection between the processor and the mobile
telephone is implemented with a Bluetooth master–slave
link.
Alignment between the processor and the telephone is,
therefore, not critical, unlike with an infrared link.
The connection between the telephone and the hospital
uses the General Packet Radio Service (GPRS) [8],
which allows simultaneou s d ata and voice transmission.
The Figure 1 has shown the following links,
1) Short-range Bluetooth link between pregnant
women and doctor telephone.
2) GPRS to a base station and to other mobile tele-
phones;
3) land lines (PSTN) and an Internet service provider
(ISP) to a hospital server and database.
4) LAN to clinicians (doctor laptop).
4. Limitation of the System
Bluetooth 128 bit authentication key and 8 bit encryption
key [5]. WLAN wired equivalent privacy (WEP) proto-
col with RC4 encryptions algorithm [8] .GPRS three-tier
security with A3 algorithm for user authentication A8
ciphering key generating algorithm and A5 ciphering
Algorithm for data Encryption.
3G f8 UMTS confidentiality algorithm and f8 UMTS
Integrity Algorith m
The rationale for choosing the components of this sys-
tem is now explained. The methodology includes consid-
eration of a combination of wireless techniques, particu-
larly the exploitation of cellular networks, types of clinical
data for transmission, and system memory storage.
Figure 1. Diagram of the Bluetooth medical service.

M. A. S. ALI 65
For Bluetooth Data Transmission we choose BlueSmirf
module provided by Sparkfun Electronics [6]. It is a class
1 model that has an approximate range of 100 meters.
The asynchronous data from the electrode are delivered
to the BlueSmirf Bluetooth module on the serial port.
The Bluetooth module is configured as a slave and the
mobile phone is considered to be functioning as a master.
The signal acquisition unit sends data to the Bluetooth
module, which transmits data continuously, in blocks of
FECG samples plus, the data are sent as raw binary bytes.
The electrocardiogram data for human assumed to be
Familiar for all researchers of biomedical signal, the Ta-
ble 1 Below described all types of biomedical data.
In the case of the design of sensing devices, focus on
Bluetooth Telemedicine Processor for Multichannel
Biomedical Signal Transmission via Mobile Cellular
Networks which samples signals from sensors on the
patient [11], It then transmits digital data over a Blue-
tooth link to mobile telephone and then through system
to the doctor .The system that uses General Packet Radio
Service (GPRS). The used Bluetooth is a universal short-
range low-power radio protocol operating in the unli-
censed industrial, scientific, and medical frequency band.
It allows both data and voice connections, the modulation
technique is Gaussian frequency-shift keying, with
transmission at a rate of 1 Msymbol/s on one of 79
channels with 1-MHz spacing in the 2.402–2.480-GHz
band. Bluetooth uses a spread-spectrum frequency hop-
ping [7] connection with a rate of 1600 hops/s and its
radio transceivers are categorized in three power classes,
as shown in Table 2 below.
Table 1. Biomedical data.
Biomedical data Type Typical file size
ECG Record Electrical signal 100 KB
Electronic stetho scope Audio 100 KB
X-ray Still image 1 MB
30s of ultrasound image Moving image 10 MB
Table 2. Power class and range.
Power
Class Maximum Outputs Power Typical Range
1 100 mW (20 dBm) 100 m
2 25 mW (4 dBm) 20 m
3 1 mW (0 dBm) 10 m
The hardware is, therefore, implemented with a pro-
grammable logic device (PLD), and a programmable,
multichannel analog-to-digital converter (ADC) allows
data to be handled for high-bandwidth third-generation
(3G) net- works. The connection between the processor
and the mobile telephone is implemented with a Blue-
tooth master–slave link, the figure below show all mobile
telemedicine system and the alignment between the Blue-
tooth processor and mobile telephone.
5. Materials and Method
FECG signal is not ready signal to be taken ,it needs
some extraction method to prepare before sending, In this
study there are two models, presented the model for signal
taken by Thoracic Electrodes 1()
x
t which record Tho-
racic Electrocardiogram (TECG), represented by 1()
s
t
and the model for signal taken by Abdominal Electrodes
2()
x
t which record Abdominal Electrocar-diogram
(AECG) represented by 2()
s
t
As mentioned above the foetal ECG is very weak
among the maternal ECG and noise. For this reason the
Significant recent advances in the field of statistical sig-
nal processing should be brought to the attention of the
biomedical engineering community. Algorithms have
been proposed to separate multiple signal sources based
solely on their statistical independence, instead of the
usual spectral differences. These algorithms promise to:
• lead to more accurate source modeling,
• More effective artifact rejection algorithms
Independent Component Analyses (ICA) & Blind
Source Separation (BSS)(see Figure 2 Below ) are be-
coming very popular for extraction FECG signal from
the AECG. The use of these techniques for extraction of
FECG in noninvasive methods just need amount of elec-
trode for record signal from abdominal.
Figure 2. Indepe nde nt component analysis ( ICA).

M. A. S. ALI
66
Figure 3. Shows the abdominal records and pure foetal sig-
nals.
Already the records of the maternal Thoracic Electro-
cardiogram (TECG) signals are known and the record s of
the Abdominal Electrocardiogram (AECG) which contains
foetal ECG + maternal ECG represent the complex part.
6. Results
The maternal ECG signal interference was canceled from
the foetal heart ECG signal .ECG signals are given as an
input and is simulated using MATLAB. The results of
programs are shown in the Figure 3. These results rep-
resent the raw data of PQRST and U complex signal re-
corded from maternal abdominal and pure PQRST com-
plex signal for Fetus after extraction , this results should
be appeared at doctor laptop computer or mobile phone
7. Discussion
Figure 3 in results above contains two graphs. The upper
graph shows AECG and FECG recombined after being
extracted. In comparing these two signals we can see the
region of FECG in AECG signal. The lower graph shows
pure FECG. The amplitude of QRS of FECG is about 30
microvolt while that of MECG is about 150 microvolt.
These values may agree to the value of QRS amplitude.
The obtained result shows the effectiveness of the pro-
posed algorithm.
8. Conclusions
It can be assumed from this study that the goal of Sensor
mobile telemedicine is to record real accurate electrocar-
diogram data and encapsulate then transmit to the target
safety without any interference. It is important to under-
stand the actual though process over several wireless
systems of individual technique which have different
attachment styles. It is easy to transfer this idea to sense
and monitoring one of very important issue that is foetal
electrocardiogram (FECG), but the weakness of FECG
signal need more security technique to avoid interference
of the noises.
The algorithm used in this study is v ery simple an d not
complex. The performance and validity of the proposed
algorithm have been confirmed by computer simulations
and experiment on real-world ECG data. The data used
here is public databases widely used by the signal proc-
ssing community known as SISTA/DAISY dataset. The
result which was obtained appears to agree with the
standard Foetal ECG signals. The Researcher recom-
mends following this method to gain more useful results.
9. Acknowledgements
This work was supported in part by university of Nyala
Republic of the Sudan. Many thanks to the University of
Nyala for offering me the fund for this conference.
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