Continuously Monitoring Foetal ECG using Mobile Telemedicine Sensor Network

doi:10.4236/eng.2013.55B013

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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

Email: [email protected]

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

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()

t which record Tho-

racic Electrocardiogram (TECG), represented by 1()

and the model for signal taken by Abdominal Electrodes

2()

t which record Abdominal Electrocar-diogram

(AECG) represented by 2()

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

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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