| It has been estimated
that 20% of the global population suffer from Cardiovascular
Disease (CVD), with around 22 million people worldwide
suffering from heart failure. Amongst these 22 million,
an estimated 400,000 are Americans while in Singapore;
there are around 300 sudden cardiac death cases each
year.
Due to the rise in medical fees in recent times, people
suffering from CVD would opt to be outpatients and go
for regular check-ups in the midst of their daily life.
This is a cause for concern as it has been found that
such check-ups do not provide a thorough view on the
outpatient’s state of health. This is due to the
fact that the electrocardiogram (ECG) data collected
during this short period of time is not comprehensive
enough to show any abnormalities of the outpatient as
ECG abnormalities only occur when the patient is suffering
from a cardiac problem. This is the one problem that
needs to be alleviated in conventional clinical examination.
Other alleviations include the assistance to capture
rare events that may provide better diagnostic and/or
prognostic therapeutic importance as well as the facilities
to measure the outpatient’s physiological responses
during his/her normal daily life. Lastly, also the aid
to capture the circadian variation in physiology signals
that reflects the progression of the disease. Today,
hospitals use the “Holter System”, which
records the outpatient’s data for 1 to 3 days,
after which it is returned for offline analysis. In
this situation, the doctor will be unable to comprehend
the outpatient’s status during his/her use of
the “Holter System”.
The solution to this is ambulatory and continuous detection
of heart abnormality and what better than a PDA or cellular
phone that has the ability to continuously collect and
process ECG signals, regardless of the availability
of telecommunication services! The challenge, of course,
is that the outpatient’s ECG and activity signals
are subjected to the noise caused by their movement.
It is thus crucial to get good quality signals and effectively
suppress noise for accurate and reliable processing.
This proposed mobile healthcare framework will also
alert and transmit critical data to the relevant healthcare
professionals when it detects cardiac abnormalities.
It works by first, capturing, processing and analyzing
ECG, activity signals and gait continuously with an
accelerometer sensor in real time. This valuable data
generated from the context will be used for ambulatory
and continuous monitoring and examination of outpatients.
It is also personalized to the CVD persons through fusion
of multiple sensors data. This is stored in the mobile
device and synchronized with the central database where
doctors may refer to the data for diagnostic purposes.
To save transmission cost and prevent network congestion,
only critical cardiac abnormalities will be transmitted
upon detection. Upon receiving the data, the physician
will verify and diagnose on the risk factor and decide
on whether immediate action is required. Otherwise,
the physician will instruct the outpatient via the message
portal on their user interface without breaking the
continuous detection.
Functionality
The MobiCare system is a perfectly weaved network which
consists of the mobile phone, wireless ECG sensor, web
server, patient’s database and user interface.
The wireless ECG sensor captures one ECG channel and
one 3D accelerometer signal. These signals will be transmitted
via Bluetooth to the mobile phone. The mobile phone
is embedded with a software known as MobiECG which serves
to process the data received in real time. Upon receiving
the data, the MobiECG will begin processing the ECG
Signals. If there is any abnormal data detected, it
will send the data over the cellular network (GPRS/3G)
to relevant hospitals or healthcare centres. Here, the
abnormal data only consists of a fixed amount of time
before and after the detection is transmitted. This
is to provide a concise summary of the data that has
contributed to the detection of abnormality.
During the transmission, a self-designed communication
protocol known as the Medical Data Transmission Protocol
is used to prevent the case of inadequate data. This
protocol is scalable in that it will detect the bandwidth
that is required for transmission via the cellular network
to a web server. The web server will in turn send the
data to hospitals and healthcare centres. The data will
be examined by the relevant physicians via an user interface,
which would determine if the risk level of the person.
At the same time, the data transmitted is also logged
into a database deployed to record the patient’s
personal particulars, medical history and ECG data logs.
Technological Aspect
There are several reasons for deciding to use a mobile
phone to process the ECG data and for not using a server
to transmit the processed data. Firstly, it is necessary
for the system to be self-sufficient and independent
on network communications that may fail. A mobile phone
is one such technology that allows the outpatient to
be constantly updated on his heart activity at anytime
and any place. Secondly, it will prevent network congestion
as data will not be constantly transmitted to the server
by several outpatients. This is crucial as real time
transmission is necessary when detecting genuine abnormality.
As mentioned above, the MobiECG processes the data
received in real time and will transmit any detected
abnormality to hospitals or healthcare centres via cellular
network. It does so by processing the ECG and 3D accelerometer
signals from the wireless ECG sensor. It will filter
the data, detect the QRS complex, identify the Q onset
and T offset, as well as calculates the intensity of
the patient’s movement using the obtained accelerometer
data. By using special algorithms to compare the patient’s
activity (being context aware) and process the ECG data,
MobiECG will then send the detected abnormal ECG data
to the hospitals or care centres up.
On the MobiECG, the received ECG and accelerometer
data from the ECG sensor are displayed as graphs. The
heart rate and QT intervals are depicted as readable
measured values and marked on the graph as red and green
lines respectively. The software also displays the detected
conditions of Atrial Fibrillation (AF) and Maximum Heart
rate (MH). The body movement intensity can be seen on
the accelerometer graph. Besides that, there is also
a portal for the software to inform the user of any
abnormalities detected by the software or instructions
as disseminated by the clinicians.
Medical Data Transmission Protocol is a special packet
structure formulated for the delivery of the abnormal
data to the clinician. This structure is scalable and
it can be revised to ensure that maximum amount of data
is delivered without compromising the freshness of the
data.
Lastly, the MobiCare Server acts as the centralized
information exchange. The data transmitted via GPRS/3G
is collected by the server, where the clients of the
MobiCare Server can subscribe to. Clients would need
to state their preference so that the corresponding
feeds will be controlled and streamed to them by the
server. The server also acts as a central communication
facility between the patient and clinician which can
allow immediate follow-ups procedures. The server also
provides a web based and lightweight user interface
which allows the clinician to access the feeds from
any computer terminals. The user interfaces provides
2 main graphs and 2 auxiliary graphs; one main graph
that shows the overall ECG wave pattern received at
the moment, while the second main graph shows a modified
ECG graph paper which facilitates the clinician to verify
and calculate the RR internal of the ECG pattern. The
first auxiliary graph depicts the heart rate variability
over time and the seconds depicts the body movement
over time. The 2 auxiliary graphs serves to provide
a more comprehensive of what a patient was doing when
an episode occurs. It also displays other information
such as the body movement intensity reading, heart rate
and the QT interval reading
Conclusion
Through this mobile healthcare framework, crucial ambulatory
and continuous monitoring & examination can be achieved.
This will significantly contribute to early detection
of high risk cardiac in problems and thus, reducing
hospitalization rate. It also enables hospital to pick
up critical data in time and response accordingly so
that patients can be treated as soon as possible. It
is the hope that this framework will be able to improve
healthcare treatments and be integrated into a global
mobile healthcare framework.
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