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CARDIAC HEALTH STATUS
IMPLEMENTATION ON MOBILE PHONES
By-Prasad Pomaji
Abhinav Sharma
Bhagyashri Samanta
Tejashree Chhajed
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Cardio Vascular Diseases
Heart disease or cardiovascular
disease are the class of diseases that
involve the heart or blood vessels.
30 percent of all deaths worldwide,
making it the single leading cause of
death.
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Diagnosis Tools
Electrocardiogram (ECG) is the mostwidely used method for diagnosingcardiovascular disease.
ECG measures the electrical impulsesthat travel through the heart,determining its rate and rhythm.
It can be used to spot coronary problems
such as heart attacks, abnormal heartrhythms, and reduced blood supply tothe heart and electrolyte disturbances.
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Limitations of current diagnosis
methods
Most electrocardiogram machines used
in hospitals and clinics today are
stationary thereby the treatment comes
too late.
Moreover checkups too at hospitals and
clinics have become very expensive.
Todays lifestyle just do not allowpeople to have a spare time, especially
for a routine checkup.(negligence)
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A More Versatile Solution
Cell Phone-BasedMonitoring of ECG data
Features: It collects the ECG data also analyzes it
to detect cardiac abnormalities orpossible cardiovascular conditions.
Low-cost. Minimizes delay which arises by
conventional methods.
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Acquired signal
We study the signal from MIT-BIH database
(Massacheutts Institute of Technology-Beth Israel
Hospital )
Eg. Signal:
Elapsed time ECG ECG
hh:mm:ss.mm (mV) (mV)
0:00.000 -0.207 -0.052
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Overview
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Signal pre-processing
Sampling ofsignal
Noises
Removal of base
line drift
Filtering
Adaptive
filteringtechniques
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Adaptive filtering techniques
Adaptive self tuning filter.
An adaptive self tuning filter is a digital filter with self
adjusting characteristic and in-built flexibility.
Uses LMS algorithm.Bandpass filter.
This filter is a combination of low pass and high pass filter.
Median filter.
It suppress isolated noise without blurring sharp edges.
These algorithms are implemented and results are compared
and best result is chosen.
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QRS detectionMost important waveform.
Is a fundamental algorithm for all
ECG features.
Detection algorithms:
Algorithm based on amplitude and
First derivative only.
Algorithm based on first derivative
only.
Algorithm based on first and
second derivative.
Results of all above algorithms are
compared and a solution is
obtained.
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Feature Extraction
Two main
categories:Morphological
features.
Statistical features.
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Feature Extraction
Morphological features
QRS area
QRS duration
R-R interval
PR interval
R wave amplitude
RT interval
QT segment
ST interval
Statistical features
QRS energy
Auto-correlationcoefficient
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Signal classification
Signal classification is comparing with expert rules.
The extracted features from the acquired signal iscompared with standard rules.
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PROBLEM STATEMENT
Cardiac Health Status Implementation on
Mobile phones.
Feasibility:
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Mathematical Model S= {Q, I, R, F}
Q: Set of Input
I: Initial State R: Intermediate State
F: Final State
Q={x: Set of Input from MIT-BIH database}
I= {A, V, B}
A={x, y: Plot (x,y) Voltage vs Time}
V={x: Adaptive Filtering Technique}
B={x: Base Line Drift Removal}
R= {M, N, O}
M= {R1, R2, R3, R4, R5}
R1={x: QRS Interval Calculation}
R2={x: QT Interval Calculation}
R3={x: ST Segment Calculation}
R4={x:RR Interval Calculation} R5={x:PR Interval Calculation}
N={x: Feature Extraction}
O={x:ANN Signal Classification}
F={x,y: Disease Classification from Z }
Z={x: Ischaemia, Hypoglycaemia, Heart Beat Rate(HBR),
Myocardial Ischaemia}
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UML Diagrams
Usecase
diagram:
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Class diagram
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State Machine diagram
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Sequence Diagram
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Deployment Diagram
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Interaction Diagram
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Test Cases Purpose: To find proper input connection from database.
Expected Output : Find the input and plot the ECG graph.
For Test Case 1:A check is performed to validate that a proper connectionbetween the database and system is maintained to avoid unrealistic values.
Purpose: Noise is introduced into the input making it unreadable.
Expected Output : A proper filtering technique is to be performed for each of the
algorithm.
For Test Case 2:Appropriate action is needed to be taken to avoid noisefrom affecting the systems performance. If the input values cross certain
limitations, the input is skipped to maintain a real time system.
Purpose: To recognize patterns and perform feature extraction and
classification.
Expected Output : Appropriate ECG features are to be studied for different
algorithms.
For Test Case 3: Each pattern of wave values is recorded and studied andcompared with the expert rules.
Purpose: To check efficiency of the system.
Expected Output : The efficiency is checked by employing different algorithms
for input set to generate a more accurate result.
For Test Case 4: System algorithms have been selected to make our system
efficient for optimal and better performance.
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Platform TechnologyThe project deals with implementing ECG system on Mobile phones:
1) Preprocessing:
This techniques will use the electronic components which a filter
uses and all the low frequency and high frequency sound is
removed.
2) Feature Extraction:
Using clustering algorithms .
The technology should provide us with adequate facilities for
parallel processing and finding a high quality solution.
The main focus is on real time processing.
3) Signal classification:
In this step computerized Expert rules are used and classification
of signal is done in order to diagnose the patient.
All the three steps are done on mobile platform using object oriented
languages which a phone uses.
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Project planWaterfall Model :
Requirements specification:
Mobility in systemEasy User Interface
Low Price
Working should be accurate
Should raise an alarm
Design:Hardware specification: This system uses a mobile phone.
User Interface: The user interface should be very easy to use by
any individual.
Price: Low.
Working: The system uses clustering algorithms, Expert rules, databases, internet
and wireless phones to make it a success.
Implementation: Using Object Oriented language C++.
Testing: Unit testing ,System testing
Maintenance :Software U dation time to time.
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Applications
Can be used on all generic mobile phones.
As it has no restricted coverage area hence can be
used within global coverage area.
Can be used to detect all heart related diseases
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Limitations
Security
Security threats
Wireless transmiision threats
Time Lag in processing
In parrallel processing
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Conclusion
Ease of Patients.
Ease of Doctors.
Mobility and Flexibility of device.Error free Proccesing.
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Future Work
Explore the correlation among medical data to
reduce the false positive rate.
We plan to apply the approach to differentmedical contexts.
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References1. Lacramioara Dranca, Alfredo Goni and Arantza Illarramendi , Using decision
trees for real-time ischemia detection, 19th IEEE Symposium on CBMS'06.
2. Cuiwei Li , Chongxun Zheng , Changfeng Tai , Detection of ECG characteristic
points using wavelet transforms, Jan. 1995 IEEE Transactions on Biomedicaltechnology.
3. K. W. Goh, ,J. Lavanya, Y Kim, E. K. Tan and C. B. Soh , A Pda-Based Ecg
Beat Detector For Home Cardiac Care, 2005 IEEE Engineering in Medicine and
Biology.
4. Daniele Apilette and Elena Baralis, Real time analysis of physiological data to
support medical applications, 2009 IEEE transactions on Information technology
in biomedicine.5. Zetao Lin, Yaozheng Ge and Guoliang Tao Algorithm for Clustering Analysis of
ECG Data, Proceedings of the 2005 IEEE, Engineering in Medicine and Biology
27th Annual Conference.
6. Yuliyan Velchev and Ognian Boumbarov, Wavelet Transform Based ECG
Characteristic Points Detector, International Scientific Conference Computer
Science2008.
7. S. S. Mehta and N. S. Lingayat, Detection of P and T-waves in
Electrocardiogram, Proceedings of the World Congress on Engineering andComputer Science 2008.
8. P. Hamilton, Open Source ECG Analysis,Computers in Cardiology,2002 IEEE.
9. Jiapupan and Willis J. Tompkins, A Real-Time QRS Detection Algorithm, IEEE
Transactions on Biomedical Engineering, March 1985.
10. K. W. Goh, J. Lavanya, Y. Kim, E. K. Tan and C. B. Soh, A PDA-Based ECG
Beat Detector for home Cardiac care, Proceedings of the 2005 IEEE,
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