Brief Methodology of detecting a myocardial Infarction using ECG.

1. Detection of Myocardial
Infarction using single lead
ECG
Submitted By : Project Supervisor
Rudra Narayan Dash(2001106417) Dr. Kanhu Charan Bhuyan
Rishikant Sahu(2001106414)
Lanin Pradhan(2001106393)
Jyoti Nanda(2001106390)

2. Content
1. Introduction
2. Research Significance
3. Data Acquisition and Processing
4. Machine Learning Model
5. Results
6. Conclusion and Future Work

3. Introduction
Background
In this project, we develop a system that takes the ECG signal of a patient, removes the noise affecting
the recording, decompose each recording into sub-bands and then, post extraction of statistical
features,we train a machine learning base classifier, with the features as the training data. Finally, using
the classifier, we predict whether the patient is suffering from a heart attack or not.
Purpose
To explore the ability of a single-lead ECG to accurately detect myocardial infarction, enhancing rapid
diagnosis and treatment interventions.
Methodology
The study combines data acquisition techniques with advanced signal processing and machine learning
models, specifically using LSTM networks, to analyze ECG signals.

4. Myocardial Infarction
● Myocardial Infarction represents a critical medical emergency characterized
by the abrupt and often catastrophic restriction or occlusion of blood supply to
a segment of the cardiac muscle.
● This dire scenario unfolds as a consequence of the formation and subsequent
lodging of a blood clot within one of the coronary arteries, pivotal conduits
responsible for delivering oxygen-rich blood to the heart tissue.

5. Electrocardiogram
● Electrocardiogram or ECG (also known as EKG) is a recording of a heart’s
electrical activity through repeated cardiac cycles.
● It is usually represented as a graph of voltage versus time of the electrical
activity of the heart using electrodes placed on the skin.
● These electrodes detect the small electrical changes that are a consequence
of cardiac muscle depolarization followed by repolarization during each
cardiac cycle (heartbeat).

6. Standard ECG for a single heart beat
Fig 1 : A standard ECG signal for a single heart beat

7. ST-Elevation Myocardial Infarction
● STEMI, or ST elevation myocardial infarction, stands as a critical
manifestation of cardiovascular disease, representing a dire scenario wherein
one of the coronary arteries, responsible for supplying oxygen-rich blood to
the heart muscle, becomes acutely obstructed.
● This occlusion often stems from the rupture of an atherosclerotic plaque,
precipitating the formation of a blood clot that swiftly impedes blood flow.

8. Non ST-Elevation Myocardial Infarction
● Non-ST elevation myocardial infarction is a specific type of heart attack
characterized by the partial blockage or narrowing of one or more coronary
arteries, leading to a decrease in blood flow to a section of the heart muscle.
● In contrast to ST elevation myocardial infarction, where there is a complete
blockage of a coronary artery, NSTEMI involves a milder obstruction, often
caused by the accumulation of fatty deposits within the artery walls, known as
atherosclerosis.

9. Research Significance
Consequences of Delayed Detection
Delayed diagnosis of Myocardial Infarction (MI) can lead to severe complications
including cardiogenic shock and death. Early detection is crucial for immediate
treatment.
Benefits of Single Lead ECG
Single lead ECGs are more portable and easier to use than traditional multi-lead
systems, allowing quicker and more accessible evaluations.

10. Data Acquisition and Processing
The Analog Front-End
Utilizes an AD620 instrumentation amplifier to capture and amplify ECG signals.
Signal Processing Techniques
Incorporates Hamilton-Tompkins segmentation and Fourier Decomposition for
noise reduction and signal clarification.

11. Machine Learning Model
Use of LSTM Networks
Long Short-Term Memory (LSTM) networks were trained to classify ECG signals
into indicative of MI or not. The system achieved a high accuracy with an AUC
score of 0.9402.
Statistical Feature Extraction
Critical for training the machine learning model by quantifying key aspects of
processed ECG segments.

12. Block Diagram of Hamilton Tompkins algorithm
Fig 2 : Block Diagram of Hamilton Tompkins algorithm

13. Hardware Model (Schematic)

14. Results
Performance Metrics
The model showed high precision (87.50%), accuracy (86.61%), recall (86.56%),
and F1 score (87.03%).
System Efficiency
Demonstrated potential for real-time medical diagnostics, enabling timely
interventions.

15. Conclusion
The study successfully demonstrated that a single-lead ECG system, equipped
with advanced signal processing and LSTM-based classification, can effectively
detect myocardial infarction.

16. Future Work
Future research will focus on improving the model's sensitivity and exploring its
adaptability to other cardiovascular conditions.

17. References
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Aug.1987.
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faculty. Lippincott Williams & Wilkins, 2011.
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Available: http://dx.doi.org/10.1161/CIR.0b013e31826e1058

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19. Thank You