PQRST wave detection on ECG signals
Putri Madona1, Rahmat Ilias Basti1, Muhammad Mahrus Zain2
1Department of Electronics Engineering, Politeknik Caltex Riau, Riau 28261, Indonesia.
Insights
This study accurately detects heart abnormalities using electrocardiogram (ECG) signal analysis. Feature extraction from PQRST intervals aids in early heart disease diagnosis and reduces mortality risk.
Area of Science:
- Biomedical Engineering
- Cardiology
- Signal Processing
Background:
- Electrocardiogram (ECG) signals are crucial for diagnosing heart conditions.
- Abnormalities in the PQRST interval can indicate underlying heart disease.
- Early detection of heart disease is vital for preventing mortality.
Purpose of the Study:
- To develop a method for detecting heart disease through ECG signal analysis.
- To accurately identify key components of the PQRST interval.
- To establish a preliminary diagnosis tool for heart health.
Main Methods:
- Data acquisition from 18 subjects over 2-minute recordings.
- Signal preprocessing of ECG data.
- Feature extraction focusing on P waves, QRS complexes, and T waves.
Main Results:
- High accuracy in detecting P waves (98.31%) and T waves (97.99%).
- Excellent accuracy for QRS complex detection (Q: 98.7%, R: 99.12%).
- S wave detection achieved 86.27% accuracy.
Conclusions:
- Feature extraction effectively identifies P waves, QRS complexes, and T waves.
- The method is capable of assessing heart rate.
- This approach supports preliminary diagnosis of heart conditions.
Objective:
One way of detecting the heart disease is to determine the presence of abnormalities in PQRST interval on ECG signals. Therefore, it is expected to be used as a preliminary diagnosis of heart health and to prevent or decrease the mortality rate due to heart attack.
Methods:
This paper uses three main processes: data acquisition, signal preprocessing, and feature extraction. The experiment was done to eighteen subjects recorded for 2min in a relaxed condition to obtain P wave points, QRS complexes, and T waves.
Result:
Based on the data obtained from the 18 subjects, the average accuracy of point P detection is 98.31%, point Q=98.7%, point R=99.12%, point S=86.27%, and point T=97.99%.
Conclusion:
The extraction of used features proved capable of detecting P waves, QRS complexes, T waves, as well as the amount of heart rate on all subjects.
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