Key result
The MATLAB-based digitization tool successfully reconstructed paper-based ECG signals with an average correlation of 0.952 compared to the original digital signals.
Why the study?
Applying machine learning to improve ECG diagnostic accuracy requires transforming existing scanned or printed ECGs into raw digital signal formats.
Effect estimate: Mean correlation 0.952
p-value: p=<0.001
A novel MATLAB-based tool can accurately digitize paper-based ECG records with >95% precision, facilitating the integration of historic ECG data into machine learning models.
May enable legacy ECG reuse in ML research; leaves open prospective validation before clinical adoption.
INTRODUCTION: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. OBJECTIVES: We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. METHODS: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. RESULTS: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/-0.021 (p-value < 0.001), QRS interval 1+/- SD (p-value < 0.001), QT interval 0.981 +/- 0.023 p-value < 0.001, and RR interval 1 +/- 0.001 p-value < 0.001. CONCLUSION: Digitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease.
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Baydoun et al. (2019) studied Electrocardiogram (ECG) digitization (n=30). MATLAB-based ECG digitization tool vs. Original digital ECG signal was evaluated on Correlation between original and digitized ECG signals (Mean correlation 0.952, p=<0.001). The MATLAB-based digitization tool successfully reconstructed paper-based ECG signals with an average correlation of 0.952 compared to the original digital signals.
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