Abstract
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.
Highlights
The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases
Most patterns of cardiac diseases are based on old datasets and stepwise algorithms through interpretation of ECG findings
The proposed method was validated using a database of thirty ECG signals extracted from a publicly available online dataset through Physionet [16]
Summary
The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. 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. 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. Most patterns of cardiac diseases are based on old datasets and stepwise algorithms through interpretation of ECG findings. Current digitized waveforms pick up on objective parameters such as PR, QRS, QT intervals, ST elevation or depression and others, as well as advanced readings such as the morphology of the T-wave or the spatial QRS-T angle and many others previously overlooked
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