Baydoun Mohammed, Safatly Lise, Abou Hassan Ossama K, Ghaziri Hassan, El Hajj Ali, Isma'eel Hussain
1Beirut Research and Innovation CenterBeirut2052 6703Lebanon.
2Electrical and Computer Engineering DepartmentAmerican University of BeirutBeirutLebanon.
IEEE J Transl Eng Health Med. 2019 Nov 7;7:1900808. doi: 10.1109/JTEHM.2019.2949784. eCollection 2019.
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.
We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal.
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.
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.
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.
心电图（ECG）在心脏病诊断中起着重要作用。然而，大多数疾病模式基于旧数据集和逐步算法，其准确性有限。应用机器学习算法可提高心电图的诊断准确性。这需要获取旧队列现有的扫描或打印心电图，并将心电图信号转换为原始数字（时间（毫秒），电压（毫伏））形式。
我们展示一种基于MATLAB的工具和算法，可将打印或扫描格式的心电图转换为数字化心电图信号。
我们的研究使用了30条心电图扫描曲线。首先实施一种图像处理方法来检测心电图感兴趣区域并提取心电图信号。随后是将结果数字化并验证的一系列步骤。
验证显示几个标准心电图参数具有非常高的相关值：PR间期0.984 +/- 0.021（p值<0.001），QRS间期1 +/-标准差（p值<0.001），QT间期0.981 +/- 0.023 p值<0.001，RR间期1 +/- 0.001 p值<0.001。
从现有的纸质或扫描心电图中可获得精度超过95%的数字化心电图信号。这使得在机器学习算法中利用历史心电图信号来识别心脏病模式并辅助心血管疾病患者的诊断和预后评估成为可能。
