A Novel Approach for Modelling the Relationship between Blood Pressure and ECG by using Time-series Feature Extraction

This paper addresses the ECG-blood pressure relationship a fact that physicians have discussed for years. The hypothesis set in the paper is that blood pressure is related to electrocardiogram (ECG) and that the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values can be predicted by using information only from a given ECG signal. Therefore, we established a protocol for creating a database considering measurements from real patients in ambulance environment, and consequently developed methodology for analysing the collected measurements. The proposed methodology follows two steps: i) first the signals are considered as time series data, and ii) a time series feature extraction method is applied to extract the important features from the ECG signals. Hereafter, a novel Machine learning method is applied (CLUS) that produced best results among the traditionally-used Machine learning methods. The best results obtained are 12.81 ± 2.66 mmHg for SBP and 8.12± 1.80 mmHg for DBP. After introducing calibration method the obtained mean absolute errors (MAEs) reduced to 6.93 ± 4.70 mmHg for SBP, and 7.13 ± 4.48 mmHg for DBP. Given the latest literature, the results are appropriately compared and confirm the relation between the ECG signal and

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