Authors:
Stefan Kochev
1
;
Neven Stevchev
2
;
Svetlana Kocheva
3
;
Tome Eftimov
4
and
Monika Simjanoska
1
Affiliations:
1
Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Rugjer Boshkovikj 16, 1000 Skopje, North Macedonia
;
2
PZU d-r Andon Kochev, Javor bb, Radovish, North Macedonia
;
3
Medical Faculty, Ss. Cyril and Methodius University, 50th Division 6, 1000 Skopje, North Macedonia
;
4
Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
Keyword(s):
ECG, Blood Pressure, Machine Learning, Feature Extraction, Time-series Analysis.
Abstract:
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
the blood pressure.
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