Effect of noise on the electrocardiographic parameters

European Journal of Preventive Cardiology

11 May 2022
Organised by: Logo
ESC Journals

Abstract

AbstractFunding Acknowledgements

Type of funding sources: None.

Purpose

Noise, defined as any sound that is unpleasant, is one of the most important environmental problems. Prolonged exposure to noise has been shown to be associated with the development of cardiovascular diseases. No study investigated the effect of noise on surface electrocardiography (ECG).

Aims

The aim of our study is to investigate the effect of noise on surface ECG parameters including P wave dispersion (PWD), QT intervals, corrected QT interval (QTc), T wave peak to end (Tp-e) interval, and Tp-e/QT and Tp-e/QTc ratios.

Methods

A total of 51 people working in the textile factory affected by the noise and 43 volunteers without any disease and who were not exposed to noise were included in this study. The average noise level in the textile factory was 112 dB. A 12-lead ECG was obtained from all individuals. PR interval, PWD, QRS duration, QT interval, QTc interval, Tp-e interval, and Tp-e/QT and Tp-e/QTc ratios were calculated for all individuals.

Results

The noise group had significantly increased PWD [35 (28-40) vs. 28 (22-36) p= 0.029], QT interval ( 373.5±27.3 vs. 359.3±2.74, P=0.001), QTc interval [(409±21 vs. 403 ± 13 P= 0.045)], Tp-e interval [(90.6 ± 6.0 vs. 83.5±7.3 P<0.001)], Tp-e/QT [(0.24±0.03 vs. 0.23±0.02, P=0.015)] and Tp-e/QTc [(0.22±0.02 vs. 0.21±0.02 P<0.001)] compared to control group. Also, duration of working was positively correlated with PWD (r=0.468, P =0.001) and Tp-e/QTc ratio (r=0.328, P=0.019). In multivariate linear regression analysis, noise was the independent predictor of both PWD (β=0.244, P=0.032) and Tp-e/QTc (β=0.319, P=0.003).

Conclusion

We showed that noise significantly increased PWD, QT and Tp-e interval measurements. Also, noise was the independent predictor for both PWD and Tp-e/QTc.

Basal characteristics and ECG of groups

Univariate linear regression analysis

Contributors

M Tascanov
M Tascanov

Author

Harran University Sanliurfa , Turkiye

3 Tanriverdi
3 Tanriverdi

Author

Harran University Sanliurfa , Turkiye

5 Altiparmak
5 Altiparmak

Author

Harran University Sanliurfa , Turkiye