Respiratory Analysis with Electrocardiogram Data: Evaluation of Pan-Tompkins Algorithm and Cubic Curve Interpolation Method
Abstract
Advancements in bioinstrumentation have facilitated the easier monitoring of biometric signals such as electrocardiogram (ECG) and respiration. This development is particularly crucial for the diagnosis and management of various conditions like stress and sleep disorders. Two commonly used features in heart rate variability (HRV) analysis derived from ECG data are standard deviation and serial correlation coefficients of R-R intervals (the time durations between heartbeats). The former utilizes the fundamental components of QRS complexes, while the latter is designed to extract relationships between respiration and heart rate. In the proposed methodology, R-R wave detection is performed on processed ECG data using the Pan-Tompkins algorithm, and the respiration duration for each R-R interval from respiration data is selected. Additionally, missing respiration data for selected R-R intervals is interpolated based on the interpolation method. The results of this study are compared with the standard interpolation and cubic spline interpolation models to assess the effectiveness of the proposed method and its ability to capture temporal fluctuations. Since standard interpolation fails to accurately detect respiration data from R-R intervals and cannot precisely handle missing R-R intervals in short samples, cubic spline interpolation is recommended as a replacement and its results are presented. The obtained results provide insights into the effectiveness and application of the Pan-Tompkins algorithm, FFT (Fast fourier transform) implementation, and cubic spline interpolation in the selection of respiration and R-wave features. According to the findings of the study, in the analysis conducted on 2-second samples with a 1000 Hz sampling frequency created from each participant's respiratory data set, missing respiratory data were successfully reconstructed from the R-R intervals of the ECG data using standard and cubic curve interpolation methods. Upon examination of RMSE (Root mean square error) values, it was observed that for 30% of the participants, as RMSE values increased, completion counts for standard interpolation increased, while completion counts for cubic curve interpolation decreased. Conversely, when RMSE values decreased, 60% of the participants showed a decrease in completion counts for standard interpolation and an increase in completion counts for cubic curve interpolation. A 10% participant group was identified where there was no apparent relationship between RMSE values and interpolation method. This indicates that in 90% of the participants, there is a linear relationship between the study's interpolation method, RMSE values, and completion counts for missing R-R intervals.
Keywords
Respiratory Analysis with Electrocardiogram Data: Evaluation of Pan-Tompkins Algorithm and Cubic Curve Interpolation Method
Abstract
Advancements in bioinstrumentation have facilitated the easier monitoring of biometric signals such as electrocardiogram (ECG) and respiration. This development is particularly crucial for the diagnosis and management of various conditions like stress and sleep disorders. Two commonly used features in heart rate variability (HRV) analysis derived from ECG data are standard deviation and serial correlation coefficients of R-R intervals (the time durations between heartbeats). The former utilizes the fundamental components of QRS complexes, while the latter is designed to extract relationships between respiration and heart rate. In the proposed methodology, R-R wave detection is performed on processed ECG data using the Pan-Tompkins algorithm, and the respiration duration for each R-R interval from respiration data is selected. Additionally, missing respiration data for selected R-R intervals is interpolated based on the interpolation method. The results of this study are compared with the standard interpolation and cubic spline interpolation models to assess the effectiveness of the proposed method and its ability to capture temporal fluctuations. Since standard interpolation fails to accurately detect respiration data from R-R intervals and cannot precisely handle missing R-R intervals in short samples, cubic spline interpolation is recommended as a replacement and its results are presented. The obtained results provide insights into the effectiveness and application of the Pan-Tompkins algorithm, FFT (Fast fourier transform) implementation, and cubic spline interpolation in the selection of respiration and R-wave features. According to the findings of the study, in the analysis conducted on 2-second samples with a 1000 Hz sampling frequency created from each participant's respiratory data set, missing respiratory data were successfully reconstructed from the R-R intervals of the ECG data using standard and cubic curve interpolation methods. Upon examination of RMSE (Root mean square error) values, it was observed that for 30% of the participants, as RMSE values increased, completion counts for standard interpolation increased, while completion counts for cubic curve interpolation decreased. Conversely, when RMSE values decreased, 60% of the participants showed a decrease in completion counts for standard interpolation and an increase in completion counts for cubic curve interpolation. A 10% participant group was identified where there was no apparent relationship between RMSE values and interpolation method. This indicates that in 90% of the participants, there is a linear relationship between the study's interpolation method, RMSE values, and completion counts for missing R-R intervals.
Keywords
