Investigation on the effect of Womersley number, ECG and PPG features for cuff less blood pressure estimation using machine learning

Abstract Objective Regulation and inhibition of high blood pressure, known as hypertension are intricate, and it demands a continuous, accurate blood pressure measurement system. All the existing continuous non-invasive techniques own challenges such as exact placement of the sensor, reconstruction of arterial pressure from finger cuff, frequent and subject based calibration. This paper presents an algorithm based on new time-domain features for continuous blood pressure monitoring which is crucial in intensive care units and can be used to predict cardiovascular ailments. Methods Here, we propose the method to estimate BP that extracts informative features like Womersley number (α), QRS, QTc interval, SDI from ECG and PPG signals and regression techniques which are employed to estimate blood pressure continuously. Performance metrics like MAE, RMSE, r, bias & 95% CI are considered to validate the proposed method. To explore the relevance of proposed physiological features with the blood pressure, genetic algorithm(GA) with the random forest model is employed. Results Significant features like alpha, QRS complex, QT interval, SDI, heart rate are acquired. The best optimal feature set from GA reduced the MAE from 13.20 to 9.54 mmHg, 9.91 to 5.48 mmHg, 7.71 to 3.37 mmHg for SBP, DBP and MBP respectively. Conclusion Model build with ECG and PPG time-domain features outperform the model trained with PPG features alone. Results obtained from GA validates the significance of ECG features correlation with BP. Significance Identifying the associations of ECG features with BP helps in selecting relevant features which minimize the computational cost as well as errors in the assessment of BP.