Estimation of respiration rate from ECG, BP and PPG signals using empirical mode decomposition

Abstract

Estimation of respiration rates from electrocardiogram (ECG), blood pressure (BP) and photoplethysmographic (PPG) signals would be an alternative approach for obtaining respiration related information. This process is useful in situations when, ECG, BP and PPG but not respiration is routinely monitored or in cases where, the cardiac arrhythmias are to be studied in correlation with respiratory information and is extremely important. There have been several efforts on ECG-Derived Respiration (EDR), BP-Derived Respiration (BDR) and PPG Derived Respiration (PDR). These methods are based on different signal processing techniques like filtering, wavelets and other statistical methods, which work by extraction of respiratory trend embedded into various physiological signals, as an additive component, or an amplitude modulated (AM) component or frequency modulated (FM) component. The proposed method is a robust, yet simple and makes use of derived Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition (EMD). Test results on ECG, BP and PPG signals of the well known MIMIC database from Physiobank archive reveal that the proposed EMD method has efficiently extracted respiratory information from ECG, BP and PPG signals. The evaluated similarity parameters in both time and frequency domains for original and estimated respiratory rates have shown the superiority of the method.