Extraction of respiration rate from ECG and BP signals using order reduced-modified covariance AR technique

Abstract

Extraction of respiration rates from electrocardiogram (ECG) and blood pressure (BP) signals would be an alternative approach for obtaining respiration related information. This process is useful in situations when, ECG, or BP 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) and BP-Derived Respiration (BDR). 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 and frequency modulated (FM) component. The proposed method is a robust, yet simple and makes use of order reduced AR-model by restricting the pole locations in the frequency range of interest. Test results on ECG and BP signals of MIMIC data base of Physiobank archive reveal that the proposed order reduced-modified covariance AR model (OR-MCAR) has efficiently extracted respiratory information from ECG and BP signals. The evaluated similarity parameters in both time and frequency domains for original and surrogate respiratory signals have shown the superiority of the method over wavelet based method.