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 fromPhysiobank 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.

On how to extract breathing rate from PPG signal using wearable devices

“…In 2011, Madhav et al [7] proposed a method based on the Empirical Mode Decomposition (EMD) for estimating BR from PPG signal. EDM is an adaptive time-frequency data analysis; it has proven to be quite versatile in a broad range of applications for extracting signals from data generated in noisy non-linear and non-stationary processes.…”

“…In the last decade, several approaches addressing PPG-derived BR have been proposed. Despite good performances have been achieved, algorithm complexity and computational requirements have prevented real-time applications [6], [7].…”

“…A general review of BR estimators from PPG signal is provided in Section II. Following, the novel approach is presented and compared to Madhav's [7], which was the starting point for the development of our method. The algorithm is validated on two distinct datasets, one already proposed in the Literature, and the other we experimentally collected using wearable devices.…”

Estimation of Respiration rate from ECG Using Canonical Components Analysis and Ensemble Empirical Mode Decomposition

“…DevyWidjajaet al [10] applied the Kernel Principal Component Analysis in order to extract ECG-Derived respiration rate from a single lead ECG signal. K.VenuMadhavet al [11] estimated the respiration signal from ECG signal, Blood pressure(BP) signal and Photoplethysmographic (PPG) signal using EMD. Several approaches have been made using wavelet decomposition in estimating the respiration signal.…”

Development of A Respiration Rate Meter - A Low-Cost Design Approach

“…Implementation of optical methods, ultrasonic proximity sensor, impedance plethysmography, ECG signal (via wavelet), facial tracking method and real-time vision based system are also studied by Scalise et al [18], Min et al [19], Ansari et al [20], Santo and Carbajal [21], Khalidi et al [6] and Tan et al [1] respectively regarding the measurement of respiration rate. Recent studies on respiration rate monitoring using capacitive textile force sensors, stepped-FM method, empirical mode decomposition method and laser Doppler vibrometry have also been carried out by Hoffmann et al [22], Otsu et al [23], Madhav et al [24] and Scalise et al [25].…”