Cardiogenic oscillations extraction in inductive plethysmography: Ensemble empirical mode decomposition

Abstract

The purpose of this study is to investigate the potential of the ensemble empirical mode decomposition (EEMD) to extract cardiogenic oscillations from inductive plethysmography signals in order to measure cardiac stroke volume. First, a simple cardio-respiratory model is used to simulate cardiac, respiratory, and cardio-respiratory signals. Second, application of empirical mode decomposition (EMD) to simulated cardio-respiratory signals demonstrates that the mode mixing phenomenon affects the extraction performance and hence also the cardiac stroke volume measurement. Stroke volume is measured as the amplitude of extracted cardiogenic oscillations, and it is compared to the stroke volume of simulated cardiac activity. Finally, we show that the EEMD leads to mode mixing removal.

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

“…EEMD was also been used to simulate cardio-respiratory signals in order to measure cardiac stroke volume. EEMD improved them better than EMD by mode mixing removal [15]. …”

Signal Quality Enhancement of Esophageal Pressure Signals in Mechanically Ventilated Patients

“…The extracted AM-FM components described previously are known as intrinsic mode functions (IMFs), which represent the oscillatory modes embedded in the data [47], [48]. While the IMFs allow for the calculation of instantaneous frequency via the Hilbert Transform [47], [49], it has been suggested that the IMFs are also related to specific physical phenomena present in the measured data [49]- [50]. Since EMD is capable of decomposing nonlinear and non-stationary signals into components that can represent physical phenomena, the algorithm has been used successfully in many applications related to biomedical signal processing such as ECG enhancement and QRS detection [51], EEG artifact removal [52]- [53], tissue artifact removal from respiration signals [54], feature extraction for epilepsy detection [55] and more recently, to identify a child's autism severity level [56].…”

Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health

“…Because of its superiority, HHT has been utilized and investigated widely to analyze data in a wide variety of applications by researchers and experts. In the past decades, more and more studies about HHT theory and application are reported [16], [17], [18], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39]. The signal processing method based on the HHT is considered as a great breakthrough of linear and stationary spectrum analysis based on Fourier transform.…”

“…The HHT is a novel method for analyzing nonlinear and nonstationary signals and it is applicable to nonlinear and nonstationary processes [30]. The HHT was first introduced and applied in the analysis of physiological system [16], [31], [32], [33], [34], [35], [36] and ECG signal denoising [37], [38]. For example, in [31] Neto et al applied empirical mode decomposition (EMD) to analyze the cardiac sympathovagal balance on automatically extracted modes, in [32] they developed a new EMD-based LF versus HF spectral decomposition of heartbeat variability and systolic blood pressure, and they defined the corresponding EMD spectral indices and study their relevance to detect and analyze accurate changes in the sympatho-vagal balance without having recourse to any fixed high-pass/low-pass filters.…”