Abstract
Breathing Rate (BR), an important deterioration indicator, has been widely neglected in hospitals due to the requirement of invasive procedures and the need for skilled nurses to be measured. On the other hand, biomedical signals such as Seismocardiography (SCG), which measures heart vibrations transmitted to the chest-wall, can be used as a non-invasive technique to estimate the BR. This makes SCG signals a highly appealing way for estimating the BR. As such, this work proposes three novel methods for extracting the BR from SCG signals. The first method is based on extracting respiration-dependent features such as the fundamental heart sound components, S1 and S2 from the SCG signal. The second novel method investigates for the first time the use of data driven methods such as the Empirical Mode Decomposition (EMD) method to identify the respiratory component from an SCG signal. Finally, the third advanced method is based on fusing frequency information from the respiration signals that result from the aforementioned proposed methods and other standard methods. The developed methods in this paper are then evaluated on adult recordings from the combined measurement of ECG, the Breathing and Seismocardiograms database. Both fusion and EMD filter-based methods outperformed the individual methods, giving a mean absolute error of 1.5 breaths per minute, using a one-minute window of data.
Highlights
The Breathing Rate (BR) plays a key role in patient monitoring as it describes the air in and out of lungs, which can be an indicator of deterioration as the body attempts to maintain oxygen delivery to the tissues [1]
The majority of studies decide the model order experimentally by testing orders ranging from 6 to 20 [23]. This approach of order selection is lurking risks as it is referring to a specific age and type of patients, it may deteriorate the BR estimation accuracy for subjects with different characteristics. This drawback is addressed in our suggested enhanced fusion method by introducing a model order selection criterion which is based on the partial autocorrelation function (PACF) of the respiration signal
The aforementioned features (S1-S1 interval, S1 and S2 intensity) create discrete non-uniform time series which are sampled at the heart rate (HR) frequency because their location depends on the R-peaks in the ECG signal
Summary
The Breathing Rate (BR) plays a key role in patient monitoring as it describes the air in and out of lungs, which can be an indicator of deterioration as the body attempts to maintain oxygen delivery to the tissues [1]. The majority of studies decide the model order experimentally by testing orders ranging from 6 to 20 [23] This approach of order selection is lurking risks as it is referring to a specific age and type of patients, it may deteriorate the BR estimation accuracy for subjects with different characteristics. This drawback is addressed in our suggested enhanced fusion method by introducing a model order selection criterion which is based on the partial autocorrelation function (PACF) of the respiration signal.
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