Limits...
Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition.

Wang FT, Chan HL, Wang CL, Jian HM, Lin SH - Sensors (Basel) (2015)

Bottom Line: This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring.Moreover, large motion artifacts disable the EMD to decompose respiratory components.A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Hwa Hsia University of Technology, 111, Gongzhuan Rd., Zhonghe, New Taipei City 23568, Taiwan. wft.intuitive@seed.net.tw.

ABSTRACT
Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method.

No MeSH data available.


Related in: MedlinePlus

A thoracic impedance signal (a) is decomposed into 10 intrinsic mode functions (IMFs) and a residual signal by empirical mode decomposition. IMF1…IMF5 have too many zero-crossing points, containing noises rather than respiratory patterns (b–f). IMF6 and IMF7 contain major respiratory component (i,j). IMF8…IMF10 (k–m) and residual signal (n) are low-frequency structures of the respiration.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4541883&req=5

sensors-15-16372-f005: A thoracic impedance signal (a) is decomposed into 10 intrinsic mode functions (IMFs) and a residual signal by empirical mode decomposition. IMF1…IMF5 have too many zero-crossing points, containing noises rather than respiratory patterns (b–f). IMF6 and IMF7 contain major respiratory component (i,j). IMF8…IMF10 (k–m) and residual signal (n) are low-frequency structures of the respiration.

Mentions: Through EMD, the thoracic impedance is decomposed into several IMFs of different oscillatory modes. Zero-crossing is one of the intrinsic properties for each IMF. An IMF with more zero-crossing points contains oscillatory components with higher frequencies and vice versa. As illustrated in Figure 5, IMFs with too many zero-crossing points contain noises rather than respiratory patterns (IMF1…IMF5). An intrinsic respiratory reconstruction index (IRRI) is defined to exclude IMFs with too many zero-crossing points (IMFj, j < IRRI) and select the rest of the IMFs for respiration reconstruction (j ≥ IRRI). IMFs that contain fewer zero-crossing points (IMF8…IMF10 in Figure 5) and a residual signal are included because they are low-frequency structures of a respiration. The respiration is therefore reconstructed by(5)x(t)=∑j=IRRInIMFj+rn


Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition.

Wang FT, Chan HL, Wang CL, Jian HM, Lin SH - Sensors (Basel) (2015)

A thoracic impedance signal (a) is decomposed into 10 intrinsic mode functions (IMFs) and a residual signal by empirical mode decomposition. IMF1…IMF5 have too many zero-crossing points, containing noises rather than respiratory patterns (b–f). IMF6 and IMF7 contain major respiratory component (i,j). IMF8…IMF10 (k–m) and residual signal (n) are low-frequency structures of the respiration.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541883&req=5

sensors-15-16372-f005: A thoracic impedance signal (a) is decomposed into 10 intrinsic mode functions (IMFs) and a residual signal by empirical mode decomposition. IMF1…IMF5 have too many zero-crossing points, containing noises rather than respiratory patterns (b–f). IMF6 and IMF7 contain major respiratory component (i,j). IMF8…IMF10 (k–m) and residual signal (n) are low-frequency structures of the respiration.
Mentions: Through EMD, the thoracic impedance is decomposed into several IMFs of different oscillatory modes. Zero-crossing is one of the intrinsic properties for each IMF. An IMF with more zero-crossing points contains oscillatory components with higher frequencies and vice versa. As illustrated in Figure 5, IMFs with too many zero-crossing points contain noises rather than respiratory patterns (IMF1…IMF5). An intrinsic respiratory reconstruction index (IRRI) is defined to exclude IMFs with too many zero-crossing points (IMFj, j < IRRI) and select the rest of the IMFs for respiration reconstruction (j ≥ IRRI). IMFs that contain fewer zero-crossing points (IMF8…IMF10 in Figure 5) and a residual signal are included because they are low-frequency structures of a respiration. The respiration is therefore reconstructed by(5)x(t)=∑j=IRRInIMFj+rn

Bottom Line: This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring.Moreover, large motion artifacts disable the EMD to decompose respiratory components.A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Hwa Hsia University of Technology, 111, Gongzhuan Rd., Zhonghe, New Taipei City 23568, Taiwan. wft.intuitive@seed.net.tw.

ABSTRACT
Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method.

No MeSH data available.


Related in: MedlinePlus