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2nd Reading January 2, 2014 16:30 WSPC/1793-5369 244-AADA

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Advances in Adaptive Data Analysis Vol. 6, No. 1 (2014) 1450002 (17 pages) c World Scientific Publishing Company DOI: 10.1142/S1793536914500022

BREATHING PATTERN RECOGNITION OF ABDOMINAL WALL MOVEMENT BY USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION

YA-CHEN CHEN Institute of Computer Science and Engineering National Chiao Tung University, 1001 University Road Hsinchu 30010, Taiwan [email protected] TZU-CHIEN HSIAO∗ Department of Computer Science, Institute of Biomedical Engineering National Chiao Tung University, 1001 University Road Hsinchu 30010, Taiwan [email protected] JU-HSIN HSU Institute of Biomedical Engineering, National Chiao Tung University 1001 University Road, Hsinchu 30010, Taiwan [email protected] JIN-LONG CHEN Department of Medical Informatics, Tzu Chi University 701 Zhongyang Road, Sec. 3, Hualien 97004, Taiwan [email protected] Received 9 September 2013 Revised 17 December 2013 Accepted 19 December 2013 Published 6 January 2014 Thoracic breathing (TB), abdominal breathing (AB), and mixing breathing are common respiratory functions. Individuals usually breathe thoracically, whereas the breathing pattern of AB is vague. Despite the statistical representation of the physiological benefits of AB, coping with a time-variant environment still remains challenging. Therefore, based on ensemble empirical mode decomposition (EEMD), this study compares the identification types of using R value, power proportion, and modified significant test (MST). Respiratory maneuver of 26 subjects results that MST varied with a paced breathing frequency is the highest accurate recognition rate of TB (80.8% in 0.2 Hz and 88.5% in 0.1 Hz) and of AB (73.1% in 0.2 and 0.1 Hz). Results of this study demonstrate that EEMD is an adaptive algorithm to decompose respiratory movement. Furthermore, MST is a highly promising feature extraction method for breathing type recognition. Keywords: Ensemble empirical mode decomposition; modified significant test; abdominal breathing; respiratory pattern. ∗ Corresponding

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1. Introduction While consisting of air entering and leaving the body, human breathing involves the changes of internal air pressure within the thorax in comparison with the outside air pressure. Interactions between inhaled and exhaled air are also known as external respiration. Thoracic volume affects lung capacity and internal pressure, as well as volume changes due to different respiratory motion patterns, including thoracic breathing (TB), abdominal breathing (AB), and complete breathing, which are commonly used in humans. Many individuals breathe unconsciously and rapidly when their rib cages are pulled upward by upper trapezius muscles and intercostal muscles. Such breathing movement is known as TB or rib cage movement (RCM). An experienced AB individual can breathe when the diaphragm and abdominal muscles move the abdominal wall. This breathing movement is commonly referred to as AB, diaphragm breathing, or abdominal wall movement (AWM). AB has a larger tidal volume than TB, since the diaphragm flattens and moves downwards to increase thoracic cavity size; inhaled air is also drawn deep into the lungs. This movement benefits individual health by transporting air deep into the alveoli and increasing alveolar gas ventilation [Romei et al. (2010)]. In clinical applications, these advantages are vital and very important for chronic obstructive pulmonary disease or asthma patients, or individuals recovering from heart surgery. These patients must manage breathing patterns routinely for more AWM and less RCM [Brasher et al. (2003); Ritz and Roth (2003); Vitacca et al. (1998)]. In psychology, AB is a recommended breathing pattern for relaxing oneself because AB can enhance the alpha wave (i.e. an electroencephalopathy component) and stimulate parasympathetic nerves continuously to achieve emotional stability [Arambula et al. (2001)]. Furthermore, yoga students are always instructed to focus their thoughts and emotions during AB in order to increase their oxygen supply and muscular strength [Arambula et al. (2001)]. Despite considerable evidence demonstrating the health benefits of AB, the exact pattern of AWM is still vague. It is hard to evaluate the dynamic efficiency of AB owing to the unclear diaphragm movement. RCM and AWM represent the amplitude and frequency of the thoracic and abdominal displacements, respectively by using semi-invasive, noninvasive or noncontact measurements [Robert et al. (2000)]. Two clinically adopted semi-invasive bedside monitoring methods are impedance pneumography and inductive plethysmography. Despite acquiring the respiratory rate and volume inexpensively, both methods possess an insufficient resolution and inaccurate measurement of the variation of thoracoabdominal (TA) asynchrony in a dynamic environment [Folke et al. (2003)]. As a noninvasive apparatus, a spirometer is widely used in an intensive care unit. Although capable of measuring the exhaled air velocity and tidal volume distribution of patients wearing an uncomfortable mask, a spirometer should not be used outside of hospital experiments or in experiment outside of hospital. Among the noncontact measurement approaches developed include optoelectronic plethysmography (OEP) using infrared cameras to determine the breathing rate 1450002-2


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Breathing Pattern Recognition of AWM by Using EEMD

and pattern of RCM and AWM [Tu et al. (2013)]. The limitation with this method is that there are more than six fixed cameras which surround the subjects during imaging [Isabella et al. (2008)]. To allow for more convenient device use, this study adopts a piezoelectricity based respiratory effort transducer belt, which can measure the variations of respiratory movement and can be worn while walking around. This device can be easily commercialized as a leather belt. Examining the variations of respiratory movement requires appropriate devices and analysis methods. Despite the extensive use of statistical analysis in the recent decade to explore the variations of respiratory movement [Robert et al. (2000); Romei et al. (2010)], the statistical information was inadequate to examine the frequency variations of respiratory movement and the experimental design was excessively long, requiring more than 30 min of breathing. However, the respiratory movement frequency is 0.2–0.3 Hz, i.e. too low to require filtering before feature extraction. Additionally, the conventional filtering method incurs phase distortion. Fourier transform (FT) is also an effective means of extracting respiratory movement features in the frequency domain, which assumes that the signals are composed of sinusoidal oscillations of constant amplitudes and periods at a pre-determined frequency range. However, the respiratory movement mechanism includes nonlinear and nonstationary properties. Therefore, based on empirical mode decomposition (EMD), this study attempts to decompose a respiratory movement signal (i.e. EMD decompose respiratory movement signal) into individual, nearly monocomponent signals, referred to as intrinsic mode functions (IMFs). After EMD processing, instantaneous frequency, phase, and spectrum can be calculated by Hilbert transforms (commonly referred to as Hilbert–Huang transforms (HHT)) to analyze and extract the features of a short-time respiratory signal (less than 5 min). For instance, using a 5 min oronasal airway pressure signal during patients in the awake period, obstructive sleep apnea (OSA) is diagnosed using HHT. Analysis results indicate that OSA can be screened quickly in the spectra of 1st and 2nd IMF [Caseiroa et al. (2010)]. Respiratory sinus arrhythmia can be quantified using EMD [Kuo et al. (2009)]. However, EMD incurs the mode mixing problem, owing to the sifting process [Huang et al. (1999)]. A previous study developed the ensemble empirical mode decomposition (EEMD) method, i.e. a noise assisted data decomposition method, to improve the natural properties of IMFs [Wu and Huang (2009)]. Despite the many advantages of exercising AB frequently for human health, the definitions and characteristics of AB remain elusive. To investigate the qualitative and quantitative characteristics of AB, this study defines the main components of RCM and AWM. Whereas four respiratory effort transducers can be used to determine the nonlinear signals of RCM and AWM, and EEMD can be integrated with statistical techniques to identify AB. Based on those results, an AB learning system can be developed and awareness of users’ respiratory efficiency can be raised as well. The rest of this paper is organized as follows. Section 2 introduces the experimental design and data processing of this study. Section 3 summarizes the results of signals RCM and AWM implemented by EEMD. Next, Sec. 3 describes 1450002-3


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the accuracy of TB and AB recognition based on the significant test. Section 4 discusses the accuracy of respiratory pattern recognition, especially AB pattern recognition. Conclusions are finally drawn in Sec. 5, along with recommendations for future research.

2. Experimental Design and Data Processing 2.1. Experimental design This study was approved by the local ethics committee and all participants gave their informed consent. Study participants were 26 healthy subjects — without symptoms or medical record of respiratory diseases (5 females and 21 males; mean age: 23.4 ± 5.4 years old; mean body height: 170.1 ± 7.9 cm; and mean body weight: 62.9 ± 9.5 kgw). Figure 1(a) illustrates the experimental procedure. All subjects underwent TB and then AB for a comparison of TB and AB in terms of pattern difference. Following TB, the authorized trainer instructed subjects to undergo a successful AB. Therefore, all subjects knew how to implement AB before doing so. Each procedure comprised two steps (i.e. 12 cycles and 6 cycles per min to observe the respiratory patterns and low respiratory frequencies). Each step was performed for 5 min, with each subject having 20 min of recordings in each experiment. Subjects wore four respiratory effort transducers (SS5LB, BIOPAC Systems, Inc., Goleta, USA) on the trunks of their bodies. Channels 1 through 4 were located below axilla, on xiphoid, above the navel, and below the navel, respectively. Figure 1(b) illustrates the experimental structure. Signals were acquired by an accessory (ELVIS, National Instruments, Austin, USA) and data acquisition card

(a)

(b) Fig. 1. (a) Displays the experimental procedure and (b) shows the data acquisition instruments. A series of TB and AB trials were demonstrated in this study, and subjects were trained in AB before performing it. RCM and ABM were acquired using a piezoelectricity based respiratory effort transducer belt. 1450002-4


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(USB 6251, National Instruments, Austin, USA) at a sampling rate of 1000 Hz and were transferred to laptop (R830, Toshiba, Tokyo, Japan). Data acquisition and data processing were programmed by using LabVIEW (v. 2011, National Instruments, Austin, USA).

2.2. Decomposition and evaluation methods Respiratory raw signals in a steady environment were analyzed using the statistical methods from a previous respiratory study [Robert et al. (2000); Romei et al. (2010)]. However, the sinusoidal characteristics of RCM and AWM signals are nonlinear and time-variant. Investigating the intra-wave fluctuations of breathing in a short time is rather complex. Based on more effective approaches of nonlinear and nonstationary signal decomposition, relative EMD is appropriate for decomposing RCM and AWM signals. Following processing, “a” number of IMFs and one residue can be obtained, as expressed in the following equation: x[n] =

a

ci [n] + r[n],

(1)

i=1

where x[n] denotes the breathing signal; ci [n] represents the ith IMF; and r[n] refers to the residue in which no more IMF is extracted. Further details of the sifting process can be found in Huang et al. [1998]. Additionally, EEMD consists of an ensemble of white noise-added signals in the sifting process. Wang investigated the different stop criteria leading to different IMFs [Wanget al. (2010)]. This study also compared the average frequency of the same data within three stop criteria and selected the most feasible one. The first one, stop criterion, is the fixed sifting epoch criterion [Huang et al. (2009)], while the second one is the threshold defined as standard deviation. Finally, the criterion of amplitude ratio is derived from the mean of upper envelope and lower envelope. Figure 2 presents the acquired data in TB and AB. Figures 2(a)–2(d) display channels 1–4 when subject performed TB, while Figs. 2(e)–2(h) show channels 1–4 when subject performed AB. From an anatomy perspective, channels 1 and 2 belong to pure RCM, because these two channels are located at the chest level. Conversely, channels 3 and 4 belong to pure AWM, because these two channels are located at the abdomen level. If subjects were undergoing TB, the signal amplitudes in channels 1 and 2 were higher than those of channels 3 and 4. If subjects were undergoing AB, the signal amplitudes in channels 3 and 4 were higher than those in channels 1 and 2. To recognize the ability of the subject to perform TB or AB, this study developed an evaluation procedure by using EEMD (Fig. 3). In the evaluation procedure, significance test was used to examine whether the IMFs originate from white noise data [Wu and Huang (2004)]. Furthermore, the properties of main components of respiratory signal were defined using power distribution [Liu et al. (2011)] and R value [Press et al. (1993)]. 1450002-5


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(a)

(e)

(b)

(f)

(c)

(g)

(d)

(h)

Fig. 2. Each subject has four channels. Here (a) to (d) represent the respiratory movement signals when subjects underwent TB, and (e) to (h) represent the respiratory movement signals when subjects underwent AB.

Fig. 3.

Data processing of the main component search.

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3. Results of Respiratory Signal Using EEMD 3.1. IMFs of the breathing signal Respiratory movement signals include a significant amount of physiological information (e.g. respiratory movement, muscle contraction, and body movement). From a frequency domain perspective, different physiological informations have different frequency bands. Therefore, TB and AB patterns cannot be easily recognized from the acquired data (Fig. 2). This study investigates the respiratory movement patterns in TB and AB in different frequency bands by decomposing the respiratory movement signal by using EEMD. Figure 4 illustrates an example of a channel of a subject. Each acquired dataset was decomposed into 15 IMFs by EEMD. Because the sampling rate is 1000 Hz and the breathing rate is around 0.1–0.2 Hz, the 9th IMF is more likely to be the main component of the original data than the other IMFs. This study attempted to identify which IMF is the main component by using the significance test, power distribution, R value and average frequency to quantify the respiratory pattern. Wang et al. suggested that different stop criteria lead to different properties of IMFs [Wang et al. (2010)]. How to select the most appropriate stop criterion is thus of priority concern. Three common methods are available for making such a selection. First, in the fixed sifting epoch criterion, EMD is similar to the dyadic

(a)

(b) Fig. 4. Fifteen IMFs in each channel. (a) The original dataset was decomposed by EEMD. (b) The corresponding 15 IMFs were obtained from top to down. The x-axis is the samples, and the y-axis is the magnitude of each IMF. 1450002-7


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filter when the sifting process runs ten times. Hence, many studies have run the EMD code in ten epochs as the default stop criterion [Wu and Huang (2009)]. Second, in the threshold of standard deviation, the standard deviation is smaller than the threshold value. Additionally, the threshold value is the mean of upper and lower envelopes. The method in this study is referred to as SD[m]. SD[m] should be smaller than the defined threshold value. Third, in the amplitude ratio, the evaluation function δ[n] is derived from the mean of upper envelope (u(n)) and lower envelope (l(n)), which is δ(n) = [u(n) + l(n)]/[u(n) − l(n)]. Additionally, amplitude ratio use threshold value (θ) to evaluate the stopping sifting process. The sifting is iterated until δ(n) < θ for 95% of the total length of δ(n), while δ(n) < 10 θ for the remaining 5%. θ can be typically approximated to 0.05. Detailed description is given by Rilling et al. [2003]. Figure 5 summarizes the average frequency of IMFs obtained by the three methods. The average frequency decays smoothly when amplitude ratio is used as the stop criteria. This finding suggests that amplitude ratio is an appropriate stop criterion for EEMD decomposition. Therefore, amplitude ratio is selected for the stop criterion in this study. This study also examines the nonstationary property by applying the joint time– frequency analysis method to different IMFs of the respiratory movement signal. FT is widely used in stationary sinusoidal signal analysis. Moreover, a conventional joint method, short-time FT (STFT), is used for short-term nonstationary signal analysis. Figure 6 summarizes the results of the spectra of the respiratory movement signal by using HHT and STFT, where the sampling rate of the original data is 1000 Hz and the window size of STFT is 10 s with 9 s overlapping each other. Owing to the respiratory frequency around 0.2 Hz, the spectrum of the main component by using HHT provides an obviously nonstationary interpretation than the one using STFT. Analysis results indicate that HHT provides a higher multi-scales resolution than that of STFT. For instance, the instantaneous frequency of respiratory movement approximates 0.2 Hz, which can be easily observed. Given its above merits, HHT is selected in this study to analyze the breathing signal.

(a)

(b)

Fig. 5. The averaged frequency of IMFs obtained by the fixed sifting time criterion, SD[m] and amplitude ratio. (a) is the result of these three criteria and (b) is the corresponding curve fittings. The comparison indicates that amplitude ratio is the smallest damping value between these three cases. 1450002-8


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(b)

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Fig. 6. Results of the HHT and short-time Fourier transform (STFT). (a) and (b) are the spectra of 1st IMF by using HHT and STFT, respectively. Also, (c) and (d) are the corresponding spectra of the main component. Since the respiratory frequency in this case is approximately 0.2 Hz, the spectrum of the main component by using HHT provides a significantly higher resolution than the one by using STFT.

3.2. Significant test Figure 7 displays the values of mean ± standard deviation of 26 subjects when calculating the averaged periods and distribution of the energy densities. This significant test is originally used to evaluate the white noise distribution and characterization by calculating the energy density and averaged period of each IMF, i.e. N 1 (ci [n])2 , (2) N n=1 SlnT,i dlnT Ti = , (3) dlnT SlnT,i T where Ei denotes the energy density of the ith IMF; SlnT,i represents the Fourier spectrum function of period (T ) of the ith IMF; and T i refers to the averaged period of the ith IMF. The white noise is identified by the sum of logarithmic averaged period (ln T i ) and logarithmic energy density (ln Ei ) and approximated to a constant [Wu and Huang (2004)]. According to this characterization of white noise, four respiratory transducers have similar distributions yet different noise

Ei =

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(a)

(b)

(c)

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Fig. 7. Characterization plot with logarithmic averaged period and logarithmic energy density of each IMF in an experiment involving 26 subjects. (a)–(d) are the results of channels 1–4, respectively. The points from left to right are 1st IMF through 15th IMF. The filled circles denote AB, and the hollow circles represent TB.

levels when the subject performed TB and AB. Therefore, this study classifies TB and AB by using the characterization of noise level. Figure 7 indicates that the first six IMFs (1st to 6th IMFs) are possibly owing to white noise. Linear curve fitting is also performed to fit those IMFs, indicating a negative relationship between the averaged periods and energy densities (slope: −1.15 ± 0.06). Additionally, the subject performing TB or AB is classified based on the distribution of energy density of 1st to 6th IMFs. For instance, if a subject is performing TB, the energy density of 1st to 6th IMFs of TB (Figs 7(a), hollow circles) is lower than that of the 1st to 6th IMFs of AB in channel 1 (Fig. 7(a), filled circles). Conversely, the energy density of 1st to 6th IMFs of AB is lower than that of the 1st to 6th IMFs of TB in channels 3 and 4 (Figs. 7(c)–7(d)) when the subject performs AB. Table 1 lists the averaged periods and energy densities of 1st to 6th IMFs. TB and AB are classified based on a statistical examination. Analysis results indicate 1450002-10

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Note: ∗ denotes p < 0.05 compared with TB;

∗∗ denotes

p < 0.01 compared with TB.

−0.28±0.85∗∗ −0.59±0.76∗∗ −2.16±0.75∗∗ −2.68±0.73∗∗ −3.17±0.72∗∗ −3.57±0.68∗∗

Logarithmic energy density 0.14±1.13∗ 1.28±1.38 −1.21±1.01∗ −0.14±1.37 −1.79±0.99∗ −0.74±1.34 −2.30±0.98∗ −1.27±1.31 −2.81±0.98∗ −1.78±1.29 −3.23±0.94 −2.23±1.23

1.73±1.86∗∗ 0.34±1.86∗∗ −0.27±1.81∗∗ −0.80±1.77∗∗ −1.31±1.73∗∗ −1.69±1.61∗∗

0.84±1.63 −0.54±1.59 −1.13±1.54 −1.65±1.51 −2.15±1.48 −2.52±1.40

IMF1 IMF2 IMF3 IMF4 IMF5 IMF6

1.60±1.68 0.11±1.61 −0.51±1.59 −1.04±1.55 −1.53±1.52 −1.87±1.42

−5.63±0.14 −5.07±0.09 −4.54±0.11 −4.00±0.10 −3.45±0.10 −2.83±0.14

TB

AB

−0.16±1.07∗∗ −1.44±1.00∗∗ −2.07±0.93∗∗ −2.59±0.92∗∗ −3.02±0.91∗∗ −3.06±0.86∗∗

−5.49±0.17∗∗ −4.96±0.11∗∗ −4.44±0.10∗∗ −3.90±0.09∗∗ −3.35±0.08∗∗ −2.71±0.10∗∗

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0.41±1.38 −0.94±1.28 −1.53±1.25 −2.05±1.24 −2.56±1.22 −3.01±1.17

−5.50±0.14∗∗ −4.95±0.09∗∗ −4.44±0.08∗∗ −3.91±0.07∗∗ −3.37±0.07∗∗ −2.78±0.07∗∗

Logarithmic averaged period −5.56±0.17 −5.55±0.19 −5.67±0.11 −5.00±0.12 −4.99±0.12 −5.08±0.07 −4.49±0.10 −4.47±0.10 −4.55±0.07 −3.95±0.10 −3.94±0.10 −4.01±0.06 −3.40±0.09 −3.39±0.09 −3.47±0.06 −2.83±0.10 −2.81±0.09 −2.88±0.06

AB

−5.60±0.15∗ −5.06±0.08∗∗ −4.54±0.08∗∗ −4.01±0.07∗∗ −3.46±0.07∗∗ −2.87±0.09∗∗

TB

−5.59±0.16 −5.03±0.10 −4.51±0.09 −3.97±0.09 −3.43±0.08 −2.83±0.09

AB

Channel 3

IMF1 IMF2 IMF3 IMF4 IMF5 IMF6

TB

Channel 2 AB

Channel 1

Comparison of the averaged periods and energy densities of 1st to 6th IMFs derived from channels 1–4 during TB and AB.

TB

Table 1.

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that TB and AB significantly differ from each other in channels 1, 3 and 4. Owing to the location of respiratory belts, channel 1 is treated as RCM. If the subject did TB, the RCM moved significantly, and the noise of TB is lower than noise of AB in channel 1. Alternatively, channels 3 and 4 are treated as AWM. If the subject performs AB, AWM moves significantly, and the noise of AB is lower than that of TB in channels 3 and 4. Consequently, the different patterns of averaged periods and energy densities of 1st to 6th IMFs in TB and in AB can be used to classify the subject either performing AB or performing TB. 3.3. Main components searching TB and AB can be classified based on the noise behavior of the significant test. The properties of the main components are defined by evaluating the correlation coefficient and energy density. According to Fig. 8, calculating the R value between IMFs and acquired data can obtain the main components, in which the R values are higher than the others. In this study, the maximum ln Ei is also used, which is calculated by the significant test to identify the main component. This is due to

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Fig. 8. Results of main components searching by using energy density and R value. (a) For 0.1 Hz paced breathing and (b) for 0.2 Hz paced breathing. 1450002-12


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Fig. 9. This figure indicates the results of main components searching by using averaged instantaneous frequency. (a) For 0.1 Hz paced breathing and (b) for 0.2 Hz paced breathing.

the breathing movement being a combination of respiratory movement and muscle contraction; therefore, the maximum energy can be treated as the dominant movement. Based on the results of 26 subjects, the main components of 0.1 Hz paced breathing are allocated at 13th IMF or 14th IMF (Fig. 8(a)), in which the averaged instantaneous frequency approaches 0.1 Hz (Fig. 9(a)). In contrast with 0.2 Hz paced breathing, the locations are 12th and 13th IMF (Fig. 8(b)), and the averaged instantaneous frequency also approaches 0.2 Hz (Fig. 9(b)). 3.4. Accuracy of TB and AB recognition In this study, TB and AB are recognized using power proportion (PP) and R value. PP is computed as the main component in every channel, which is divided by the sum of the main component of four channels, respectively. According to Sec. 2.1, TB pattern is revealed as belonging to channels 1 and 2, and AB pattern is revealed as belonging to channels 3 and 4. Therefore, if channels 1 or 2 has the maximum value of PP and R value when the subject is performing TB, the results determine correct recognition. The process of AB recognition is same as that of TB recognition. Table 2 summarizes the accuracy of breathing pattern recognition. Owing to the R value, the averaged accuracy of the TB procedure is 76.9% (69.2% and 84.6% in 0.2 Hz and 0.1 Hz, respectively), and the averaged accuracy of AB procedure is 67.3% (69.2% and 65.4% in 0.2 Hz and 0.1 Hz, respectively). If PP recognizes the patterns of TB and AB, the averaged accuracy of the TB procedure is 80.8% (84.6% and 76.9% in 0.2 Hz and 0.1 Hz, respectively) and the averaged accuracy of AB procedure is 67.4% (65.4% and 73.1% in 0.2 Hz and 0.1 Hz, respectively). According to Sec. 3.2, the trend of 1st to 6th IMFs can be treated as the noise level. Additionally, the intercept value of linear curve fitting of 1st to 6th IMFs can classify the two groups, TB and AB. For instance the intercept values in Figs. 7(a), 7(c)–7(d) are shifted to the same level. Obviously, the main components are used 1450002-13


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Accuracy of breathing pattern recognition in TB and AB. TB

R value PP MST1 MST2

AB

0.2 Hz

0.1 Hz

0.2 Hz

0.1 Hz

69.2% 84.6% 80.8% 84.6%

84.6% 76.9% 88.5% 76.9%

69.2% 65.4% 73.1% 73.1%

65.4% 73.1% 73.1% 73.1%

to classify the subject performing TB or AB. In this study, the paced breathing patterns in 0.1 Hz and 0.2 Hz are designed for the subject performing TB and AB. The logarithmic energy density can be shifted upward by the following equation: y = ax + b,

(4)

where x denotes the logarithmic averaged period; y represents the shifted energy density; and a and b refer to slope and intercept of linear curve fitting, respectively. The y value can be shifted based on the property of paced breathing frequency, i.e. x is equal to logarithmic 10 (0.1 Hz) and x is equal to logarithmic 5 (0.2 Hz). After shifting, either subject performing TB or AB is determined using the maximum energy densities in channels 1–4. The above process is referred to as the first modified significant test (MST1). Additionally, another shifting process (called MST2) is based on the corresponding averaged period of main components. The equation is described as following: y = axmax(log E) + b,

(5)

where xmax(log E) denotes the corresponding averaged period of the main components. Table 2 lists the comparison results of MST1/MST2 and PP/R value. MST1 and MST2 generally have a higher accuracy than that of the other methods. 4. Respiratory Pattern Recognition 4.1. Accuracy of AB recognition Four respiratory effort transducers (channels 1–4), which are located on the subject’s trunk, acquire the respiratory movement. The RCM and AWM signals during breathing are recorded spontaneously. Since the properties of acquired signals are location-dependent, the decomposed features indicate the direct influence of location. For instance, in Fig. 7(b), because channel 2 denotes the transducer fastening on xiphoid which is influenced by either RCM or AWM, the trend of 1st to 6th IMFs in channel 2 has difficulty in discriminating between TB and AB. Therefore, the signal of channel 1 is treated as RCM. Additionally, the signals of channels 3 and 4 are treated as AWM. The calculation of R value is the correlation between the acquired signal and its main components. Moreover, maximum R values in four channels are compared with each other to determine whether the subject performed 1450002-14


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TB or AB. According to our results, the body movement is obvious when performing a slow AB. The acquired signal of AWM during AB in 0.1 Hz is always combined with body movement. The accuracy of AB recognition (i.e. 65.4% in 0.1 Hz) is less than the value of TB recognition (i.e. 84.6% in 0.1 Hz). This study also attempts to modify the influence coming from body movement by using PP and MST. MST1 and MST2 are based on a fixed paced breathing period and the averaged period of main components, respectively. According to Table 2, MST1 and MST2 are more accurate than R value and PP when performing AB. Calculating MST is a potential means of recognizing AB.

4.2. TA asynchrony This study has elucidated the properties of respiratory movement by decomposing the respiratory movement based on EEMD and recognizing TB and AB by R, PP and MST values. This study focuses on TA motion. In this paper, TA motion (including RCM and AWM) offer insight into the actions of the respiratory muscles and can be measured as well as provide a quantitative assessment of pulmonary ventilation [Goldman (1982)]. TA motion and muscular activity can facilitate an evaluation of a respiratory system [Tomich et al. (2007)]. Gender, age, and different positions (e.g. sitting or supine) have different TA motions [Parreira et al. (2010)]. Additionally, obstructive pulmonary disease can be diagnosed based on TA asynchrony [Hammer and Newth (2009)]. According to this paper, TA asynchrony is essential for a respiratory system, explaining the importance of analyzing TA asynchrony by instantaneous phase. Previous paper investigated TA asynchrony by using a statistical method or time domain phase calculation. Phase distortion occurs when phase calculation with filter extraction is performed. Based on EEMD, this study attempts to decompose respiratory movement and avoid phase distortion by using direct quadrature [Huang et al. (2009)]. Related results provide further insight into the interaction between RCM and AWM on the instantaneous phase.

5. Conclusion Based on EEMD, this study attempts to decompose respiratory movement and classify the specific properties of TB and AB by using the significant test. R, PP, and MST values are also calculated to determine the accuracy of TB and AB recognition. Analysis results indicate that MST1, which is modified by a fixed breathing frequency, is the most effective means of recognizing TB (80.8% in 0.2 Hz and 88.5% in 0.1 Hz) and AB (73.1% in 0.2 Hz and 0.1 Hz) in the paced breathing experiment. We conclude that EEMD is an adaptive algorithm, capable of decomposing respiratory movement. Moreover, the MST with the significant test is a highly effective means of extracting the features of respiratory pattern for breathing type recognition. Efforts are underway in our laboratory to investigate the instantaneous phase difference between RCM and AWM in spontaneous breathing. 1450002-15


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