Changes in bowel sounds of inpatients undergoing general anesthesia

Abstract

Background:
General anesthesia can affect intestinal function, but there is no objective, practical and effective indicator to evaluate the inhibition and recovery of intestinal function. The main objectives of this study were to assess whether bowel sounds (BSs) changed before, immediately after and 3 h after general anesthesia, and whether these changes in BSs are an effective indicator of intestinal function and an accurate guide for postoperative feeding.

Methods:
We randomly selected 26 inpatients and collected three sets of 5-min continuous BS data before the operation (Pre-op), immediately after the operation (Pro-op) and 3 h after the operation (3 h-Pro-op) for each patient. Then, the linear and nonlinear characteristic values (CVs) of each effective bowel sound were extracted and paired t tests and rank-sum tests were used to evaluate the changes in the BSs.

Results:
The differences in CVs, between Pre-op and Pro-op, as well as between Pro-op and 3 h-Pro-op, were statistically significant (p < 0.05). However, there are no statistically significant differences between all the CVs between Pre-op and 3 h-Pro-op (p > 0.05).

Conclusion:
BSs change before and after general anesthesia. Furthermore, the BSs are weakened due to general anesthesia and recover to the pre-op state 3 h later. Therefore, the BSs can be an indicator of intestinal function under general anesthesia, so as to provide guidance for postoperative feeding, which is of considerable clinical significance.

Full text

Changes inbowel sounds ofinpatients
undergoing general anesthesia
Guojing Wang1,2,3 , Mingjun Wang4, Hongyun Liu1,2,3, Suping Zhao5, Lu Liu5 and Weidong Wang1,2,3*
Background
General anesthesia can inhibit gastrointestinal function, so postoperative feeding
needs to wait for the gradual recovery of gastrointestinal function to allow appro-
priate and timely feeding. At present, there is no objective or practical evaluation
method for postoperative recovery of gastrointestinal function, so the time for
resuming oral input is somewhat arbitrary. However, for patients undergoing general
anesthesia, it is important to be able to take in nutrition relatively early for opti-
mal postoperative recovery. Therefore, the evaluation of gastrointestinal function
Abstract
Background: General anesthesia can affect intestinal function, but there is no objec-
tive, practical and effective indicator to evaluate the inhibition and recovery of intesti-
nal function. The main objectives of this study were to assess whether bowel sounds
(BSs) changed before, immediately after and 3 h after general anesthesia, and whether
these changes in BSs are an effective indicator of intestinal function and an accurate
guide for postoperative feeding.
Methods: We randomly selected 26 inpatients and collected three sets of 5-min con-
tinuous BS data before the operation (Pre-op), immediately after the operation (Pro-op)
and 3 h after the operation (3 h-Pro-op) for each patient. Then, the linear and nonlinear
characteristic values (CVs) of each effective bowel sound were extracted and paired t
tests and rank-sum tests were used to evaluate the changes in the BSs.
Results: The differences in CVs, between Pre-op and Pro-op, as well as between
Pro-op and 3 h-Pro-op, were statistically significant (p < 0.05). However, there are no
statistically significant differences between all the CVs between Pre-op and 3 h-Pro-op
(p > 0.05).
Conclusion: BSs change before and after general anesthesia. Furthermore, the BSs
are weakened due to general anesthesia and recover to the pre-op state 3 h later.
Therefore, the BSs can be an indicator of intestinal function under general anesthesia,
so as to provide guidance for postoperative feeding, which is of considerable clinical
significance.
Keywords: Bowel sounds, General anesthesia, Intestinal function
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RESEARCH
Wangetal. BioMed Eng OnLine (2020) 19:60
https://doi.org/10.1186/s12938-020-00805-z
BioMedical Engineering
OnLine
*Correspondence:
wang_weidong301@163.
com
1 Key Laboratory
of Biomedical Engineering
and Translational Medicine,
Ministry of Industry
and Information Technology,
Chinese PLA General
Hospital, Beijing, China
Full list of author information
is available at the end of the
article
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after general anesthesia directly affects the judgment of timely postoperative feeding
time, which has important clinical significance.
Studies of noninvasive methods to judge the recovery of gastrointestinal function
after general anesthesia mainly use anal exhaust [1], electrogastrogram [2], bowel
sounds (BSs) auscultation and dynamic magnetic resonance imaging (DMR) [3].
Anal exhaust mainly depends on the patients’ subjective complaints, which cannot
objectively and in a timely way reflect the recovery of gastrointestinal function. Elec-
trogastrograms are more objective and accurate than anal exhaust assessment, but
are easily influenced by other bioelectric signals, and the current analysis of electro-
gastrogram data is not particularly mature or widely accepted. DMR relies on large
imaging equipment, so it is difficult to monitor patients in a timely way during the
perioperative period.
Auscultation of BSs is an important noninvasive way to judge gastrointesti-
nal function. BSs are produced by the movement of substances in the intestine, so
the sounds can objectively reflect the activity of intestinal peristalsis in real time.
Research on BSs use the characteristics of BSs to observe gastrointestinal status and
diagnose gastrointestinal diseases. In clinical practice, the observation of gastroin-
testinal peristalsis is used to monitor feeding events, thus for example, providing a
reference for the monitoring of blood glucose in an artificial pancreas system [4];
BSs can also be used as one of the indicative parameters of gastrointestinal diseases
[5]. If the gastrointestinal tract development lesions occur, such as gastroduodenal
disease, intestinal disease, and large bowel disease, the corresponding intensity or
number of BSs may also be abnormal. In addition, BSs can indicate other diseases.
Recent studies have found that BSs can not only indicate gastrointestinal state, but
also have clinical significance for sepsis [6], Parkinson’s disease [7] and other dis-
eases. The above studies show that BSs can reflect gastrointestinal function, so we
can consider the application of changes in BSs in the evaluation of gastrointestinal
function recovery in patients after general anesthesia.
However, using a handheld stethoscope for auscultation is still the main way to
quickly obtain intestinal sounds in the clinic. As for the identifying the specific gut
sounds, the results tend to be random and depend on of subjective judgment and are
therefore questionable [8, 9]. In the study of BSs, the sounds are acquired by means
of an assembly of mature pickups and storage units. Currently, there is no special
bowel sound equipment in the clinical environment to collect bowel sound data.
In this study, considering the requirements of the perioperative medical environ-
ment and the patient’s poor cooperation before and after the operation, we used
a self-developed wearable bowel sound recording device. The device can be easily
attached to the patient’s abdomen, and the sound data can be collected and stored
conveniently with no interference with the patients’ treatment.
The hypothesis to be tested is that the results of BSs analysis indicate the occur-
rence of changes in BSs in patients undergoing general anesthesia using our sound
recording device, and that it provides theoretical and practical support for the use
of bowel sounds as a reference index for post-anesthesia bowel function recovery
evaluation and postoperative feeding.
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Results
e research design was approved by the Medical Ethics Committee of the Chinese PLA
General Hospital for clinical research (No. 2018-176-01). We randomly selected 26 out
of 339 inpatients from August to October 2019 in the Second Department of Otolar-
yngology, Head and Neck Surgery, at the Chinese PLA General Hospital. Each subject
signed an informed consent form. We recorded clinical factors that might influence
bowel sounds, including age, gender, BMI, and anesthetic type. e patients were also
asked to confirm the absence of intestinal disease to eliminate abnormal changes in
bowel sounds caused by gastrointestinal dysfunction. ree sets of 5-min continuous
BSs were collected from each patient. e first set of data was collected before the oper-
ation (Pre-op) which was defined as the time after fasting for 24h and before entering
the operating room. e second set was collected after entering the recovery room and
completing tracheal extubation (Pro-op). e last set was collected at 3h after extuba-
tion (3h-Pro-op) in the ward if conditions permitted. e acquisition location of bowel
sounds was determined as the right lower abdominal region [10]. To minimize the influ-
ence of different devices and different operators on the accuracy of the tests, one person
used the same device to test the subjects’ BSs during the experiment.
We obtained 70 sets of 5-min BSs from 26 patients as shown in Table1. e 70 data-
sets consisted of 26 sets at preoperative, 26 sets at postoperative and 18 sets at 3h after
surgery.
Characteristic values (CVs) were calculated for each effective bowel sounds (EBS),
including 7 linear parameters and 8 nonlinear parameters. After statistical analysis, all
the p values were adjusted using the false discovery rate (FDR) method, and a value of
p < 0.05 was considered to indicate statistical significance; otherwise, no statistical differ-
ence was considered.
Table2 shows the statistical analysis results of the CVs of EBSs between Pre-op and
after Pro-op. e results show that in the linear time-domain parameter analysis of
BSs, the frequency of EBSs, namely Num_bs, the number of BSs within 5min, is sta-
tistically significant (p < 0.05). e parameters that can represent the intestinal sound
energy include Mean_Mag_bs, Std_Mag_bs, and Sum_bs. Among these three param-
eters, Mean_Mag_bs and Std_Mag_bs had no statistical significance (p > 0.05), but there
was a statistical difference (p < 0.05) in Sum_bs. e duration parameters of intestinal
sounds included Mean_Duration, Std_Duration, and Sum_Duration, in which the differ-
ence between the two parameters of Mean_Duration and Std_Duration was not statisti-
cally significant (p > 0.05), while the Sum_Duration difference was statistically significant
Table 1 Patient data
BMI body mass index
a Values are presented as mean (range)
b Values are presented as mean (standard deviation)
Age (years) 39 (9–77)a
Sex (M/F) 15/11
BMI 24.88 (4.56)b
Operation time (min) 123.65 (67.06)b
Anesthesia type General anesthesia
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(p < 0.05). is indicated that the linear CVs that reflect occurrence frequency, overall
energy and overall duration of EBSs and the nonlinear CVs that reflect the dispersion
degree of stability and complexity of EBSs were statistically significant (p < 0.05). How-
ever, there were no statistically significant differences in the CVs reflecting the energy
and duration, as well as the stability and complexity of local EBSs (p > 0.05). erefore,
the frequency of occurrence, the energy and the duration of BSs were affected by the
operation general anesthesia and were weakened as a whole, but the energy and duration
of local EBSs were not affected, indicating that general anesthesia affected the overall
intestinal peristalsis intensity, but did not inhibit the local intestinal peristalsis state. In
the nonlinear recursive parameter analysis, the mean values of RR, Lmean, ENTR and
TT were not statistically different (p > 0.05). However, the standard deviation of RR,
Lmean, ENTR and TT showed statistical differences (p < 0.05). RR, Lmean and TT all
reflect stability of the signal, while ENTR reflects the complexity. ere was no statisti-
cal difference (p > 0.05) in the mean of the recursive parameters of BSs, but there were
statistical differences (p < 0.05) in the standard deviation of these recursive parameters,
indicating that the dispersion degree of stability and complexity of the system became
smaller.
Table3 shows the statistical analysis of BSs between Pro-op and 3h-Pro-op. e fre-
quency of EBSs increased with statistical significance (p < 0.05) 3h later. Compared with
the Pre-op and Pro-op comparisons, the Mean_Mag_bs, Std_Mag_bs, Mean_Dura-
tion, and Std_Duration, which represent the mean and standard deviation of single EBS
energy and duration, the differences were not statistically significant (p > 0.05). However,
there were statistically significant differences (p < 0.05) between Sum_bs representing
total energy and Sum_Duration representing total duration, both of which were larger,
indicating that the overall energy and duration of BSs had recovered to a certain extent
Table 2 Statistical analysis results betweenPre-op andPro-op
Pre-op before operation, Pro-op after operation
*Values of this line are presented as mean (standard deviation), and the statistical method is the paired t test because the
data are normally distributed
**Values of this line are presented as median (quartile range), and the statistical method is the rank-sum test because the
data are not normally distributed. CVs, characteristic values
CVs Pre-op (n = 26) Pro-op (n = 26) p values
Num_bs 23.23 (13.61) 10.34 (10.86) 0.001*
Sum_bs 5,768,536 (4,326,873) 2,121,638 (1,928,966) 0.004*
Sum_Duration_bs 98,292 (52,350) 45,808 (41,512) 0.001*
Mean_Duration 0.500 (0.287) 0.515 (0.433) 0.790**
Std_Duration 0.660 (0.580) 0.411 (0.372) 0.108*
Mean_Mag_bs 58.177 (38.967) 47.921 (38.363) 0.494*
Std_Mag_bs 39.340 (32.061) 10.124 (35.826) 0.144**
Mean_RR 0.108 (0.050) 0.082 (0.028) 0.086**
Std_RR- 0.066 (0.064) 0.019 (0.034) 0.009**
Mean_Lmean 29.789 (6.750) 27.352 (12.817) 0.518*
Std_Lmean 12.089 (13.025) 6.564 (6.134) 0.017**
Mean_ENTR 4.030 (0.205) 3.947 (0.322) 0.518**
Std_ENTR 0.380 (0.105) 0.234 (0.167) 0.001*
Mean_TT 32.858 (8.974) 30.141 (16.471) 0.518*
Std_TT 17.147 (18.951) 8.405 (9.488) 0.010**
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after 3h. It was consistent with the statistical results of Pre-op and Pro-op, there was no
statistical difference in the mean of the recursive parameters of BSs, but there was a sta-
tistical difference in the standard deviation of these recursive parameters.
Table4 shows the results of the statistical analysis of CVs of Pre-op and 3h-Pro-
op. e results showed that there was no statistical difference (p > 0.05) among all the
parameters, indicating that there was no statistical difference in the intestinal peristalsis
of 3h-Pro-op comparing with Pre-op.
Discussion
General anesthesia can inhibit the gastrointestinal function of patients which includes
delayed gastric emptying, small bowel transit and colonic transit [11, 12]. For this rea-
son, patients undergoing general anesthesia cannot take food immediately after surgery.
A good clinical indication of the return of coordinated bowel motility after surgery can
not only guide postoperative feeding times, but also evaluate the recovery from anes-
thesia. In this study, the data of 5-min BSs at Pre-op, Pro-op, and 3h-Pro-op, which
reflected bowel function at each time point, were tested. After processing and analyzing
the BSs data, the CVs were extracted and statistically analyzed to evaluate the changes
of the BSs before, immediately after and 3h after general anesthesia. e data of Pre-op
and Pro-op were compared to see whether the intestinal function was weakened to illus-
trate the inhibitory effect of general anesthesia on intestinal function. e data collected
at Pro-op and 3h-Pro-op were compared to observe whether intestinal function was
stronger to indicate the recovery status 3h after general anesthesia. We also compared
3h-Pro-op and Pre-op data to see if bowel function returned to the same state before the
general anesthesia.
Table 3 Statistical analysis results ofCVs betweenPro-op and3h-Pro-op
Pro-op, after operation, 3h-Pro-op, 3h after operation
*Values of this line are presented as mean (standard deviation), and the statistical method is the paired t test when the data
are normally distributed
**Values of this line are presented as median (quartile range), and the statistical method is the rank-sum test when the data
are not normally distributed. CVs, characteristic values
CVs Pro-op (n = 18) 3h-Pro-op (n = 18) p values
Num_bs 7.0 (11.5) 20 (24) 0.015**
Sum_bs 1,417,106 (2,695,398) 4,164,854 (7,324,520) 0.036**
Sum_Duration_bs 39,630 (38,007) 83,282 (53,044) 0.015*
Mean_Duration 0.530 (0.300) 0.440 (0.236) 0.249**
Std_Duration 0.328 (0.342) 0.480 (0.366) 0.232*
Mean_Mag_bs 51.892 (41.220) 58.564 (33.982) 0.618*
Std_Mag_bs 10.417 (43.974) 35.874 (62.101) 0.232**
Mean_RR 0.078 (0.027) 0.103 (0.044) 0.170**
Std_RR- 0.029 (0.044) 0.069 (0.037) 0.036*
Mean_Lmean 28.048 (15.141) 30.685 (8.675) 0.618*
Std_Lmean 5.322 (8.423) 12.214 (9.214) 0.036**
Mean_ENTR 4.001 (0.523) 3.972 (0.264) 0.146**
Std_ENTR 0.191 (0.154) 0.393 (0.100) 0.015*
Mean_TT 30.794 (19.410) 33.285 (12.157) 0.646*
Std_TT 6.223 (9.759) 16.801 (12.988) 0.036**
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e statistical results of the CVs show that (1) the differences in BSs between Pre-
op and Pro-op were statistically significant. (2) e differences in BSs between Pro-op
and 3h-Pro-op were statistically significant. (3) ere were no significant differences
between Pre-op and 3 h-Pro-op BSs. Specifically, the effect of general anesthesia on
bowel function is holistic. In the statistical analysis between Pre-op and Pro-op, as well
as between Pro-op and 3h-Pro-op, there was no statistical difference in characteristic
values of local BSs in the linear time-domain, but the differences in the overall occur-
rence frequency, total energy, and duration of intestinal sounds were statistically signifi-
cant. And there was a weaker trend after surgery compared with that before surgery, and
there was a stronger trend at recovery after 3h compared with that immediately after
surgery. Among the nonlinear dynamic parameters, there was no statistical difference in
the mean value of the parameters that could express the complexity and stability of the
local BSs, but the difference in the standard deviations of the nonlinear parameters was
statistically significant, indicating that the complexity and stability dispersion degree of
the BSs changed after general anesthesia. e degree of dispersion was smaller immedi-
ately after the operation, and recovered within 3h. e statistical analysis of Pre-op and
3h-Pro-op data showed that there were no statistical differences in either local char-
acteristic parameters or overall characteristic parameters, whether it was linear time-
domain parameters or nonlinear dynamic parameters, indicating that intestinal function
had returned to the preoperative state to a certain extent 3h after surgery.
In this study, 5min of BSs at Pre-op, Pro-op and 3h-Pro-op were collected to repre-
sent the intestinal status at the three periods, which has certain limitations. Under ideal
conditions, the BSs should be measured continuously from the preoperative to the post-
operative period, but the current surgical environment and perioperative nursing pro-
cedures do not allow for full-time measurement. However, the 5-min BSs can effectively
Table 4 Statistical analysis results ofcharacteristic values betweenPro-op and3h-Pro-op
Pro-op after operation, 3h-Pro-op 3h after operation
*Values of this line are presented as mean (standard deviation), and the statistical method is the paired t test when the data
are normally distributed
**Values of this line are presented as median (quartile range), and the statistical method is the rank-sum test when the data
are not normally distributed. CVs, characteristic values
CVs Pre-op (n = 18) 3h-Pro-op (n = 18) p values
Num_bs 23.5 (18.25) 20 (24) 0.979**
Sum_bs 6,150,012 (4,826,065) 5,483,036 (5,221,266) 0.979*
Sum_Duration_bs 100,070 (43,353) 83,282 (53,044) 0.865*
Mean_Duration 0.565 (0.492) 0.440 (0.236) 0.645**
Std_Duration 0.754 (0.669) 0.480 (0.366) 0.865*
Mean_Mag_bs 57.345 (42.065) 58.564 (33.982) 0.979*
Std_Mag_bs 34.746 (20.342) 42.825 (31.226) 0.865*
Mean_RR 0.114 (0.049) 0.120 (0.054) 0.979*
Std_RR- 0.050 (0.057) 0.068 (0.032) 0.865**
Mean_Lmean 29.129 (7.657) 30.685 (8.675) 0.979*
Std_Lmean 12.679 (9.011) 13.601 (7.473) 0.979*
Mean_ENTR 4.023 (0.199) 4.022 (0.237) 0.988*
Std_ENTR 0.352 (0.107) 0.393 (0.100) 0.865*
Mean_TT 32.337 (10.111) 33.285 (12.157) 0.979*
Std_TT 17.746 (11.587) 18.331 (9.279) 0.979*
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reflect the intestinal function to a certain extent [13, 14], so research based on the 5-min
BSs is effective.
Another limitation is that many empirical thresholds are used in the analysis, espe-
cially in the recognition of EBSs. Will the subjectivity of these empirical values affect
the judgment of the results of general anesthesia on the change of bowel sounds? e
answer is no. In this study, the same recognition threshold and methods were used to
analyze and identify the intestinal sound data of the tested patients at Pre-op, Pro-op
and 3h-Pro-op, in order to judge the changing trend at these three time points, so it is
not affected. Furthermore, in the current study of BSs, there is no recognized gold stand-
ard for the recognition accuracy of EBSs, and there is no standardized database to ver-
ify the accuracy. Most of the reference standards in the current study are based on the
subjective judgment of clinicians, but the accuracy of such subjective judgment is also
questionable [8]. Future research might use the device we made to test BSs at different
intervals postoperation to determine the time it takes for intestinal function to return to
pre-op status in individuals to more accurately judge the optimal postoperation feeding
times.
Conclusion
In conclusion, the hypothesis to be tested was supported as BSs changed before, imme-
diately after and 3h after general anesthesia in the way we predicted. e BSs weakened
during surgery, and 3h later, the BSs returned to the preoperative state. erefore, the
BSs using the parameters we identified can be used as an indicator of intestinal function
changes after general anesthesia, so as to provide guidance for postoperative feeding,
which is of great clinical significance.
Methods
Data collection
Patients’ BSs were collected using a self-developed wearable bowel sound device. e
device uses a Knowles’ SiSonic MEMS microphone (SPU1410LR5H-QB), which has an
ultra-wide band (UWB) flat frequency response (± 2dB, 10Hz–10kHz) and a tightly
matched sensitivity of ± 3dB. Since the frequency of BSs is mainly distributed within
the 100Hz–1kHz band, this microphone is practical for the pick-up of BSs. e bowel
sound and the ambient noise acquired by the microphones are filtered and amplified
through the second-order active low-pass filter first. e cut-off frequency of the low-
pass filter is 2kHz, and the magnification is 2 times. After the filter, a stage of amplifica-
tion was carried out, and the amplification factor was 30. e amplified analog signal
enters the analog–digital converter (12bit) of STM32L151 to realize analog-to-digital
conversion. e sample rate of BSs was 8kHz and the converted data are stored in the
Micro-SD card.
Signal processing
In the process of BSs’ acquisition, the ambient noise is easily introduced, which directly
affects the quality of the BS signal. erefore, it is necessary to remove the ambient noise
to better analyze and identify the BSs. We used the noise acquisition channel of the
recorder to collect the ambient noise, and the adaptive noise cancelation was used to
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remove the noise. Specifically, the least mean square (LMS) [15] algorithm was adopted
because the LMS algorithm is more robust than the recursive least squares (RLS) algo-
rithm [16]. e order of the filter was determined to be 32, and the step size factor was
set as 0.000001 to achieve a good adaptive cancelation.
Adaptive filtering can eliminate the environmental noise, but the high-frequency noise
in the signal still affects the identification and analysis of effective bowel sounds (EBSs).
As an effective and practical method, wavelet denoising has achieved good results in sig-
nal and image denoising, and has been widely used in engineering applications includ-
ing the enhancement of bowel sounds [17]. Donoho [18] and Walker [19] proposed a
wavelet threshold denoising method. e wavelet coefficient of signal contains impor-
tant information after wavelet transformation using the Mallat algorithm. e wavelet
coefficient of the noise is less than the wavelet coefficient of the signal. By selecting a
suitable threshold, the wavelet coefficients greater than the threshold are considered to
be generated by BS signals and should be retained, while those less than the threshold
are considered to be generated by external noise and set to zero to achieve the purpose
of denoising. In the process of wavelet decomposition, the wavelet basis, the number of
decomposition layers and the threshold should be determined. For the selection of wave-
let basis, we chose sym8 wavelet basis which is from the two common wavelet bases of
db wavelet system and sym wavelet system. For determining the number of decomposi-
tion layers, too large or too small will both affect the final denoising effect. In this paper,
the number of decomposition layers was determined to be 5 after comparing the denois-
ing effects of different decomposition layers. For the determination of threshold value,
the Birge-Massart [20] algorithm was used to obtain the threshold value of each layer of
one-dimensional wavelet transform, and soft threshold function was used for denoising.
For bowel sound signals, there is no standard signal to refer to, so the wavelet denoising
was used combining with adaptive filtering, to keep the frequency response range below
1kHz [21] which is the main frequency range of bowel sounds.
After the adaptive filtering and wavelet denoising, the waveform (Fig.1) can be used to
identify EBSs. e fractal dimension (FD) can quantitatively describe the complexity of
the signal. e FD of EBSs is different from that of background sounds [22]. To calculate
the FD of a time series, we can either reconstruct the phase space first and then calcu-
late the correlation dimension of the time series [23–25] or directly calculate the FD in
the time-domain. e time series in this paper was the audio signal with a high sam-
pling rate and large data volume, so the FD was calculated directly in the time-domain.
e Katz method [22, 26] used in FD calculation can effectively judge the randomness of
waveforms. When calculating the FD of the BS signal, we employed a sliding window to
realize the short-time processing of audio signals. e length of the sliding window was
set to int (0.006*fs), where int indicates the integer part of the argument, and fs is the
sampling frequency of the BS signal. e constant 0.006 is empirically set and justified
by the efficient performance of the algorithm [22]. e FD of the data in the sliding win-
dow is calculated. To ensure that the length of the data before and after calculating the
FD is equal, the first and the last FD are used to make up the data at both ends. After the
FD sequence is calculated, the peak value is extracted to ensure the effective recognition
of the BSs. e peak extraction method adopts the FD-peak peeling algorithm (FD-PPA)
[22].
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Wangetal. BioMed Eng OnLine (2020) 19:60
FD-PPA makes the EBSs more obvious in the waveform, but the voice endpoint detec-
tion (VAD) technology is needed to extract the EBSs. e purpose of VAD technology
is to identify the starting point and ending point of EBSs accurately from a segment of
the signal containing EBSs to distinguish the EBS and the non-BS signal. It is an impor-
tant aspect of speech processing technology. As for the BS signal, we identified the EBSs
which satisfied certain conditions, while the others are considered as non-BS signals.
In this paper, the time series after FD-PPA were used as the input sequence to judge
the starting and ending points of EBSs. e threshold for entering the BS segment, the
length threshold of identified noise, and the maximum allowed mute length in the BS
segment are set. Based on the above three thresholds, the endpoint of EBSs was deter-
mined. As a rule of thumb, the first is the threshold for entering BS segments which was
set to 1.01. When the input value is greater than 1.01, it is considered to be the starting
point of EBSs. e second parameter is the minimum duration threshold of the EBS sig-
nal, and the BS segment less than this threshold is considered as noise. And this thresh-
old is set to 50ms. e maximum mute length allowed in the BS segment is the third
threshold which was set to 250ms. If the mute length in the BS segment is less than this
value, the BS is considered unfinished; otherwise, the BS segment is considered finished.
After the VAD, there are also many kinds of vocal signals mixed in, such as heart
sounds, breath sounds and background noises similar to BSs. Limited to the problem
26.7 26.7526.826.85 26.9 26.9527
Time(Sec)
-0.1
0
0.1
Amp(V)
(a)
26.7 26.7526.826.85 26.9 26.9527
Time(Sec)
-0.1
0
0.1
Amp(V)
(b)
26.7 26.7526.826.85 26.9 26.9527
Time(Sec)
-0.1
0
0.1
Amp(V)
(c)
26.7 26.7526.826.85 26.9 26.9527
Time(Sec)
-0.1
0
0.1
Amp(V)
(d)
Fig. 1 The bowel sounds’ signal after adaptive filtering and wavelet denoising. a The original signal is
obtained from the microphone after amplification without any cancelation, and b the ambient noise is
collected from the other microphone. c The signal after LMS adaptive filter. d The signal after wavelet
denoising
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Wangetal. BioMed Eng OnLine (2020) 19:60
of environmental noise collection and filter residue, we set three thresholds to remove
three kinds of residual noise based on experience. Specifically, the envelope of each EBS
was obtained by complex analytic wavelet transformation [27]. en, we excluded the
sound segment whose envelope maximum value was less than 0.037V, which meant that
a sound segment with a too small amplitude is considered as noise. In the measured
data, the confounding heart sounds is obvious. We extracted the envelope of sound seg-
ment and calculated the peak number. And based on the experience in judging heart
sounds we ruled out the sound segment whose peak value was less than 3. We also found
that for BS segments with a very small signal-to-noise ratio, there was residual noise and
it was identified as a gut sound, which also needed to be removed. As for this speech
segment with residual noise, we filtered out the envelope peak number which was more
than 3 in the length of 1000 sampling points based on experience.
Characteristic values’ extraction
e characteristic values (CVs) can quantitatively reflect the characteristics of BSs, so we
extracted linear and nonlinear CVs for quantitative evaluation and statistical analysis.
e linear CVs are mainly time-domain parameters, as shown in Table5.
Physiological signals have been shown to be chaotic [28]. As the basic physiological
signal, gut sound also has nonlinear dynamic characteristics. erefore, nonlinear CVs
were calculated in this paper. Recurrence quantification analysis (RQA) [29] can meas-
ure the complexity of a short and non-stationary characteristic signal with noise [30].
It has been broadly applied in the analysis of physiological data [31–33]. In this paper,
phase space reconstruction was carried out for each EBS signal. Based on the recursive
graph, recursive quantitative analysis was carried out and quantitative parameters were
extracted [34], as shown in Table6. ere are multiple EBSs in each period, so to real-
ize the subsequent statistical analysis, the mean value (Mean_) and standard deviation
(Std_) of each CV in each period were calculated.
Figure2 shows an overview of BSs data acquisition, processing, and analysis, and
f(t)
is calculated as Eq. (1).
Statistical analysis
We attempted to analyze the differences in BSs at Pre-op, Pro-op and 3h-Pro-op. e
CVs can quantitatively represent the signals, so we conducted statistical analysis on the
CVs of the 26 patients between Pre-op and Pro-op, the 18 patients between Pro-op and
3h-Pro-op, and the 18 patients between Pre-op and 3h-Pro-op. And then determined
whether there were statistical differences. e CVs for statistical analysis included linear
CVs and the means and standard deviations of nonlinear CVs.
Statistical analyses were performed using IBM SPSS Statistics 25. Each set of statistical
analyses was taken from the same patients at two moments. And the normal distribu-
tion test was performed before statistical analysis. Normality test is performed for each
(1)
f(t)=

∫∞
−∞ 

f(t)


2dt
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Wangetal. BioMed Eng OnLine (2020) 19:60
set of data using Shapiro–Wilk normal test. If the data are normally distributed, the sig-
nificance level (i.e., p value) should be greater than 0.05; otherwise, the p value should
be less than 0.05. So for data satisfying normal distribution, the parametric statistical
method of paired t test was used. Otherwise, the rank-sum test was used. A value of
p < 0.05 was considered to indicate statistical significance.
Abbreviations
BSs: Bowel sounds; Pre-op: Before operation; Pro-op: After operation; 3 h-Pro-op: Three hours after operation; EBSs: Effec-
tive bowel sounds; CVs: Characteristic values; DMR: Dynamic magnetic resonance; LMS: Least mean square; FD: Fractal
dimension; VAD: Voice endpoint detection; RQA: Recurrence quantification analysis.
Acknowledgements
We thank the Department of Otolaryngology and Head and Neck Surgery and Anesthesia recovery room personnel of
the First Medical Center (Chinese PLA General Hospital, Beijing, China) for their assistance in collecting data for the study.
Authors’ contributions
GW, MW, and WW mainly designed the experiment, analyzed the data, achieved the result and writing the manuscript.
HL analyzed the data and writing the manuscript. SZ and LL helped to acquire data. All authors read and approved the
final manuscript.
Funding
This work was supported by the National Natural Science Foundation of China (Grant No. 61701540).
Availability of data and materials
The datasets generated during and/or analyzed during the current study are available from the corresponding author at
reasonable request.
Table 5 The linear CVs ofEBSs
CVs characteristic values, EBSs eective bowel sounds
Linear CVs Calculation methods Physiological signicance
Num_bs The number of identified effective bowel
sounds during the measurement Frequency of bowel sounds in the 5 min
Sum_bs The sum of the absolute values of the identi-
fied effective bowel sounds Reflecting the total energy of the bowel
sounds
Sum_Duration_bs The sum of the duration of the identified
effective bowel sounds Reflecting the total duration of bowel sounds
Mean_Duration The mean of the duration of effective bowel
sounds Mean duration of effective bowel sounds
Std_Duration The standard deviation of the duration of
effective bowel sounds Standard deviation of duration of effective
bowel sounds
Mean_Mag_bs The mean of the mean absolute value of
effective bowel sounds The average energy of effective bowel sounds
Std_Mag_bs The standard deviation of the mean abso-
lute value of effective bowel sounds The standard deviation of the energy of the
effective bowel sound
Table 6 The nonlinear CVs ofEBSs
CVs characteristic values, EBSs eective bowel sounds
Nonlinear CVs Calculation methods Physiological signicance
RR The percentage of recurrent points falling within the
specified radius Reflect the similarity of signal fluctuation
Lmean The mean of the diagonal lengths in recurrence plot Related to the separation velocity of
adjacent trajectories
ENTR The Shannon information entropy of all diagonal line
lengths A measure of signal complexity
TT The average length of vertical line structures Degree of system stability
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Wangetal. BioMed Eng OnLine (2020) 19:60
Ethics approval and consent to participate
The research has been approved by the Medical Ethics Committee of Chinese PLA General Hospital for clinical research
(No. 2018-176-01). Each subject signed the informed consent.
Consent for publication
When bowel sounds were obtained from patients, prior approval was obtained for future use of these records.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology,
Chinese PLA General Hospital, Beijing, China. 2 Department of Medical Engineering, Medical Care Center, Chinese PLA
General Hospital, Beijing, China. 3 Bioengineering Research Center, Medical Innovation Research Division, Chinese PLA
General Hospital, Beijing, China. 4 Anesthesia and Operation Center, Chinese PLA General Hospital, Beijing, China. 5 Col-
lege of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China.
Received: 9 April 2020 Accepted: 24 July 2020
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