# Still in Womb: Intrauterine Acoustic Embedded Active  Noise Control for Infant Incubators

Authors: Lichuan Liu; Shruthi Gujjula; Priya Thanigai; Sen M. Kuo
Journal: Advances in Acoustics and Vibration
 (2008)

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## Abstract

Excessive noise in neonatal care units and inside incubators can have a number of detrimental effects on an infant's health. We proposed a novel, audio-integrated approach to \tccheck{achieve} active noise control (ANC) for infant incubators. We also presented the implementation of the robust, nonlinear filtered-X least mean M-estimate algorithm, for reducing impulsive interference in incubators. The healthcare application is further enhanced by integrating the “womb
effect”, that is, by using intrauterine and maternal heart sounds, proven to be beneficial to infant health, for soothing the infant and masking the residual noise. A computer model for audio-integrated noise cancellation utilizing experimentally measured transfer functions is developed for simulations using real medical equipment noise. The simulation of the audio integrated ANC system produced optimal results and the system was further validated by real-time experiments to be robust and efficient.


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## Body

## 1. Introduction

Neonatal
intensive care units (NICU) house and treat premature infants until their organ
systems are considered fully developed. These infants are enclosed in
incubators, as shown in Figure1, that monitor their vital statistics and
ensure that environmental conditions are maintained at optimum levels. The
incubators create the precise and consistent environment [1], such as temperature and
humidity, controlled by microprocessor. However, according to the American
Academy of Pediatrics [2], high noise levels are common in the NICU and in
incubators, causing considerable auditory damage to preterm infants [3]. The noise is typically due
to ventilation or breathing equipment and human activity. Figure 2 is an
example of the real incubator noise in time domain with segments marked by
impulse due to respiratory pumps and the background equipment hum. The
consequences of exposing infants to incubator noise vary from short-term
effects such as sleep disturbance to long-term effects such as delayed speech
development. To reduce medical equipment noise and external noise from the
NICU, passive control systems such as absorbers [4] are not always efficient. This puts forth a need for
an active noise control (ANC) system that can cancel noise inside the incubator
adaptively [5, 6]. Another approach to
create a healthier ambience in NICUs is the
introduction of intrauterine audio into the incubator that allows the infant to
feel comforted. Intrauterine audio is a combination of low-frequency sounds
from the womb and includes the sound of the muffled heartbeat which can be
heard distinctly in the background.Figure 1
Mobile incubator unit: Giraffe Incubator by GE Healthcare.Figure 2
Example wave
form of incubator noise, sampling frequencyfs=4KHz.However, neither playing soothing audio nor applying
an ANC system is individually efficient creating the need for an integrated
system that can reduce harmful equipment noise while simultaneously playing
beneficial intrauterine audio. To achieve this end, this paper proposes an
innovative application for neonatal healthcare—the intrauterine acoustics
embedded active noise controller. The integrated system aims at recreating
prenatal ambience for premature infants who are required to spend extended
periods enclosed inside infant incubators.Section2 of the paper discusses the positive effects
of playing uterine audio to premature infants. These positive effects are both
medical and psychological, and reflect results from studies carried out over
the last three decades. Section 3 focuses on developing an ANC system utilizing
the filtered-X least mean square (FXLMS) algorithm for cancellation of
broadband noise using transfer functions measured from the real GE Healthcare
Girraffe incubator. The laboratory setup was
modeled using the same incubator shown in Figure 1. Section 4 introduces the novel
filtered-X least mean M-estimate (FXLMM) algorithm that is found to be
statistically robust in the presence of impulsive interference in the input.
Section 5 outlines the audio-integration algorithm that introduces intrauterine
audio and allows it to be played simultaneously while the ANC system is in
operation. This integration serves two important purposes—it provides a
potential health benefit for infants by utilizing womb sounds as heard by the
infant and also masks the residual noise after noise cancellation has been
performed. The algorithm is intended to prevent interference from the soothing
audio on the performance of the ANC algorithm and ensures that the audio is not
cancelled by the ANC system. The audio interference cancellation filter also
performs online modeling of the secondary path to enhance the performance of
the ANC system. Section 6 shows the simulation and real time experiment
results.


## 2. A Study of Neonatal Response to Uterine Sounds

This section
briefly reviews that the numerous benefits intrauterine audio has on neonatal
growth from [5]. It is
widely accepted that the brain of the fetus develops while it is inside the
womb. An infant's ears begin to develop when it is around eight weeks old and
can be considered fully developed by the twenty-fourth week. The development of
the inner ears and the nerve endings from the brain
is  so advanced that the baby can hear the muffled
sounds of the heartbeat and the blood flowing through the umbilical cord. The
human cochlear system, which is considered fully developed by the twenty-fourth
week, transforms acoustic vibrations into nervous influx allowing infants to
have an understanding of rhythm at a very early stage [7]. These sounds form an
imprint on the fetal brain and it has been verified that post birth, the infant
is comforted while listening to it.Playing soothing audio has always been known to
relieve stress and has in recent years become an established form of therapy.
There have been a number of studies that indicate that music has a positive
impact on premature infants yet the kind of audio to be played is contentious.
The various available options include playing nature sounds, live and recorded
music. But “womb music” has consistently been considered the most
favorable choice. According to [7] the womb is not a silent place and is typically awash
with sounds. Sounds that are heard inside the uterus include maternal
heartbeat, respiration, intestinal gurgling and sounds from blood vessels. The
maternal heartbeat heard by the infant is a muffled version of the original as
it passes through layers of tissues before reaching the infant. A study
conducted by   Rosner and Doherty in [8] states that “playing prerecorded intrauterine sounds
to newborns reportedly soothes the babies.” The study concluded that 90%
of infants who listened to intrauterine audio were calmed down significantly.In another study conducted by Murooka et al. [9], the authors used a
piezoelectric microphone to record and analyze intrauterine sounds. The sounds
were found to be mainly from blood vessels and were found to produce a calming
effect on 86% of the infants, and 30% of the infants were found to have
increased sleep cycles. The authors asserted that playing such sounds
externally recreates the “in-utero” ambience for infants [9]. A pioneering study
conducted by Salk [10]
exposed neonates to prerecorded maternal heartbeat and concluded that test
infants showed increased weight gain and food intake. Flowers, McCain, and
Hilker combined uterine sounds with soft ballads and tested the impact of music
on nine African-American premature infants. The infants displayed improvement
in respiration rate, oxygen saturation, and time spent in sleeping [11].This paper therefore proposes the utilization of sound
files from a commercially available product—the Baby Sleep System [12]. The soothing audio
consists of intrauterine heartbeat recorded through a condenser microphone,
which is a very accurate representation of uterine sounds as heard by the
infant. The heartbeats were taken at 72 beats per minute, the rate of a relaxed
adult heart. They were combined with the sound of blood and fluid movement to
produce an “in-utero” effect for the infant. This audio was incorporated
along with the ANC system and serves two main purposes. The ANC system is
optimized to cancel equipment and external NICU noise to the maximum possible
extent. The audio integration allows for the soothing audio to be played
continuously without interfering with the ANC system. Also, the integrated
system can be considered cost effective as the power amplifiers and
loudspeakers used by the ANC system can be used for playing the soothing audio,
thus maximizing the utility of resources.


## 3. Active Noise Control for the Incubators

The noise in
incubator can be classified as broadband noise because it covers a wide range
of frequencies [13].
The noise sources are some medical equipments in the ICU, such as a blowers,
nebulizers, humidifiers, and pumps. Figure 3 shows the magnitude spectrum of
the recorded sample of broadband incubator noise. We can find that the power of
the noise is spread over a wide spectrum of the noise signal. The ANC systems
can be used to cancel this high-power wideband noise.Figure 3
Magnitude response of the incubator wideband noise.ANC is based on the principle of untilizing
destructive interference to cancel unwanted noise. The objective of an ANC
system is to generate an “antinoise” to cancel the primary noise. The amount of
noise which can be cancelled depends on the
accuracy of the amplitude and phase of this
antinoise [14].The block diagram of a feedforward broadband ANC
system using the FXLMS algorithm is illustrated in Figure4, where P(z) is the transfer function of the primary path
from the noise source to the error microphones, S(z) is the transfer function of secondary path and S^(z) is its estimate. The primary noise d(n) inside the incubator is cancelled by the
antinoise y(n) generated by the adaptive filter W(z).
The antinoise is produced by the secondary loudspeakers and e(n) is the residual noise picked up by the error
microphone. In Figure 4, S(z) which is the secondary path between e(n) and y(n),
includes the secondary loudspeakers, error microphones, and acoustic path
between the loudspeakers and the error microphones. The secondary path is
modeled offline and retained during the online operation of ANC. The estimate
compensates for the secondary-path effects [15].Figure 4
Block
diagram of ANC system with the FXLMS algorithm.The output of the adaptive filter can be represented
as [15](1)y(n)=wT(n)x(n),where w(n)=[w0(n)w1(n)…wL−1(n)]T is the coefficient vector of the adaptive
filter W(z) and x(n)=[x(n)x(n−1)…x(n−L+1)]T is the L×1 reference signal vector. The signal y(n) is filtered through the secondary path S(z) and is subtracted from the primary noise d(n) to generate the residual error e(n).
The equations for simulation are given by(2)d(n)=p(n)∗x(n),y′(n)=s(n)∗y(n),e(n)=d(n)−y′(n)=d(n)−s(n)∗[wT(n)x(n)],where ∗ denotes the convolution operator, and p(n) and s(n) are the primary and secondary path responses,
respectively. All these operations are carried out by the system internally and
the signals picked up in real-time ANC are the reference signal x(n) and the residual error e(n).
For the adaptive filter, the weight update equation is(3)w(n+1)=w(n)+μe(n)x'(n),where μ is the step size and x'(n)=[x′(n)x′(n−1)…x′(n−L+1)]T is the reference signal vector x(n) filtered by the secondary path model S^(z),(4)x'(n)=s^(n)∗x(n),where s^(n) is an accurate estimate of s(n).The experimental setup is shown in Figure5. One
microphone is placed on either side of the infant head. The outputs from both
are analyzed by a spectrum analyzer. The cancelling loudspeakers are placed in
the incubator, and can be seen behind the infant head. Typically the offline
modeling of the secondary paths from the cancelling loudspeakers to the error
microphones is using adaptive filters with the least mean square algorithm. The
magnitude responses of the primary paths P(z) from the experimental setup are shown in
Figure 6.Figure 5
Experiment setup by using the GE
Healthcare Giraffe Incubator.Magnitude responses of the
primary paths.Typically, white noise is used for adaptive system
identification. But it is found to be annoying especially in sensitive
environments like the NICU. The proposed method utilizes offline modeling
approach. Nature's sound, in this case, the sound of a flowing stream is used.
Nature's sounds are preferred owing to their flat spectrum and their pleasing
effect on the listener. The secondary paths estimator converged for a filter
length of30.
Satisfactory results of offline modeling are shown in Figure 7.Magnitude
responses of the secondary  paths.


## 4. Nonlinear Algorithm for Impulse Noise Suppression

The performance
of the linear adaptive filters degrade dramatically in the presence of impulse
noise, therefore nonlinear algorithms are capable of reducing the adverse
effects [16]. The FXLMM
algorithm is a simple and robust method. It employs the mean M-estimation error
objective function and is capable of performing effectively in impulsive
environment [17–19].The objective of the adaptive filterW(z) is to minimize the least M-estimate function
criterion ρ[e(i)] where ρ(⋅) is the M-estimate function. The coefficient
vector w(n) is updated in the negative direction of the
gradient vector(5)w(n+1)=w(n)−μ∇JMPand the objective function
is(6)JMP≡E[ρ[e(n)]]≅ρ[e(n)],where E[⋅] is the expectation operator, ρ(⋅) is chosen to be the Hampel three-part
redescending M-estimate function, which is well known for its computational simplicity.
It defines as
(7)ρ[e(n)]={e2(n)2,0≤|e(n)|<ξ,ξ|e(n)|−ξ22,ξ≤|e(n)|<Δ1,ξ2(Δ1+Δ2)−ξ22+(ξ|e(n)|−Δ2)22(Δ1−Δ2),Δ1≤|e(n)|<Δ2,ξ2(Δ1+Δ2)−ξ22,Δ2≤|e(n)|,where ξ, Δ1,
and Δ2 are the threshold parameters.The objective function is minimized by(8)∇JMP=∂JMP(n)∂w(n)=∂ρ[e(n)]∂e(n)∂e(n)∂w(n).Letψ[e(n)] be the first-order partial derivative of ρ[e(n)],
(8) becomes(9)∇JMP=ψ[e(n)]∂e(n)∂w(n)=ψ[e(n)][−s(n)∗x(n)]=−q[e(n)]e(n)[s(n)∗x(n)].Define q[e(n)]≡ψ[e(n)]/e(n) as the weight function. Since s(n) is the impulse response of the secondary path
and not available directly, we use its estimation to calculate the
gradient,(10)∇JMP≅−q[e(n)]e(n)[s^(n)∗x(n)]=−q[e(n)]e(n)x'(n).Substituting (10) into (5), we can get the weight
vector update equation as(11)w(n+1)=w(n)+μq[e(n)]e(n)x'(n),where μ is the step size parameter. Equation (11) is
known as the least M-estimate algorithm and can be viewed as a generalization
of the LMS algorithm. It becomes identical to the LMS algorithm when noise e(n) is less than a threshold ξ.
When the signal error e(n)>ξ, q[e(n)] in (11) decreases and reaches 0 when e(n)>Δ2.
Thus, the least M-estimate algorithm is capable of reducing the effect of large
signal error during the updating process [17].


## 5. Intrauterine Acoustics Embedded Active Noise Controller

This section
develops an algorithm that can integrate the “comfort” audio with the
existing ANC system, and provide an environment that is capable of improving
the health of the infant by masking the undesired residual noise. The comfort
audio used is a combination of maternal heartbeat and other intrauterine
sounds [12]. Research
has proven that playing womb sound to infant in incubator showed significant
benefit in the respiration rate, sleep cycle, and oxygen saturation [11]. Unfortunately, there are
two main issues with the integration of audio to the ANC system need to be
considered: first, the audio signal can act as interference to the ANC system
and impede proper adaptation; and second, the ANC system can cancel the
intended soothing sound. Hence, a method must be devised to
subtract the audio from error signal before it is used to
update the coefficients of the adaptive filter [20]. The block diagram of the
audio integration algorithm is shown in Figure 8. The soothing audio a(n) is added to y(n) and can be heard by the infant inside the
incubator.Figure 8
Block diagram of the
audio-integrated ANC system.At the acoustic summing junction, the antinoisey′(n) and the primary noise d(n) are combined to produce the residual error e_a(n).
It contains the true error (residual noise) e(n) and the component of audio. Therefore, by
subtracting the audio from the residual error e_a(n),
we can get the true error, then the true error is used to update the weight
vector of the adaptive filter W(z).
It  should be noted that the audio signal passed the secondary path, and
filtered by S(z),
then it is subtracted. The z transform of residual error  e_a(n) can be expressed as [21](12)E_A(z)=D(z)−S(z)[Y(z)+A(z)].The adaptive filterS^(z) is used to cancel the audio interference on
the performance of W(z).
This filter generates(13)E(z)=E_A(z)+S^(z)A(z).Then we can get the following equation by substituting
(12) into (13):(14)E(z)=D(z)−S(z)Y(z).We assume thatS(z)=S^(z) and the audio is uncorrelated with the primary
noise. Then (13) can be expressed in time domain as(15)e(n)=d(n)−[y(n)∗s(n)]which is the true error used to
update W(z) by using the FXLMS system.The main advantage of this algorithm lies in its
ability to model the secondary path online. This involves the estimation of the
secondary path in parallel with the operation of the ANC system. TheS(z) filter is modeled through a system
identification scheme. It uses soothing audio as the reference signal and
treats the secondary path as the unknown system. This makes the algorithm
sensitive to time-varying secondary paths.The key advantages of the intrauterine acoustic
embedded ANC system can be summarized as follows. (i) It re-establishes pre-natal
ambience thus fostering infant health. (ii) The secondary path is modeled
online making the system more receptive to changes in the environment. (iii) It
is successful in masking residual error and in preventing the audio from
interfering with the updation. (iv) The audio integration does not require
supplementary hardware, existing speakers and power amplifier of the ANC system
can be used making it cost effective.


## 6. Simulation and Experiment Results

## 6.1. Multichannel FXLMS Algorithm

In the previous
sections, we described  the single channel ANC system. In this section, an
example of multichannel ANC system,1×2×2 FXLMS algorithm is used for real experiment. Figure 9 shows the multichannel feedforward ANC system using the 1×2×2 FXLMS algorithm. In this system, two secondary
speakers and two error microphones are used independently. These two error
microphones pick up the residual errors e1(n) and e2(n) at different positions, thus able to form two
individual quiet zones centered at the error microphones. The ANC algorithm
used two adaptive filters W1(z) and W2(z) to generate antinoise y1(n) and y2(n) to drive the two independent secondary
speakers. In Figure 9, d1(n) and d2(n) are the primary noises to be cancelled, S11(z), S12(z), S21(z),
and S22(z) are the secondary path transfer functions, and P1(z) and P2(z) are the primary path transfer functions.Figure 9
Block Diagram of the1×2×2 FXLMS algorithm.The multichannel FXLMS algorithm is summarized as
follows:(16)yi(n)=wiT(n)x(n),i=1,2,wi(n+1)=wi(n)+μi[ei(n)x(n)∗s^i1(n)+ei(n)x(n)∗s^i2(n)],i=1,2,
where w1(n) and w2(n) are weight vectors of the adaptive filters W1(z) and W2(z),
respectively, μ1 and μ2 are the step sizes, s^11(n), s^12(n), s^21(n),
and s^22(n) are the impulse responses of S^11(z), S^12(z), S^21(z),
and S^22(z),
respectively.Similar to the multichannel ANC system (as shown in
Figure9), we extended the single-channel audio-integrated ANC system (as shown
in Figure 8) into a 1×2×2 multichanel system. In this multichannel
intrauterine acoustics embedded ANC system, the two adaptive filters W1(z) and W2(z) are used to update the two antinoise y1(n) and y2(n).


## 6.2. Simulation Results

To evaluate the
performance of the innovative intrauterine acoustic embedded ANC, we
investigate the noise cancellation achievement through simulation and real time
experiment.In the simulation, we apply the intrauterine acoustics
embedded ANC system described in Section6.1. The input reference noise is
taken from an incubator noise audio file at first. The ANC system is simulated
with measured P1(z), P2(z), S11(z), S12(z), S21(z),
and S22(z).
A 60-tap filter with step size of 0.1 is used for the adaptive noise cancellation
filter W1(z) and W2(z).
The residual noise is found to be 16 dB lower than the input on average. The
plots illustrating the spectra of noise before (ANC
OFF) and after (ANC ON) cancellation at left error microphone and right error
microphone after assigning intrauterine audio are
shown in Figures 10 and 11.Figure 10
Simulated spectra at left error
microphone before (ANC OFF) and after (ANC ON) active noise control.Figure 11
Simulated spectra at right
error microphone before (ANC OFF) and after (ANC ON) active noise control.To demonstrate the impulse noise suppress by nonlinear
algorithms, the noise signal is interspersed with high-amplitude random
impulses (30 dB higher than background). The impulses are at timen=40000, 52000,
and 64000 and last for a length of 100 samples. The FXLMM algorithm was implemented
for the audio-integrated ANC system. The probabilities θξ, θΔ1,
and θΔ2 for determining the threshold were taken to be
0.05, 0.025, and 0.005, respectively, for 95%, 97.5%, and 99.5% confidence that
the error vector was in the interval [0,ξ], [0,Δ1],
and [0,Δ2],
respectively [17]. A 60-tap adaptive filter with a step size of 0.0007 was implemented. The results of incubator noise
cancellation are shown in Figures 12 and 13.Figure 12
Learning curves at left error
microphone for the FXLMS and FXLMM algorithms (μ=0.0007,
impulse occurred at n=40000, 62000, and 64000).Figure 13
Learning curves at right
error microphone for the FXLMS and FXLMM algorithms (μ=0.0007,
impulse occurred at n=40000, 62000, and 64000).The simulation results show that the FXLMM algorithm
behaves in an identical manner to the FXLMS algorithm until before the impulses
are encountered. The FXLMS algorithm, however, exhibits a degraded system
performance with a very high mean-squared error (MSE) in the presence of impulses.
The FXLMM algorithm is found to be more robust while handling impulses.
Comparing the MSE plots of the two algorithms shows that the FXLMM algorithm
has superior performance in the presence of impulses and is more effective in
suppressing the adverse influence of impulse noise.


## 6.3. Real-Time Experiment Results

A real-time
experiment is set up as shown in Figure5 with the real GE Healthcare
incubator, we use a 200 Hz sinusoidal signal generated by a
loudspeaker as the primary noise (60 dB higher than the background), two
antinoise loudspeakers are fixed in the incubator, two error microphones are
placed near baby's ears to pick up the noise residue, the primary microphone is
set on the top of the incubator in order to collect the primary noise signal. A
TI TMS320C30 DSP is used for the ANC system. The assembly language is used for
software developing in order to achieve the real-time processing requirement
[22]. For the
real-time experiment setup, the sampling frequency is 1.938 KHz, two 220-tap filters with the convergence factor of 0.0003 were used for the adaptive noise cancellation
filters W1(z) and W2(z).The real-time noise cancellation results based on the
real time experiment are shown in Figures14 and 15. By comparing the
spectra of the noise before and after cancellation, we can find that the
residual noises are 30.231 dB lower than the original noise at the left
error microphone and 23.685 dB lower at the right error microphone. After
the ANC system, the high power noise is dramatically reduced into an acceptable
range and not harmful any more.Figure 14
Real-time noise cancellation at left error microphone in
the incubator.Figure 15
Real
time noise cancellation at right error microphone in the incubator.


## 7. Conclusion

In this paper,
a novel neonatal healthcare application, the intrauterine acoustics embedded
active noise controller, has been presented. The integration algorithm created
a beneficial environment for the infant and allowed the residual noise from the
ANC system to be masked. The ANC system involved an adaptive method of noise
cancellation using the statistically robust FXLMM algorithm. It allowed for
stable operation of the ANC system in the presence of impulsive interference in
the input. Real transfer functions measured from a laboratory setup were used
to develop a computer model for simulation of the ANC system. The integration
algorithm was proven to be highly advantageous as it allows the secondary path
to be modeled online making the system more sensitive to changes in the
environment. The real time controller was found to be cost effective and
displayed stable performance in the real incubator.



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*Source: 495317-2008-04-06.xml*