1 Signal Processing Methods for Doppler Radar Heart Rate ...madsen/papers/Host-Madsen.pdf ·...

1

Signal Processing Methods for Doppler Radar

Heart Rate Monitoring

Anders Hst-Madsen1,2, Nicolas Petrochilos2, Olga Boric-Lubecke1,2, VictorM. Lubecke1,2, Byung-Kwon Park2, and Qin Zhou3

Kai Sensors, Inc., HI, USA1, University of Hawaii at Manoa, HI, USA2 andBroadcom, Inc., USA3

A practical means for unobtrusive and ubiquitous detection and monitoringof heart and respiration activity from a distance could be a powerful tool forhealth care, emergency, and surveillance applications, yet remains a largelyunrealized goal. Without the need for contact or subject preparation (specialclothing, attachments, etc.), this could better extend health monitoring to thechronically ill in routine life, allow wellness monitoring for a large populationwithout known predisposition for risk or harm, and provide alarm and datain emergencies. Such technology could also be used to detect lost or hiddensubjects, to help assess emotional state, and to compliment more cumbersomemeasurements as pre-screening. Doppler radar remote sensing of vital signshas shown promise to this end, with proof of concept demonstrated for variousapplications. Unfortunately, this principle has not been developed to thelevel of practical application, mainly due to a lack of an effective way toisolate desired target motion from interference. However, by leveraging recentadvances in signal processing and wireless communications technologies, thistechnique has the potential to transcend mere novelty and make a profoundimpact on health and welfare in society.

1.1 Introduction

Practical non-contact detection and monitoring of human could be a powerfultool for health care, emergency, military, and security applications. remotesensing of heart and respiration activity has shown promise toward this end,with proof of concept demonstrated for various applications [3, 12, 14]. Byestimating the associated in a radio signal reflected by the body, cardiopul-monary related movement can be discerned without physical contact. Throughits non-invasive nature, this approach is well suited to applications where it is

This material is based upon work supported by the US National ScienceFoundation under Grant No. ECS0428975

2 Hst-Madsen Petrochilos, Boric-Lubecke, Lubecke, Park and Zhou

important to minimize disruption of the subjects activity, particularly whereprolonged monitoring is needed. A robust Doppler radar system would bewell suited to collection of long-term heart beat interval data for () diagnosisand prognosis [8]. Additional benefits of microwave Doppler radar includethe versatile ability to function at a distance through clothing, walls, ordebris. This allows the system to be applied both in medical health carescenarios, where some degree of subject cooperation can be assumed, andin emergency response or security applications, where subjects do not orcannot cooperate. Alternative techniques for long-term medical monitoringtypically require direct contact (ECG and Holter monitors, piezoelectricsensors), while minimally invasive techniques tend to require very accuratecontrol or placement (laser Doppler vibrometer), which might not always bepossible or desirable. The use of infrared (IR) body heat sensors in search andrescue operations is limited due to poor IR propagation properties throughwalls, rubble and weather. While Doppler radar offers distinct advantages,this approach has not been developed to the level of practical application,mainly because there has been no genuinely effective way to isolate desiredtarget motion from other motion and other targets. Fortunately, throughthe application of recent advances in wireless communications and signalprocessing technologies, Doppler cardiopulmonary radar now has the potentialto transcend mere novelty and make a significant impact on health care andnational security.

The use of Doppler radar was demonstrated for detection of respiratoryrate in 1975 [10], and heart rate in 1979 [11], using commercially availablewaveguide X-band Doppler transceivers. Our recent work to implement thisconcept by leveraging telecommunications technology, includes the detectionof heart and respiration signals with existing wireless terminals [14, 15],implementation of dedicated low-cost microwave Doppler radars [57], anddevelopment of related software for automated rate detection [13]. Dopplersensing with communications signals in the 800 2400 MHz range has beendemonstrated with very promising results for both detection of surface andinternal heart and respiration motion [14]. Higher frequency signals like thoseused for motion-controlled doors and traffic lights, in the 10 GHz range,also work well for detection of cardiopulmonary motion at the chest surface,even through clothing [1]. While reliable heart and respiration rate extractioncan be performed for relatively still and isolated subjects [6], it is a majorchallenge to obtain useful data in the presence of random motion of thehuman target, radar, peripheral human subjects and other moving objects.Many contact (such as ECG, EEG) and non-contact medical measurements(such as fMRI) also suffer from motion artifacts due to random motion ofthe subject during the measurements. Various DSP techniques are used toextract useful data from such measurements [19]. The problem of backgroundnoise has been a barrier to bringing Doppler vital signs sensing into practical,everyday applications. We propose to explore promising new solutions to thisproblem, taking advantage of recent developments in wireless communications.

1 Signal Processing Methods for Doppler Radar Heart Rate Monitoring 3

These have the potential to not only improve the robustness of Dopplerradar sensing to practical levels, but to also make possible the gatheringof additional information, such as determining the number of subjects ina particular environment. The application of techniques to provide robustDoppler radar heart signal detection, will be discussed, as well as detectionand count of multiple subjects.

Multiple antennas can be used to detect multiple copies of the same signalwith different phase information, with coherent combining used to provide agreatly improved estimate of desired Doppler motion. Figure 1 shows the blockdiagram of experimental set-up with one transmit and two receive antennas.Each receive antenna is connected to a quadrature receiver chain to providetwo orthonormal baseband signals (I and Q). DC components are eliminatedvia a dc canceller. Demodulated output results are compared with a wiredfinger pulse sensor reference. When more than one target is in view, multipletransmitters and receivers providing multiple signal copies could be used todistinguish between the different sources of Doppler motion, and isolate thedesired signal. We will first discuss demodulation methods for recovery ofphase information in Doppler radar system, followed by signal processingmethods for heart signal detection and estimation, and finally separation ofmultiple heartbeat signals.

Fig. 1.1. Block diagram of a Doppler radar system with one transmit and tworeceive antennas. Each receive antenna is connected to a quadrature receiver chain toprovide two orthonormal baseband signals (I and Q). DC components are eliminatedvia a dc canceller. Demodulated output results are compared with a wired fingerpulse sensor reference.


1.2 Signal Model

In this section we will discuss modeling of the signal. We assume a continuouswave (CW) radar system transmitting a single tone signal at frequency .The transmitted signal is

s(t) = cos (t+ (t)) (1.1)

Where (t) is phase noise in the oscillator.This signal is reflected from a target at a nominal distance d, with a time-

varying displacement given by x(t). Suppose at first that the signal from thesubject arrives from a single path. The received signal at the k-th antenna isthen

rk(t) = Ak cos

(

t4

(d+ x(t) k) +

(

t2d

c k

)

+ 0

)

+ wk(t)

= dA sin (1.2)

Here dA is the inter-antenna spacing, is the angle of arrival, wk(t) is noiseat the antenna, and Ak is the received amplitude at the k-th antenna, whichdepends on the antenna pattern e.g., if the antennas are omnidirectional,Ak is independent of k and 0 some initial phase offset. To arrive at this,we have used some key properties of x(t). First, x(t) is slowly varying, sothat x(t d/c k) x(t). Secondly, x(t) is small compared to d so that(t(2dx(t))/ck) (t2d/ck). At the receiver, rk(t) is multiplied bycos(t+(t)) and the phase shifted signal sin(t+(t)) then lowpass filteredresulting in the complex signal

rk(t) = Ak exp

(

j

(

4

(x(t) k) +

(

t2d

ck

)

(t) +

))

+ wk(t)

= Ck sk() exp (jk(t)) exp

(

j4

x(t)

)

+ wk(t) (1.3)

Ck = Akej

sk() = ej 4

k

k(t) =

(

t2d

c k

)

(t)

=4

d+ 0

If x(t) is small compared to and k(t) is small, we can use a first order(i.e., linear) approximation for the complex exponential, so that

rk(t) Cksk() + Cksk()j4

x(t) + Cksk()jk(t) + wk(t) (1.4)

So far we have assumed that the RF signal is only reflected by a singleobject, the subject of interest, and that only the vital sign signal is returned.


However, there are multiple other reflections that must be taken into account.The signal is reflected by various stationary objects (e.g., walls) (and poten-tially moving objects, e.g., rustling leaves, although in many applications thisis less likely). These reflections results in a dc-offset, as well as some additionalnoise. Thus, in most cases the dc of the signal contains no information andcan be removed. This turns out to be of practical value as well. The signaldue to the heartbeat is very small, and if the dc is not removed this givesdynamic range problems with the quantizer. Additionally, the signal reflectedoff the body of the subject is also reflected by stationary objects, resulting inmultipath. In that case the received signal is (after dc-removal)

rk(t) = exp

(

j4

x(t)

) M

i=1

Ck,isk(i) exp (jk,i(t)) + wk(t) (1.5)

where M is the number of multipath components. Each signal has its ownsignal strength Ck,i, angle of arrival (delay) i = dA sini, and phase noisecontribution k,i. x(t). The signal x(t), of course, also experiences a differentdelay through the different paths, but this delay is insignificant relative to thebandwidth of x(t). The non-linear model for multipath is rather complex,but if the linear model is valid, we get a simpler system model (again, afterdc-removal)

rk(t) x(t)

M

i=1

Ck,isk(i)j4

+ (w)k(t)

= vkx(t) + wk(t) (1.6)

where vk is a complex constant that summarizes all the factors affecting thesignal at the n-th antenna.

1.2.1 Physiological Signal Model

The signal displacement generated by the subject consists of respiration andheartbeat. The respiration is usually in the range 0.10.8Hz and the heartbeatin the range 0.82Hz. While the respiration is a stronger signal than theheartbeat, it is also more difficult to characterize and therefore to detect. Inthe current method we therefore remove most of the respiration by high passfiltering. The heartbeat signal itself is a rather complicated signal. It is nearlyperiodic, but the period can vary from one beat to the next; this is called heartrate variability (HRV). HRV can be modeled as a random process [16] withstrong periodicity. We consider the filtered received signal: bandpass filteredwith a pass band of 0.82Hz so that only the fundamental frequency of theheartbeat is received. The resulting signal is modeled as

s(t) = (A+ (t)) cos (t+ (t) + 0) (1.7)


The amplitude variations (t) and (t) are zero mean random processesmodeling the HRV. Notice that this is the same kind of model used in [22]but that the frequency is unknown here. Dealing directly with the model(1.7) for signal processing is complicated, since it is a non-linear functionof a random process (t) that is not completely characterized. We thereforemust use some approximation for (t). As (t) is rapidly varying (the timebetween hearbeats can vary 10% from one heartbeat to the next), a linearapproximation is not appropriate. Instead we will use a piecewise constantapproximation of (t) (and (t)) so that

s(t) = Ai cos(t+ i), t [(i 1)T0, iT0] (1.8)

where T0 is some suitably chosen interval that can be optimized for perfor-mance, and Ai and i are modeled as deterministic, unknown constants.

1.3 Single Person Signal Processing

1.3.1 Demodulation

Each antenna has two output signals, for a total of 2K signals. In a singleperson system, the first step is to combine these 2K signal into a single signalthat best possible represents the heartbeat, a step we will call demodulation.The purpose is both graphical display of the signal (i.e., similar to the usualECG signal), and to use in further signal processing. However, it should benoticed that the demodulated signal is not necessarily a sufficient statistic, sobetter performance can be obtained from using the 2K direct signals; this isin essence using the full MIMO advantage. However, using a single signal canconsiderably simplify signal processing.

There are two methods for demodulation. The first is linear demodulation.A linear demodulator is of the form

x[n] =

K

k=1

ak(rk[n]) + bk(rk[n]) = dT r[n] (1.9)

where rk[n] is the sampled version of rk(t), d = [a1 b1 . . . aK bK ]T , and r[n] =

[(r1[n]) (r1[n]) . . . (rK [n]) (rK [n]) ]T . As criterion for performance we

will use mean square error (MSE)

mind

E[

(x[n] x[n])2]

(1.10)

However, it can also be proven that the minimum MSE (MMSE) solutionmaximizes the signal-to-noise ration (SNR) at the output. In [23] we haveproven that the optimum demodulator is projection onto the eigenvectorcorresponding to the maximum eigenvalue of the covariance matrix. This


result is not surprising if the linear model (1.6) is assumed, but [23] shows itis also true for the non-linear model (without multipath).

The second method for demodulation is non-linear demodulation. This ismainly relevant for a single antenna, although non-linear modulation outputsfrom multiple antennas could be combined. So, assume a single antennaand let r[n] = r1[n] Clearly, from the model (1.5) the optimum non-lineardemodulator is given by

x[n] = Arg(r[n] c)

4(1.11)

Here, c is unknown dc offset of the signal. So, to implement this estimator cneeds to be known, or estimated.

It is easy to see that, given c, the ML estimator of the remaining parametersis

x(c)[n] = Arg(r[n] c)

4(1.12)

A(c) =1

N

N1

n=0

{

(r[n] c) exp(j4

x(c)[n]

}

(1.13)

The estimation problem for c can now be stated as

c = arg mincR

d(c) (1.14)

d(c) =

N1

n=0

r[n] A(c) exp(jx(c)[n]) c

2

(1.15)

Unfortunately, a closed form expression for c does not exist, and the MLE isdifficult to find numerically. In [17] we have developed a heuristic estimatorfor k, which is almost as good as the MLE for reasonable MSE.

The data in [17] shows that linear and non-linear demodulation is almostequivalent at low frequencies, such as the 2.4GHz we use for experiments, butat higher frequencies, non-linear demodulation is better.

1.3.2 Detection of Heartbeat and Estimation of Heart Rate

In this section we consider how to estimate the heart rate from the de-modulated wireless signal and how to detect if a heartbeat is present. Thesignal after demodulation is still a very noisy signal. It does not have thewell-defined peaks known from ECG signals, and methods from ECG signalprocessing therefore cannot be directly applied. We will first derive themaximum likelihood estimator (MLE) based on the model (1.8) for the heartrate, and corresponding to this the (GLRT).


MLE estimator based on demodulated data

Consider estimation of average heart rate from the demodulated data. In thatcase the data is real, and we have a model

x[n] = s[n] + w[n] (1.16)

where w[n] is identically distributed zero mean real Gaussian noise withunknown variance 2. We assume the model (1.8), where T0 = NT . Weconsider a window of data of length MN and divide this into M subwindowsof length N . We can then write the received signal sm[n] during the m-thsubwindow as

sm[n] = Am cos(n+ m) = am cos(n) + bm sin(n) (1.17)

where the constant Am includes all scaling due to body reflections, wirelesspropagation, demodulation, and heartbeat amplitude variations. For simplic-ity we use [0, ] for the discrete-time frequency. We can write the receivedsignal during the m-th subwindow as

xm[n] = sm[n] + w[n+mN ] (1.18)

The joint density function for the observation vector is, under the assumptionof white Gaussian noise

f(x) =(

22)

MN

2 exp

(

1

22

M1

m=0

N1

n=0

(xm[n] sm[n])2

)

(1.19)

where = [, 0, . . . M1, 0, . . . M1, 2] denotes the vector of unknown

parameters. Define the square error 2 and 2m

2m =

N1

n=0

(xm[n] sm[n])2

(1.20)

2 =

M1

m=0

2m (1.21)

To maximize the log-likelihood function is equivalent to minimize the squareerror 2, the summation of 2m over m. The maximization results in [23]

=arg max

1

M

M1

m=0

|Xm()|2

(1.22)

am =1

N{Xm()} (1.23)

bm = 1

N{Xm()} (1.24)

2 =1

MN

(

M1

m=0

N1

n=0

x2m[n] N

2

M1

m=0

|Xm()|2

)

(1.25)


We notice that the ML estimator of the frequency is obtained from thecombination of the DTFTs in each interval. Each of these can be calculatedusing FFTs, so the complexity of the algorithm is low.

For detection of heartbeat, we consider H1 the hypothesis that a heartbeatis present, and H0 that no heartbeat is present. Since heartbeat frequencyand other parameter are unknown, this is a composite hypothesis test. Ageneral method and in many cases optimum solution for this problem is thegeneralized likelihood ratio test (GLRT) detector [18]. The principle is toestimate the unknown parameters under each of the hypothesis using theMLE, and then calculating the resulting likelihood ratio. Above we haveestimated the parameters under the H1 hypothesis. Under the H0 hypothesis,we only need to estimate the noise variance:

2 =1

MN

M1

m=0

N1

n=0

x2m[n] (1.26)

f(x;H0) =(

220

)MN

2

exp

{

MN

2

}

(1.27)

Now we can represent the generalized likelihood ratio for hypothesis H1 andH0 as

LG(x) =f(x;H1)

f(x;H0)=

(

21

20

)MN

2

(1.28)

MLE estimator based on direct data

As mentioned the demodulated data does not constitute a sufficient statistic.We therefore consider ML estimation directly from the received data. Thus,we utilize more directly the MIMO advantage. As for the demodulated case,we divide the data into M windows of length N samples. The received signalat the k-th antenna during the m-th window can then be written as

zk,m[n] = sk,m[n] + wk,m[n] = xk,m[n] + jyk,m[n] (1.29)

where (xk,m[n], yk,m[n]) is the received I and Q data, and wk,m[n] is a sequenceof independent, identically distributed zero mean circular complex Gaussiannoise with unknown variance 2. Let Ak,m and Bk,m denote the magnitudesfor the I and Q channel data for the k-th antenna. We can write these as:

Ak,m =Ck,m cos(k) (1.30)

Bk,m =Ck,m cos(k) (1.31)

In the multipath model it is most reasonable to assume that the k areindependent between antennas. We can now write the received signal as


xk,m[n] = Ak,m cos(tm,n + m) + {wk,m[n]} (1.32)

= cos(k) (Ck,m cos(m) cos(tm,n) (1.33)

Ck,m sin(m) sin(tm,n)) + {wk,m[n]}

yk,m[n] = Ak,m cos(tm,n + m) + {wk,m[n]} (1.34)

= sin(k) (Ck,m cos(m) cos(tm,n) (1.35)

Ck,m sin(m) sin(tm,n)) + {wk,m[n]} (1.36)

It can be noticed here that k only depends on k while m only dependson m. This is the most accurate model, but it seems impossible to get explicitexpressions for the MLE estimator. We therefore consider two cases. First, weassume that k also depends on m. It can then be proven [23] that we get thefollowing explicit solution

= arg max

M

m=1

(

1

N

K

k=1

|Xk,m()|2

+ |Yk,m()|2

+1

N

g2m + 4f2m

)

(1.37)

2 =

M

m=1

(

K

k=1

N1

n=0

x2k,m[n] +

K

k=1

N1

n=0

y2k,m[n]

1

N

K

k=1

|Xk,m()|2

+ |Yk,m()|2

1

N

g2m + 4f2m

)

(1.38)

where

fm =

K

k=1

{Xk,m()}{Xk,m()} + {Yk,m()}{Yk,m()} (1.39)

gm =

K

k=1

{Xk,m()}2

+ {Xk,m()}2{Yk,m()}

2{Yk,m()}

2

(1.40)

The estimate of 2 can then be used in (1.28) to get the GLRT for thiscase.

Second, we assume that m also depends on k. It can then be proven [23]that we get the following explicit solution


= arg max

K

k=1

(

Xk()2

+ Yk()2

+

(

Xk()2 Yk()

2

)2

+ 4{Xk()Yk()}2

)

2 =

K

k=1

k

2k =

M1

m=0

N1

n=0

x2k,m[n] +

M1

m=0

N1

n=0

y2k,m[n]

1

NXk()

2

1

NYk()

2

1

N

(

Xk()2 Yk()

2

)2

+ 4{Xk()Yk()}2

(1.41)

where

Xk()2

=M1

m=0

|Xk,m()|2

Yk()2

=M1

m=0

|Yk,m()|2

Xk()Yk() =

M1

m=0

Xk,mYk,m() (1.42)

Again, the estimate of 2 can then be used in (1.28) to get the GLRT forthis case.

What should be noticed is that although the solutions look complex, theyare expressed as simple combinations of discrete time Fourier transforms, andtherefore they can be calculated extremely fast using FFTs.

Figures 1.2 and 1.3 show the detection performance of the detectors in theform of ROCs (receiver operating characteristic) based on measured data for anumber of subjects. Figure 1.2 compares the detector based on demodulateddata (equation (1.25) and (1.28)) with one based on direct data (equation(1.41) and (1.28)); the figure confirms that using direct data is more efficient.Figure 1.3 compares the performance of the direct data detector for varioussizes of windows and subwindows.

1.4 Multiple People Signal Processing

Consider the signal model (1.6). If there are d multiple sources, they add uplinearly, resulting in


0 0.1 0.2 0.3

0.8

0.9

1Ant2, subwin= 8s,order=4

PFA

PD

0 0.1 0.2 0.3

0.8

0.9


PFA

PD

0 0.1 0.2 0.30.75

0.8

0.85

0.9

0.95


PFA

PD

0 0.1 0.2 0.30.75

0.8

0.85

0.9

0.95


PFA

PD

cmpsvd

cmpsvd

cmpsvd

cmpsvd

Fig. 1.2. ROC curves for the GLRT either based on demodulated data (SVD) ordirect data (CMP).

0 0.05 0.1 0.15 0.2 0.25 0.30.75

0.8

0.85

0.9

0.95

1

PFA

PD

Antenna 2, Complex model with different parameters

subwin= 8s,order=4subwin= 4s,order=8subwin= 4s,order=6subwin= 3s,order=8

subwin= 4s,order= 6

Fig. 1.3. Performance of the GLRT for direct data with different parameters.


rk(t) =d

j=1

vk,jxj(t) + wk(t) (1.43)

In the sampled version, this can be written as the classical instantaneous linearmodel:

r[n] = M x[n] + w[n]

As the data is not known, nor the mixing matrix, (BSS) methods are thenatural choice. Note also that 1) the noise power is of the same magnitudeor higher than the signal power, 2) the received IQ signal is complex, but thesources s[n] are real, so we apply real BSS, and we work on:

y[n] =

[

(r[n])(r[n])

]

=

[

(M)(M)

]

x[n] +

[

(w[n])(w[n])

]

y[n] = Mx[n] + w[n] , (1.44)

where the size of the received signal is multiplied by two, thus allowing twiceas many independent beamformers.

The aim of multiple people signal processing is now to separate the multiplesources. Once they are separated, the single person signal processing from theprevious section can be utilized.

1.4.1 Heart beat signal

As model for the heartbeat we consider (1.7). Because of the phase variations(t) the signal is not strongly periodic. However, the amplitude A + (t) isnearly constant, which means that x(t) has an analytical representation whichis almost a signal. The analytical signal is obtained by adding the signal andthe quadrature of its . [2]

1 0.5 0 0.5 1 1.51.5

1

0.5

0

0.5

1

(a) Full0.6 0.4 0.2 0 0.2 0.4 0.6

0.6

0.4

0.2

0

0.2

0.4

0.6

(b) Filtered

Fig. 1.4. Analytical representation of a finger pulse sensor signal after bandpassfiltering in the range 0.03-30 Hz. a) Full version, b) Filtered version.

Figure 1.4 shows the analytical signal of an ECG signal from, and itslowpass filtered version. The almost circular trajectory indicates that indeed


the heartbeat signals are nearly constant modulus, and that after lowpassfiltering, this property shows up even stronger.

In real world applications, there might be many sources, but we are onlyinterested in those that are heartbeat and/or respiration. We therefore needto use specific characteristics of these signals. One possibility is to use thatthe signal is quasi-periodic, as we used in the single person detector in theprevious section. However, the signal is not very periodic, and we thereforebelieve that the constant modulus property is a stronger indicator. For thisreason we focus on BSS methods that use the constant modulus property.

1.4.2 Algorithm

Our algorithm is based on three steps:

) Use a band-pass filter over the range [0.75;2] Hz.) Generate the analytic version of our signal by taking it and then adding

its Hilbert Transform, H{r(t)}, in quadrature:ra(t) = r(t) + H{r(t)},

) Use the ACMA algorithm on the data [21]. ACMA is a BSS algorithm thatsearch for the beamformers wi, such that the estimated sources xi = w

Hr

are constant-modulus: |xi| 1.

Step remove the heart-beat harmonics, the respiration and the lowfrequency interferences.

Doing these pre-processing steps insures that each source produces a rank-one only matrix and is constant-modulus, so that the assumptions neededby ACMA are respected.

The choice of the separation algorithm follows the next reasoning: RACMA[20] is then not applicable in our case, since at the end of step , the sourcesare complex. [21] is the logical choice as it needs less samples than any HOS-based method, as ACI [4] or EFICA [9], is computationally faster, and ourdata is constant-modulus.

1.4.3 Results

As we do not have enough measurements with two subjects to draw statisti-cally sound performance evaluations of our algorithm, we have to recourse tosemi-synthesized and full simulations in this section.

We compare our method, pp-ACMA, to either RACMA, ICA, or EFICA.The later algorithms do not need the pre-processing to function, but we alsoevaluate EFICA with the pre-processed data in order to have a comparisonthat is fair to ACMA.

Simulations

In order to have better insight on the algorithm behavior, we conduct nowthe evaluation over fully-simulated data, i.e. the heartbeat source signal arenow generated by the method proposed in [16] with different average rate (so


different subjects). Note that we can tune the Signal Noise Ratio (SNR), theincoming average heart-rate and time integration, and therefore fully explorethe limits of the algorithms. Once the separation algorithm has deliveredoutput signals, they are used to estimate the heart rate. The separation isdeclared a success if both measured hearbeats are within 0.08 Hz of theirtrue values, and the output SINR is above 3 dB, otherwise it is a failure. Wealso compare to EFICA, which is either applied to the direct data or to thepre-processed data.

ACMA

0.1

0.10.5

0.5

0.90.90

.99 0.9

9

f 2 (

Hz)

f1 (Hz)

0.6 0.8 1 1.2 1.4 1.6 1.8

0.6

0.8

1

1.2

1.4

1.6

1.8

ACI

0.70.7

0.7

0.7

0.7

0.7

0.7

0.7

0.7 0.9

0.9

0.9

0.9

0.9

0.9

f 2 (

Hz)

f1 (Hz)

0.6 0.8 1 1.2 1.4 1.6 1.8

0.6

0.8

1

1.2

1.4

1.6

1.8

EFICA, R

0.7

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0.7

0.7

0.70.7

0.7

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0.9

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f 2 (

Hz)

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0.6 0.8 1 1.2 1.4 1.6 1.8

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EFICA, C

0.010.

010.

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0.6 0.8 1 1.2 1.4 1.6 1.8

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0.8

1

1.2

1.4

1.6

1.8

Fig. 1.5. Success rate of the algorithms as a function of average rate of theheartbeat.

First, we consider two sources with a SNR of 5 dB, and an integrationtimes of 6 seconds (so 120 samples). Figure 1.5 present the Success rate of thePP-ACMA, ICA, EFICA directly on data (denoted EFICA,R), and EFICAon the pre-processed data (denoted EFICA,C). We note that even two sourceswith the same average heart-rate, due to the difference in HRV, the separationis still possible up to 50% of the cases with ACMA. In the other cases, ICAand EFICA (R) behaves also quite well, but not with a perfect 100% as theACMA. Notice also that the pre-processed method cannot work outside thefrequency range of the filters, but the frequencies limits have been chosenaccording to reasonable heartbeat rate. Finally, we note that EFICA-C is notreliable in any case, this is due to the loss of diversity in the signal, whichimpairs the high-order statistics.


3020

100

10

05

100

50

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SNR (dB)

ACMA

Time (s) 3020

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05

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50

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SNR (dB)

ACI

Time (s)

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SNR (dB)

EFICA, R

Time (s) 3020

100

10

05

100

50

100

SNR (dB)

EFICA, C

Time (s)

Fig. 1.6. Success rate of the algorithms as a function of SNR and the integrationtime.

Next, we keep the frequencies fixed to 0.9 and 1.2 Hz, and we vary theSNR and the time of observation. We note on Figure 1.6 that thanks to thepre-processing gain, acceptable success rate can be obtained till 15 dB SNRfor PP-ACMA, and that 4 s is a minimum integration time (approximately 4cycles). While ACI and EFICA-R needs 5 dB more, but 3 seconds is alreadyenough for them. This counter-intuitive result can be explained by the factthat we dont strictly compare the algorithm over the same signals: indeed,for the PP-ACMA, the signal has been amputated from most of its harmonicsand its spectral diversity, that ICA and EFICA-R are still using. Next to it,in a real scenario, where the pre-processing is mandatory, note that EFICA-Cunder-performs severely.

From now, we just concentrate on our scheme for our specific applications.We keep the first average frequency at 1.1 Hz, and we vary the other averagefrequency as well the time of integration. From figure 1.7, we confirm thatgiven enough times of integration it is possible to separate two sources withequal average rate, as for instance for T = 9 s, the success rate is above 60%.

Experimental Results

Finally we will present some results from real measurements. The data isfrom a single experiment with two persons and two antennas. The ACMAalgorithms was applied. Figure 1.8 shows the radial distribution after usingACMA. As can be seen, the CM property is well satisfied. Figures 1.9and 1.10 show the two separated signals in the time domain respectivelyfrequency domain. It can be seen that the two sources agree with the reference


0.1

0.1

0.1

0.2

0.2

0.2

0.2

0.5

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0.9

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1

11

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1

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f2 (Hz)

f 2 (

Hz)

1.6 1.8 2 2.2 2.4 2.6 2.81

2

3

4

5

6

7

8

9

Fig. 1.7. Success rate of the pp-ACMA as a function of average rate of the secondheart beat and the integration time.

measurements, although there is some leakage from the stronger source to theweaker source.

2 0 22

1

0

1

2Output 1

0 1 20

0.03

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0.1

0.13Radial distribution

2 0 22

1

0

1

2Output 2

0 0.5 1 0

0.03

0.06

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0.13Radial distribution

Fig. 1.8. Radial distribution of the two channels after separation with ACMA.


0 5 10 152

1

0

1

2

Time (seconds)

Sou

rce

1

10 12 14 16

1

0

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0 5 10 152

1

0

1

2

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Sou

rce

210 12 14 16

1

0

1

Time (seconds)S

ourc

e 2

Fig. 1.9. The two separated sources in the time domain. The solid curve shows thereference measurements

1.5 Conclusion

In this chapter we have outlined methods for signal processing of Dopplersignals. The main problems we have focused on is detection of a signal andseparation of multiple signals. We have outlined a number of methods fordetection and separation and shown the efficacy on real signals. The key tech-nology we considered was using multiple antenna transmitters and receivers.We first showed how this could be used for enhanced signal processing whena single person is present. We then showed how this could be used to monitormultiple persons.

References

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2. Bracewell, R.: The Fourier Transform and Its Applications, 3rd edn. McGraw-Hill, New York (1999)

3. Chen, K.M., Mirsa, D., Wang, H., Chuang, H.R., Postow, E.: An X-bandmicrowave life detection system. IEEE Transactions on Biomedical Engineering33, 69770 (1986)


0 0.5 1 1.5 2 2.5 3 3.5 410

0

101

102

103

104

Frequency (Hz)

Ave

rage

d sp

ectr

um

Output 1Output 2Reference 1Reference 2

Fig. 1.10. The two separated sources in the frequency domain.

4. Comon, P.: Independent component analysis, a new concept? Signal Processing,Special issue on Higher-Order Statistics 36(3), 287314 (1994)

5. Droitcour, A., Boric-Lubecke, O., Lubecke, V.M., Lin, J.: 0.25 m CMOS andbiCMOS single chip direct conversion Doppler radars for remote sensing of vitalsigns. In: IEEE ISSCC Digest of Technical Papers, pp. 348349 (2002)

6. Droitcour, A.D., Boric-Lubecke, O., Lubecke, V.M., Lin, J., Kovacs, G.T.A.:Range correlation effect on ISM band I/Q CMOS radar for non-contact sensingof vital signs. In: IEEE MTT-S IMS2003 Digest, vol. 3, pp. 19451948 (2003)

7. Droitcour, A.D., Lubecke, V.M., Lin, J., Boric-Lubecke, O.: A microwave radiofor Doppler radar sensing of vital signs. In: IEEE MTT-S IMS2001 Digest,vol. 1, pp. 175178 (2001)

8. Hilton, M.F., Bates, R.A., Godfrey, K.R., et al.: Evaluation of frequency andtime-frequency spectral analysis of heart rate variability as a diagnostic markerof the sleep apnea syndrome. Med. Biol. Eng. Comput. 37(6), 760769 (1999)

9. Koldovsk, Z., Tichavsk, P., Oja, E.: Dfficient variant of algorithm fastica forindependent component analysis attaining the Cramer-Rao lower bound. IEEETrans. on Neural Networks 17(5), 12651277 (2006)

10. Lin, J.C.: Non-invasive microwave measurement of respiration. Proc. IEEE 63,1530 (1975)

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12. Lin, J.C.: Microwave sensing of physiological movement and volume change: areview. Bioelectromagnetics 13, 557565 (1992)

13. Lohman, B.B., Boric-Lubecke, O., Lubecke, V.M., Ong, P.W., Sondhi, M.M.: Adigital signal processor for Doppler radar sensing of vital signs. In: 23rd Annual


International Conference of the IEEE Engineering in Medicine and BiologySociety (2001)

14. Lubecke, V., Boric-Lubecke, O., Awater, G., Ong, P.W., Gammel, P., Yan, R.H.,Lin, J.C.: Remote sensing of vital signs with telecommunications signals. In:World Congress on Medical Physics and Biomedical Engineering (2000)

15. Lubecke, V., Boric-Lubecke, O., Beck, E.: A compact low-cost add-on module forDoppler radar sensing of vital signs using a wireless communications terminal.In: IEEE MTT-S International Microwave Symposium (2002)

16. McSharry, P.E., Clifford, G.D., Tarassenko, L., Smith, L.: A dynamical modelfor generating synthetic electrocardiogram signals. IEEE Trans. BiomedicalEngineering 50, 289294 (2003)

17. Park, B.K., Vergara, A., Boric-Lubecke, O., Lubecke, V., Hst-Madsen, A.:Center tracking quadrature demodulation for a Doppler radar motion detector.IEEE Transactions on Microwave Theory And Techniques (2007). Submitted.Available at http://www.ee.hawaii.edu/madsen/papers/index.html

18. Poor, H.V.: An Introduction to Signal Detection and Estimation. Springer-Verlag (1994)

19. Thakor, N.V., Zhu, Y.S.: Application of adaptive filtering to ECG analysis:Noise cancellation and arrhythmia detection. IEEE Trans. on Biomed. Eng. 38,785794 (1991)

20. van der Veen, A.: Analytical method for blind binary signal separation. IEEETrans. Signal Processing 45(4), 10781082 (1997)

21. van der Veen, A., Paulraj, A.: An analytical constant modulus algorithm. IEEETrans. Signal Processing 44(5), 11361155 (1996)

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23. Zhou, Q., Petrochilos, N., Hst-Madsen, A., Boric-Lubecke, O., Lubecke,V.: Detection and monitoring of heartbeat using Doppler radar. IEEETransactions on Signal Processing (2007). Submitted. Available athttp://www.ee.hawaii.edu/madsen/papers/index.html

Index

Blind source separation, 13ACMA, 14

Cardiopulmonary activity, 1

Doppler radar, 1Doppler shift, 1

Heart rate variability, 2HRV, see Heart rate variability

Maximum likelihood estimation, MLE

generalized likelihood ratio test, 7

Multiple input multiple output, MIMO,systems, 3

Signal modelling

constant modulus, 13

Hilbert transform, 13

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