MUHD: A Multi-channel Ultrasound Prototype for Remote Heartbeat

Detection

S. Franceschini

, M. Ambrosanio

and F. Baselice

Department of Engineering, University of Naples Parthenope, Naples, Italy

Keywords:

Ultrasound System, Heartbeat Detection, Bio-radar.

Abstract:

This paper presents a novel system based on ultrasonic waves that is capable of detecting heartbeat in a

contactless fashion. The aim of this work is to design, build and test a prototype that could be effective, simple

in its realisation and use and with a low cost of production. The idea is the exploit the displacement of the skin

related to cardiac activity, that is possible by using phase difference between a transmitted wave and the waves

resulting from the interaction with the subject skin. Nevertheless, this type of procedure is not new in the

scientiﬁc literature, but in this manuscript the authors contribution mainly consists in the implementation of a

multi-channel architecture in order to overcome the well known “null-point” issue. Furthermore, an a-priori

regularisation function is used for making the system more robust against noise and artifact. The performance

of the prototype has been tested on volunteers and the results are quite close to standard electrocardiography

used as reference.

1 INTRODUCTION

Remote sensing of human vital signs is of great in-

terest since the second half of the 20

century (Mas-

sagram et al., 2009). The ability of detecting heart-

beats and breath without any contact makes these

technologies very attractive for several applications,

such as occupancy sensing, identiﬁcation devices,

driver-health control and others. Among these param-

eters, heart rate (HR) is of great interest.

Nowadays the gold-standard in HR monitoring

is the electrocardiogram (ECG), which consists in a

measure of the electrical heart activity that rules the

heartbeat. Even though it is very reliable, it has some

drawbacks such as susceptibility to moving artifacts

and the need of constant contact with subject’s skin,

which might be annoying for long time monitoring, or

simply impossible for some classes of patients (e.g.,

patients with burns or having allergies with electrodes

or gel employed in the measure).

Several techniques have been developed for the re-

mote monitoring of physiological parameters (Kran-

jec et al., 2014; Arcelus et al., 2013; Bonde et al.,

2018). The cardiac activity can be detected basically

https://orcid.org/0000-0002-7608-6686

https://orcid.org/0000-0003-3669-8183

https://orcid.org/0000-0002-5964-8667

in two ways: directly or indirectly, i.e. measuring

other parameters related to the heart activity. During

its rhythmical activity the heart has some volumetric

and pressure changes that cause waves propagating

through the body. These mechanical waves are visible

via sub-millimetre displacements of the skin, and usu-

ally are detected by very high resolution sensors often

exploiting the Doppler effect (Droitcour et al., 2004).

In the research community, articles based on electro-

magnetic (EM) radar devices for monitoring purposes

are quite numerous (Suzuki et al., 2008; Obeid et al.,

2010; Varanini et al., 2008). Although they can reach

high-level performance, their use is limited by their

cost, high susceptibility to external interference, high

amount of EM energy required for the two-way path

of the wave and the need of semi-ﬁxed patients.

To overcome some of these issues, ultrasound

(US) systems have been proposed (Min et al., 2010).

These devices are cheaper than EM radars and free

from interference with other electronic devices. US

monitoring systems could be classiﬁed by their archi-

tecture, the most appropriated one is the short range

continuous wave (CW) US radar (Kim and Nguyen,

2004; Gu et al., 2010; Droitcour et al., 2001). Unlike

other approaches, this paper presents a multi-channel

CW-US system that measures the phase difference be-

tween a transmitted and received wave. Conversely

from the Doppler effect which allows a measure of

Franceschini, S., Ambrosanio, M. and Baselice, F.

MUHD: A Multi-channel Ultrasound Prototype for Remote Heartbeat Detection.

DOI: 10.5220/0008982700570063

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES, pages 57-63

ISBN: 978-989-758-398-8; ISSN: 2184-4305

VCO

SWSD

XOR

to ADC

SWSD



















Moving target



Figure 1: High-level sketch of a possible architecture of CW US system for the detection of moving targets. VCO: voltage-

controlled oscillator, TX: transmitting sensor, RX: receiving sensor, SWSD: square-wave shaping detector, XOR: exclusive

OR logic port, ADC: analog-to-digital converter.

skin-movement velocity, the proposed system mea-

sures its displacement. The choice of using different

channels is related to the need of overcoming the null-

point issue addressed in Section 2. Moreover the sys-

tem uses an a-priori-based detection strategy which

makes the measures more robust against noise and ar-

tifacts.

The remainder of the paper is as follows: Section

2 provides some details regarding the physical model,

while Section 3 is focused on the technical hardware

used for the prototype realisation. The description

of the idea behind the software implementations are

shown is Section 4, in the same Section a protocol ap-

plied on some volunteers and some results are shown.

Finally, some conclusions end the paper.

2 THEORETICAL MODEL

The main idea of this system is to exploit a secondary

effect of the cardiac activity for the heartbeat detec-

tion. During its rhythmical beating, the heart pro-

duces pressure variations in near tissues causing pres-

sure waves that propagate until the skin. Thus, as

a consequence of the heartbeat, tiny vibrations are

observable in regions near to the main human pres-

sure points. Considering the chest area, these vibra-

tions are typically between 0.2 and 0.5 mm with a fre-

quency between 1 and 2 Hz. It is important to notice

that the same region is also affected by the respira-

tion activity that is responsible of a 4-12 mm vibration

in a frequency range [0.1, 0.3] Hz(Konno and Mead,

1967; Massaroni et al., 2018b; Hassan et al., 2017;

Massaroni et al., 2018a). Providing useful informa-

tion from the movement of the skin is not easy both

for the superimposed presence of the breathing activ-

ity as well as for the weak vibrations to be detected.

Another issue is related to the undesired movements

of the patients that may worsen the quality of the

measures. Regarding the useful regions for collect-

ing the heartbeat information, an interesting region is

the pit of neck which seems to be a valid alternative to

the standard chest area (Silbernagl and Despopoulos,

2015).

Although the detection of sub-millimetre move-

ments is quite challenging, a good option for its de-

tection could be the use of a continuous-wave ul-

trasound (CW-US) system with a possible architec-

ture presented in Fig. 1. A sinusoidal wave is pro-

vided by a voltage-controlled oscillator (VCO) and

travels through the air reaching the region of inter-

est, and then goes back as back-scattered modulated

wave. If the target is moving, the reﬂected wave has

some phase variations. By comparing the transmitted

and received waves, and extracting the phase varia-

tion with a non-linear procedure, the system is capa-

ble of providing information regarding the heart activ-

ity. Referring to a model with the transmitter in front

of the target and a receiver aligned to the subject, it is

possible to write the transmitted wave S

(t) as:

(t) = A

·cos [2π f

t +φ

(t)] , (1)

where f

is the carrier frequency, A

is the signal

amplitude and φ

(t) is the phase noise of the VCO.

The reﬂected wave is multiplied by the transmitted

one and then low-pass ﬁltered. The base-band output

(t) can be expressed as:

(t) ≈ A ·cos



+β



1 +

√



·x (t) + ∆φ

(t)



(2)

with A amplitude of the base-band signal, β

= 2π/λ

is the carrier wavelength, d

is the mean distance

between the transmitter and the target, x(t) is the ra-

dial movement of the skin which is, in our case, the

quantity of interest, ∆φ

(t) is the residual noise, h is

the distance between transmitter and receiver, and k is

a constant quantity related to β

, d

and h.

BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices

If the target is quite near to the transmitter, the

phase noise of the reﬂected signal is almost equal to

the one of the local oscillator, so by mixing both sig-

nals the residual phase ∆θ

(t) is usually negligible.

Since the displacement is very small compared to the

wavelength, if k is an odd multiple of π/2, the base-

band output S

(t) becomes approximately linear re-

spect to the quantity x (t), which represents the opti-

mal case; conversely, if k is an even number of multi-

ples of π/2, the output signal is not linear anymore to

the cardiac information and this represents the worst

case, which is known as “null-point” and makes the

measures very inaccurate. Since the working point

depends on k, which is related to the target distance,

the null-point problem occurs every λ

/4.

For addressing this issue, several techniques have

been proposed as in (Xiao et al., 2006) and (Droitcour

et al., 2004). Our solution is designed to be cheap

and technologically easy to be implemented since it

is based on the spatial diversity of different receivers

positions. All the receivers, being placed at a different

distance from the transmitter (i.e., different h values),

they receive different signals which yields to different

phase terms.

The distance has to be chosen properly in order to

allow the system to be capable of using at least one

channel in every situation. The right distances are the

ones that guarantee to avoid the simultaneous pres-

ence of null-points in all channels. In the following

section, more details about the hardware and software

design of the prototype will be provided.

3 PROTOTYPE DEVELOPMENT

The proposed system is a coherent CW-US system

which has been tested in numerous cases, both on

moving phantoms as well as on human volunteers in

a laboratory-controlled scenario. The transmitted sig-

nal is a cosine at frequency of 40 kHz. Being the

wave transmitted in air, its velocity is approximately

equal to 343 m/s, with a wavelength of approximately

8 mm; therefore, the maximum allowed displacement

is equal to 4 mm and larger movements will result in

a non-unique solution in the non linear demodulation.

However, as shown in the Section 2, the displacement

to be detected is lower than this value.

The active part of the system is the VCO which

supplies the 40 kHz cosine used for the transmission.

The amplitude voltage is ﬁxed to the one that ensures

safety values as sound pressure levels (SPL). More

in detail, this value must be lower than 105 dB for

a subject at a 30-cm distance in accordance with the

International Commission on Non-Ionising Radiation

Figure 2: Numerical example of the null-point issue. Sup-

posed a cosine-law displacement of the target, the channels

in red and in black are affected by null points and the result-

ing signal is no more a cosine function.

Protection (ICNIRP) guideline (Jammet et al., 1984).

Both the transmitted and back-scattered waves are

then sent to a square-wave shaping detector (SWSD)

and converted into square waves with amplitude be-

tween 0 and 5 V. Finally, the signals are sent to an ex-

clusive OR logic port (XOR) which provides a signal

related to the phase difference between the transmitter

and the receiver.

In case of no differences, the output will be 0 Volt;

conversely, in case of a half-period delay the result

will be 5 Volt (the maximum value). The former case

results in a null output, which is the aforementioned

null-point. A numerical example of the possible effect

of the null point issue is shown in Fig. 2, in which

the target is moving following a cosine law and the

signal acquired by the red and black channels, which

are effected by the null-point problem, are not cosines

anymore.

A multi-channel system has been used to over-

come this problem, which allows to record signals

at different sensors locations. Therefore, if the null-

point occurs in one channel, it is very unlikely that it

will occur in all of them, having chosen the distances

among transmitter and receivers properly. In particu-

lar, since our purpose at this step is a near range de-

tection, the mutual distances between the transmitter

and the receivers are 1.6, 2.0, 3.0, 3.5 cm for the red,

green, violet and black-coloured sensors respectively.

This particular conﬁguration ensures that there is no

null-point in all the four channels simultaneously if

the target is nearer than 40 cm from the prototype. If

the working distance increases, farther receivers loca-

tions must be considered.

Regarding the ultrasonic sensors, the models

40LT16 and 40LR16 manufactured by SensComp

MUHD: A Multi-channel Ultrasound Prototype for Remote Heartbeat Detection

Figure 3: Picture of the prototype. In the top the board with

the US sensors, in the bottom the board with the electronics.

were employed for the transmitter and receivers, re-

spectively. These piezoelectric sensors have a quite

narrow bandwidth of about 2 kHz centred at 40 kHz

with a radiation pattern at -6 dB of approximately

sixty degrees both in the horizonal and vertical axes.

The SWSD electronics is mounted on one board,

while OR logic ports and the electronic components

are placed on a second board. A picture of the com-

plete prototype is shown in Fig. 3. The output signal

of the XOR port is sent to an analog-to-digital con-

verter (ADC) which, in our case is a PCI-6251 board

manufactured by National Instrument. This board has

a 16-bit resolution and a maximum sample rate of

1.25 MS/s. For our measures, the sampling frequency

was ﬁxed at 200 Hz with sixteen-bit precision. A Lab-

VIEW script was used to manage the acquisition rou-

tine. Once acquired, the signals are processed in Mat-

lab (The Mathworks, Inc.).

4 DATA PROCESSING

In order to evaluate the capability of the prototype

to detect the heartbeat, the system was ﬁrstly tested

on moving phantoms and then on volunteers. The

complete signal processing is a step-by-step proce-

dure that aims at emphasising the heartbeat compo-

nents in the acquired signals. In this section, some

results and the procedure employed for the data pro-

cessing are reported.

4.1 Acquisition Protocol

The prototype has been tested on 4 male volunteers,

with an age between 30 and 60 years. Per each sub-

ject, two acquisitions of length 60 seconds of the car-

diac signal were recorded. In all the cases the proto-

Figure 4: Band-pass Gaussian-shaped ﬁlter for cutting-off

noise and breathing from the signal.

type was placed at 20-cm distance from the pit of the

neck, considering this area as target area. All the mea-

sures were recorded in the same scenario and condi-

tions. The volunteers laid down in a supine position,

breathing normally and relaxed. During the acquisi-

tion, in order to evaluate the performance of the sys-

tem, the ECG signal was also recorded. For this pur-

pose, three electrodes were placed on left and right

arms and on the right leg following the Einthoven’s

triangle. These electrodes are linked with a commer-

cial electronic board which ﬁlters and ampliﬁes the

signal. The ECG signal is acquired with the same

DAQ used for the US sensors, guaranteeing the si-

multaneity of all the signals.

4.2 Pre-processing

After acquiring the signals, a ﬁlter to cut-off noise

and artifact effects is employed. More in detail, high-

frequency noise and low-frequency residual breathing

components are the main cause of signal degradation.

For the data pre-processing, a modiﬁed Gaussian-

shaped band-pass ﬁlter was employed whose fre-

quency response is reported in Fig. 4. The bandwidth

of this ﬁlter is [0.6, 6] Hz, which is a good compro-

mise between preserving the useful component of the

signal and reducing the amount of noise and breath-

ing effect. Fig. 5 shows a portion of a recorded ﬁl-

tered US signal (in red) overlapped on the reference

ECG signal (in blue). It is worth to note that the sig-

nal measured by the proposed US system has peaks

which have a constant delay with respect to the ECG

ones, and the reason is related to the fact that the two

signals are different, since the former is mechanical,

while the latter is electrical.

4.3 Detection Strategy

The pre-processed data is cut into pieces of equal

length and ﬁltered by means of a moving window.

The length of the window is ﬁxed and properly se-

lected in order to have a region with the presence of

only one peak with the highest probability. The peak

is assumed to correspond to the maximum value in

BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices

Figure 5: Plot of the reference ECG (blue) and ﬁltered US

(red) signals (ten-second length signals).

each window. It is worth to note that this strategy is an

adaptive one, since after the detection of each heart-

beat, the next window will be starting from the time

instant at which the current heartbeat is estimated. For

the heartbeat detection, a good value for window’s

width seems to be 1.25 s, which works quite well for

healthy patients. However, in presence of two or more

peaks closer than 0.5 second, the one with higher am-

plitude is selected. The length of the moving window,

as well as the peak selection, are easily tunable in case

of patients affected by bradycardia or tachycardia.

4.3.1 Best Channel Selection

The detection strategy proposed previously is charac-

terised by different performance according to the con-

sidered channels. Therefore, it is worth to exploit an

automatic strategy for selecting the best channel per

each measure. To this aim, a two-step procedure was

adopted. Firstly, the recorded signals whose ampli-

tudes are close to the limits of the range [0, 5] Volt

are discarded, since they are intrinsically not reliable.

After that, the signal power in the frequency band-

width [0.9,1.2] Hz is estimated (which represents the

useful bandwidth for healthy people). The higher the

power, the higher the contribution of the cardiac sig-

nal is supposed to be in the overall acquired signal.

This measure represents an indirect evaluation of the

signal to noise (SNR) ratio, so the channel that has

the highest value is selected for the heartbeat detec-

tion step.

4.3.2 A-priori Regularisation Window

Even though the automatic choice of the best chan-

nel assures that the detection step is performed on

the channel with the highest SNR, artifacts and noise

Figure 6: Clipped Gaussian-shaped window employed for

the heartbeat detection step.

Figure 7: Effect of the regularisation window. In blue the

reference signal, in red the US signal, the black stars repre-

sent the beats detected without the use of the a-priori func-

tion, while the green circles are the beats detected by em-

ploying the clipped Gaussian-shaped window.

might still be present in the measures. In order

to make the detection step more robust, a clipped

Gaussian-shaped window is employed. This regulari-

sation window emphasises parts of the signal in which

the peaks are expected to be located with higher prob-

ability. To this aim, every searching window is mul-

tiplied with this window centred at the expected po-

sition of the beat. This instant is calculated using the

distance evaluated at the previous detected beat.

Fig. 6 shows an example of application of this

window in case that the expected beat is centred at

1 second from the previous one. Fig. 7 illustrates

a comparison of the heartbeat detection step by em-

ploying a clipped Gaussian-shaped function and with-

out any prior information. It is clear from the ﬁgure

that the detection strategy without the regularisation

window is not able to properly identify the heartbeat

peak.

4.4 Results

After the heartbeat detection step, an average BPM

value was calculated per each case. A comparison be-

tween the average BPMs evaluated via standard ECG

MUHD: A Multi-channel Ultrasound Prototype for Remote Heartbeat Detection

Table 1: Mean of heartbeats [BPM] measured by the ECG and the proposed US system.

Subject 1 Subject 2 Subject 3 Subject 4

Acq.1 Acq.2 Acq.1 Acq.2 Acq.1 Acq.2 Acq.1 Acq.2

ECG 64.10 65.55 57.28 58.04 67.30 70.07 56.74 63.83

US 64.10 65.55 57.28 58.18 67.42 70.09 56.80 63.89

and the ones by means of the prototype are presented

in Table 1. The table shows that the results obtained

by the proposed system are very similar to the ones

obtained by reference ECG with a very low error

(maximum absolute error of 0.12 s).

5 CONCLUSIONS

This manuscript presents a US prototype for non-

contact heartbeat detection. The system measures

skin displacement due to the pressure waves gen-

erated by the cardiac activity and exploits a multi-

channel architecture to overcome the null-point issue

and a windowing procedure for enforcing the heart-

beat detection. The prototype was built and tested on

four volunteers. Results are encouraging, as the sys-

tem was able to provide a performance level in detect-

ing heartbeats comparable to standard electrocardio-

graphy. Further work will focus on the improvement

of system robustness to subject movements and ex-

ternal artifacts, as well as on increasing the working

distance of the system.

ACKNOWLEDGEMENTS

The work has been partially supported by the funding

program “Bando di sostegno alla ricerca individuale

per il triennio 2015-2017” of the University of Napoli

Parthenope.

REFERENCES

Arcelus, A., Sardar, M., and Mihailidis, A. (2013). Design

of a capacitive ecg sensor for unobtrusive heart rate

measurements. In 2013 IEEE International Instru-

mentation and Measurement Technology Conference

(I2MTC), pages 407–410.

Bonde, A., Pan, S., Jia, Z., Zhang, Y., Noh, H. Y., and

Zhang, P. (2018). Vvrrm: Vehicular vibration-based

heart rr-interval monitoring system. In Proceedings of

the 19th International Workshop on Mobile Comput-

ing Systems & Applications, pages 37–42. ACM.

Droitcour, A., Lubecke, V., Lin, J., and Boric-Lubecke, O.

(2001). A microwave radio for doppler radar sensing

of vital signs. In International Microwave Symposium

Digest, volume 1, pages 175–178. IEEE.

Droitcour, A. D., Boric-Lubecke, O., Lubecke, V. M., Lin,

J., and Kovacs, G. T. (2004). Range correlation

and I/Q performance beneﬁts in single-chip silicon

doppler radars for noncontact cardiopulmonary moni-

toring. IEEE Transactions on Microwave Theory and

Techniques, 52(3):838–848.

Gu, C., Li, C., Lin, J., Long, J., Huangfu, J., and

Ran, L. (2010). Instrument-based noncontact doppler

radar vital sign detection system using heterodyne

digital quadrature demodulation architecture. IEEE

Transactions on Instrumentation and Measurement,

59(6):1580–1588.

Hassan, M. A., Malik, A. S., Foﬁ, D., Saad, N. M., Ali,

Y. S., and Meriaudeau, F. (2017). Video-based heart-

beat rate measuring method using ballistocardiogra-

phy. IEEE Sensors Journal, 17(14):4544–4557.

Jammet, H. P., Bosnjakovic, B. F. M., Czerski, P., Faber,

M., Harder, D., Marshall, J., Repacholi, M. H., Sliney,

D. H., and Villforth, J. C. (1984). Interim guidelines

on limits of human exposure to airborne ultrasound.

international non-ionizing radiation committee of the

international radiation protection association. Health

Physics, 46:969–974.

Kim, S. and Nguyen, C. (2004). On the development of

a multifunction millimeter-wave sensor for displace-

ment sensing and low-velocity measurement. IEEE

Transactions on Microwave Theory and Techniques,

52(11):2503–2512.

Konno, K. and Mead, J. (1967). Measurement of the sepa-

rate volume changes of rib cage and abdomen during

breathing. Journal of applied physiology, 22(3):407–

422.

Kranjec, J., Begus, S., Drnovsek, J., and Gersak, G. (2014).

Novel methods for noncontact heart rate measure-

ment: A feasibility study. IEEE Transactions on In-

strumentation and Measurement, 63(4):838–847.

Massagram, W., Lubecke, V. M., and Boric-Lubecke, O.

(2009). Microwave non-invasive sensing of respira-

tory tidal volume. In Annual International Confer-

ence of Engineering in Medicine and Biology Society,

pages 4832–4835. IEEE.

Massaroni, C., Lopes, D. S., Lo Presti, D., Schena, E.,

and Silvestri, S. (2018a). Contactless monitoring of

breathing patterns and respiratory rate at the pit of the

neck: A single camera approach. Journal of Sensors,

2018.

Massaroni, C., Venanzi, C., Silvatti, A. P., Lo Presti, D.,

Saccomandi, P., Formica, D., Giurazza, F., Caponero,

M. A., and Schena, E. (2018b). Smart textile for

respiratory monitoring and thoraco-abdominal mo-

BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices

tion pattern evaluation. Journal of biophotonics,

11(5):e201700263.

Min, S. D., Kim, J. K., Shin, H. S., Yun, Y. H., Lee, C. K.,

and Lee, M. (2010). Noncontact respiration rate mea-

surement system using an ultrasonic proximity sensor.

IEEE Sensors Journal, 10(11):1732–1739.

Obeid, D., Sadek, S., Zaharia, G., and El Zein, G. (2010).

Multitunable microwave system for touchless heart-

beat detection and heart rate variability extraction. Mi-

crowave and optical technology letters, 52(1):192–

198.

Silbernagl, S. and Despopoulos, A. (2015). Color Atlas of

Physiology. Thieme.

Suzuki, S., Matsui, T., Imuta, H., Uenoyama, M., Yura, H.,

Ishihara, M., and Kawakami, M. (2008). A novel auto-

nomic activation measurement method for stress mon-

itoring: non-contact measurement of heart rate vari-

ability using a compact microwave radar. Medical &

biological engineering & computing, 46(7):709–714.

Varanini, M., Berardi, P., Conforti, F., Micalizzi, M.,

Neglia, D., and Macerata, A. (2008). Cardiac and

respiratory monitoring through non-invasive and con-

tactless radar technique. In Computers in Cardiology,

pages 149–152. IEEE.

Xiao, Y., Lin, J., Boric-Lubecke, O., and Lubecke, M.

(2006). Frequency-tuning technique for remote de-

tection of heartbeat and respiration using low-power

double-sideband transmission in the Ka-band. IEEE

Transactions on Microwave Theory and Techniques,

54(5):2023–2032.

MUHD: A Multi-channel Ultrasound Prototype for Remote Heartbeat Detection