AUTOMATIC EVALUATION OF THE

QUANTITATIVE SEISMOCARDIOGRAM

Z. Trefny, J. Svacinka, S. Trojan, J. Slavicek, P. Smrcka and M. Trefny

Cardiological Laboratory, U Průhonu 52, Prague 7, Czech Republic

Institute of Physilogy, 1

Medical Faculty, Charles University in Prague, Albertov 5, Prague 2, Czech Republic

Faculty of Biomedical Engineering, Czech Technical University in Prague, Studnickova 7, Prague 2, Czech Republic

Keywords: Time-domain segmentation of the seismocardiogram, J-wave recogniton.

Abstract: The device for quantitative seismocardiography (Q-SCG) detects cardiac vibrations caused by the heart

activity, the measuring sensor is usually placed in the plate of the chair – additional instruments applied on

the proband’s body are not required. The results of the Q-SCG analysis are usable in various clinical fields.

The first and most important step in the process of detection of significant characteristics of measured Q-

SCG curves is to detect pseudo-periods in the signal regardless of the initial pseudo-period position. Other

characteristics can be acquired by a relatively simple process over the appointed pseudo-period. The

experimental equipment for the Q-SCG measuring and analysis was developed and also special algorithms

for preprocessing, segmentation and interactive analysis of the Q-SCG signal were developed. In this

contribution technical principles of the quantitative seismocardiography are introduced; the method is easy,

robust and is appropriate for real-time Q-SCG processing.

1 INTRODUCTION

Ballistocardiography (BCG): In 1936, Starr began

the era of high-frequency ballistocardiography,

which lasted approximately 15 years. Different types

of instruments were developed, on which the

displacement, velocity or acceleration of the body

lying on a table was measured. Later studies showed

that there are difficulties when comparing records

registered on different apparatuses. This was mainly

caused by two factors: (a) the instrument’s natural

frequency, (b) the instrument’s damping.

Figure: 1: Records registered using the old BCG

instrument with a frequency of 2Hz and critical damping.

The lower curve depicts the effect of force applied, which

is of the same intensity but differs in the duration. The

upper curve is a record, from which one cannot determine

either size or duration of the acting force.

Quantitative ballistocardiography (Q-BCG): Fo-

llowing the critical evaluation of all these facts, we

began in 1952 our own experiments related to the

construction of an apparatus which would lack the

aforementioned shortcomings. We constructed an

apparatus whose advantages lie not only in the

simplicity of its design, but also in its important

functional qualities. To achieve a minimal distortion

caused by the transmission from the origin of the

force to the recorder it is necessary that the natural

frequencies of the transmission systems are as far as

possible from the mentioned frequency range.

The cardiovascular activity is manifested by a

force acting on the human body which represents a

mechanical vibratory system transmitting the force

to the balistocardiographic apparatus.

The basic part of our portable quantitative

balistocardiograph is a very rigid piezoelectric force

transducer resting on a rigid chair. The examined

person sits (Figure 2) on the light seat placed on the

transducer and the force caused by the

cardiovascular activity is measured in this way. The

output of the piezoelectric pick-up is fed into an

operational amplifier.

463

Trefny Z., Svacinka J., Trojan S., Slavicek J., Smrcka P. and Trefny M. (2007).

AUTOMATIC EVALUATION OF THE QUANTITATIVE SEISMOCARDIOGRAM.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - AIDSS, pages 463-467

DOI: 10.5220/0002412604630467

 SciTePress

Figure 2: Position of the examined person during the

QBCG session.

The advantage of the piezoelectric transducer is

in very low compliance together with a very high

natural frequency. Another advantage of the rigid

pick-up is the fact that it can be preloaded with a

substantial static force – the weight of the examined

person, and it is still possible to measure the

alternating forces of the magnitude of g+ (gram as

weight). The measured force is registered (REG)..

Figure 3: Records registered using the QBCG instrument.

The lower curve depicts the effect of force applied. The

upper curve is a record, from which we can determine size

and duration of the acting force. Compare with BCG

record on Figure 1.

Quantitative seismocardiography: (Q-SCG: This

method enables the recording of force applied

without phase or time deformation. Thus, heart rate

may be monitored and analysed using the method of

heart rate variability: statistical and autocorrelation

analysis, spectral analysis, total effect of regulation,

vegetative homeostasis, activity of subcortical

centre, activity of the vasomotor centre and stress

index (SI). For example the SI we can calculate

simply as

SI = AMo / (2 . Mo . MxDMn)

where Mo is modus of the RR interval series, AMo

is the amplitude of the modus and MxDMn is a

difference between maximal and minimal RR

interval . The SI describes the tension of regulative

systems, represents the degree of stress of regulatory

systems (the degree of the predominance of the

activity of the central mechanism of regulation over

the autonomic ones).

The method of Q-SCG was designated by the

laboratory employees as an absolutely non-invasive,

and the persons examined did not have any

electrodes attached to the body surface and were not

connected by cables to the registering instrument.

This new field of monitoring heart activity, whereby

we determine both amplitude-force and time-

frequency relationships, is termed quantitative

seismocardiography. Thus, one may determine the

force-response of the cardiovascular system to

changes in external stimuli, as well as the

autonomous nervous system regulation of the

circulation and the activity of the sympathetic and

parasympathetic systems.

2 METHODS OF QSCG

MEASUREMENT AND

ANALYSIS

2.1 Experimental Equipment

In terms of practical use, a portable telemetric

system for the Q-SCG measuring has been

developed. This system allows data to be acquired

and assessed using quantitative seismocardiography

(Q-SCG). It is composed of the three HW modules

that are telemetrically interconnected with the option

of interconnecting through a metallic line. These are

the seismocardiographic, the accelerometric modules

and a module for the data transfer interconnected

with the PC through the USB interface. Block

scheme of the whole system is on figure 6.

Figure 4: Main sensor of she Q-SCG measuring equipment

- detail of the solid-state accelerometer between measuring

metal plates.

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Sensing mechanical body reactions, which are

induced in response to the cardiovascular dynamics,

is provided by the seismocardiographic module,

which reads the strain coming from the mechanical

deformation of the piezo-electric plate. This sensing

module (figures 4 and 5) is mounted on a special

device, which works by transmitting the mechanical

body reactions onto the piezo-electric element. The

data transfer module is designed to transmit the data

from the radio-module into the PC through the USB

interface.

Figure 5: Measuring plates of the proposed Q-SCG device.

Figure 6: Block scheme of the experimental Q-SCG

device.

2.2 Algorithm for the Time-Domain

Segmentation of the Q-SCG

Algorithms for preprocessing, segmentation and

interactive analysis of the Q-SCG signal were

developed. In this contribution we will focuse on the

original method for basic segmentation of the Q-

SCG signal in time-domain; this first step is crucial

for the successfulness of the whole diagnostic

process. Our method is relatively simple and was

developed for the detection of Q-SCG pseudo-

periods in real time. The method is derived from a

well-known and robust algorithm for QRS complex

detection in traditional electrocardiograms (ECG),

originally developed by Hamilton et al. The

algorithm was based on the first derivative of the

input signal and many thresholds and parameters are

automatically adapted to individual changes in the

input signal using sophisticated empirical rules. The

results (position of the dominant – so-called R -

wave) are obtained with some detection delay

(above 200 ms). For details on the algorithm, see

(Hamilton).

For our purposes it is important that the initial

values of many parameters are adjustable and by

modification of these values the original method was

slightly adapted to Q-SCG’s different curve

morphology. Namely the following parameters were

changed: (1) length of the first derivative from the

original 10 ms to 80 ms, (2) length of the high-pass

pre-filter from 125 ms to 350 ms, (3) length of

moving window integration from 80 ms to 200 ms.

Optimal values were selected experimentally in

order to achieve the best detection results.

Additionally, we developed a special backward

searching process for the precise detection of the

position of the I-wave and J-wave in each Q-SCG

pseudo-period.

The function of the whole algorithm is as follows:

output of the traditional ECG QRS detector gives the

rough position of the systolic complex inside the Q-

SCG - candidate X. Then the specific morphology of

the Q-SCG curve is utilized to backward search the

position of the J-wave – we expect the first big

negative peak in MTI samples (about 100 ms). If the

detection is successful, we assign the position of the

peak as the I-wave; see Figure 7.

Finally we search forward for the position of the

J-wave, which we expect to be the first big positive

peak in maximally MTJ samples (about 160 ms), see

Figure 8.

Control

unit,

A/D

Converter

Commu-

nication

interface

WIFI

Bluetooth

SCG unit

ECG measuring

unit

Auxiliary

measuring units

PATIENT‘S MEASURING DEVICE

PATIENT

WORKSTATION

FOR

SUPERVISON,

DATA

PROCESSING

WIRELESS CONNECTION

AUTOMATIC EVALUATION OF THE QUANTITATIVE SEISMOCARDIOGRAM

465

For the peak-detection we used a very simple

method based on the first difference (length 15 ms):

when the transition from negative to positive value

of the difference occurs, then the sequence is marked

as a negative peak; the transition from a positive to

negative difference means a positive peak. If

searching for the J-wave or the I-wave fails,

candidate “X” is rejected and the algorithm

continues without detection of the Q-SCG pseudo-

period.

The rejection of “candidate X” is very important

step and it increases robustness of the whole

detection procedure against the artifacts – see

demonstration on the Figure 9.

The false detection of the dominant “candidate X”,

which is not a true Q-SCG cycle, was corrected by

the proposed simple backward searching algorithm,

because the morphology in the nearest neighborhood

of the point X3 does not match the detection rules –

backward searching for the I-wave in MTI samples

was not successful, the false positive detection of the

systolic complex was correctly rejected.

Our experimental software allows also automatic

extraction of classical Q-SCG hemodynamical

parameters, especially so called systolic force (F).

The current version of the system is designed for OS

Windows XP and has user-friendly interface. Block

scheme of the system is on figure 11.

3 CONCLUSION

For high-quality measurements we can obtain good-

looking signals for which both methods exhibit

excellent results. For disruptive and spurious signals

there is still a good chance of obtaining authentic

information because we first detect the impairments

and remove the particular interval of the signal.

For good-looking and typical signals, the

methods behave very well, achieving nearly

complete success (see Figure 12). The Q-SCG signal

offers specific information about functional

changes of the cardiovascular system regulation

which preceded the structural changes coming later.

The equipment is ready for use, algorithms for

automatic analysis of the Q-SCG signal are

prepared.

Quantitative seismocardiography probably offers

a more complex view of both inotropic and

chronotropic hearth function. It will be suitable for:

6000

7000

8000

9000

10000

11000

12000

13000

1 58 115 172 229 286 343 400 457

time [sam ples]

Force (quant. levels )

max

MTI

Figure 7: Backward local I-peak searching in the Q-

SCG cycle.

6000

7000

8000

9000

10000

11000

12000

13000

1 58 115 172 229 286 343 400 457

time [samples]

Force (quant. levels)

I J

max

MTJ

Figure 8: Forward local J-peak searching in the Q-SCG

cycle.

8800

10800

12800

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221

time (samples)

Force (quant. levels)

max

MTI

Figure 9: Rejection of the false beat detection. We searc

backward from “candidate X3” for the first big negative

peak. The I-wave must be recognized in MTI samples

(about 100 ms), so in this case the detection was not

successful.

5000

7000

9000

11000

13000

15000

1 73 145 217 289 361 433 505 577 649 721 793 865 937 1009 108 1 1153 1225

time (samples)

Force (quant. levels)

X2 X3 X4

Figure 10: Result of the whole detection: false candidate

X3 was correctly rejected.

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examining operators exposed to stress; for assessing

the effect of work, fatigue, mental stress; for

monitoring persons as part of disease prevention; for

determining a person’s ability to carry out their

duties both on the ground and in the air.

ACKNOWLEDGEMENTS

This work has been supported by the Ministry of

Education of the Czech Republic under project No.

MSM6840770012 and by the project EUREKA

E!3031.

REFERENCES

Jerosch-Herold, M. et al., 1999. The seismocardiogram as

magnetic-field-compatible alternative to the

electrocardiogram for cardiac stress monitoring, In

International Journal of Cardiac Imaging, 15(6), pp.

523-31

Trefny, Z. et al., 1996: Some physical aspects in

cardiovascular dynamics, In J. Cardiovasc. Diagnosis

and Procedures, 13(2), pp. 141 - 145

Hamilton, P. – Tompkins, W.J. (1987): Quantitative

investigation of QRS detection rules using the

MIT/BIH arrhythmia database, In IEEE Trans.

Biomed.Eng., 33, pp. 1158-65

Trefny Z. – David E. - Bayevsky R.M.: Achievements in

Space Medicine into Healt Care Practice and Industry,

Development of Space Cardiology metods in the

Earth's Health Service, Berlin 2001

Freisen G., Jannet T.: A comparison of the Noise

Sensitivity of Nine QRS Detection Algorithms’, IEEE

Trans. Biomed. Eng., 1990, 85(1)

Measuring

software

Visualization,

printing and

archivation

Setup and

calibration

Signal

acquisition

and data

Data processing unit

Unit for time-

domain

segmentation of the

Unit for extraction

of the

hemod

namical

Unit for extraction of

the HRV parameters

Figure 11: Block scheme of the software system. Presente

algorithm is in the box „Unit for time-domain segmentatio

of the Q-SCG curves“.

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10000

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1 70 139 208 277 346 415 484 553 622 691 760 829 898 967 1036 1105 1174 1243 1312 1381 1450

Force (quant. levels)

time (samples)

J J

Figure 12: Typical Q-SCG signal with correctly placed

reference points.

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