INFLUENCES OF DIGITAL BAND-PASS FILTERING ON THE

BCG WAVEFORM

Mikko Koivuluoma

, Laurentiu Barna

, Teemu Koivistoinen

1) Institute of Signal Processing, Tampere University of Technology, Korkeakoulunkatu 1, Tampere, Finland

Tiit Kööbi

, Alpo Värri

2) Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland

Keywords: Ballistocardiogram, ProHeMon, EMFi.

Abstract: The band pass filter is used to attenuating breathing originated signal from the heart originated BCG signal.

The bandwidth of the both signals slightly overlap, hereby the complete attenuation of the breathing is not

possible without also altering the heart originated BCG waveforms and the parameters which are obtained

from the BCG. In our study we investigated the optimal lower cut-off frequency, and 1.3 Hz was found as

the reasonable compromise between the attenuation of the breathing and the altering of the heart originated

BCG.

1 INTRODUCTION

The developments in sensors, recording devices, and

signal processing techniques, experienced over the

past two decades, significantly increase the analysis

possibilities of the ballistocardiogram (BCG). The

potential of BCG to provide valuable information

about the condition of the heart was clearly

demonstrated even from early stages, when it was

used to predict the evolution of ischemic myocardial

diseases. Other clinical studies in which BCG

proved useful include prognosis, monitoring,

physical conditioning, stress tests, evaluation of

therapy, and cardiovascular surgery (Marinelli

1991). The use of BCG has also been reported in

epidemiological and cardiovascular screening

studies (Star and Wood, 1961; Kiessling, 1970;

Lynn and Wolf, 1974). Because during the signal

measurement stage, no electrodes need to be

attached to the body of the subject, BCG presents

great potential for modern healthcare, especially in

the case of home care monitoring.

The majority of modern BCG analysis methods

rely on two separate stages, the signal measurement

and the offline signal analysis with the help of a

digital computer. When this is the case, one can

closely look into the measured signal and decide

about the filtering methods appropriate for each

particular measurement. Such offline or visual

analysis of BCG signals is time consuming and the

costs associated with it are considerable. A better

solution was offered by the advances of computers

and electronic technology that provide a good basis

for automatic cardiac performance monitoring and

heart disease diagnosis, by assisting clinical practice

and thus saving diagnosis time.

Because the raw BCG signal is usually corrupted

with breathing and movement artifacts, a pre-

processing of the raw data is necessary before the

interpretation stage can take place. For a real-time

automatic BCG analysis system, one should know in

advance how this preprocessing will affect the BCG

waveform. In the mid sixties the subjects were

asked to hold their breath for some part of the

recording, in order to eliminate the respiration effect

on the BCG signal. Although this technique

increases the quality of the raw BCG signal, it

cannot be performed over long periods of time and it

is rather uncomfortable for the subject. For these

reasons digital filtering was proposed as an

alternative, but was not pursued until recently due to

the limitations of computing power at that time.

This study was made to investigate how

bandpass filtering the raw BCG signal at different

cut-off frequencies affects the BCG waveform, with

respect to the BCG waveform parameters usually

taken into consideration as meaningful diagnostic

information.

Koivuluoma M., Barna L., Koivistoinen T., Kööbi T. and Värri A. (2008).

INFLUENCES OF DIGITAL BAND-PASS FILTERING ON THE BCG WAVEFORM.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 84-89

DOI: 10.5220/0001061100840089

 SciTePress

BCG Signal

ECG Signal

Figure 1: One dimensional BCG (top) and ECG (bottom)

signals from a normal healthy subject during breath

suppression.

2 THE BALLISTOCARDIOGRAM

BCG is a measure of the heart’s mechanical activity

associated with the flow of blood out of the heart’s

chambers. It was studied very actively from the

1940s until the mid-1970s when research activity in

this area ended almost completely due to the

technical limitations of sensors, signal conditioning

electronics, recording devices and the high

diagnostic value of the already available

electrocardiogram (ECG). With the advance of

technology, BCG signals can now be easily

recorded, unobtrusively, both on supine and sitting

positions using noninvasive modern techniques.

The idealized BCG waveform consists of seven

components, labeled the H through N wave (Starr

and Noordergraaf, 1967) with the IJK-complex

being the predominantly identifiable segment (see

Fig. 1). In time, typically the BCG will trail the ECG

by about 0.1-0.3s (Braunstein and Thomas, 1953).

A simple spectral analysis of the BCG signal

shows that the spectra power of the BCG waveform

is mostly within 20Hz (see Fig. 2) but in literature is

was reported that the BCG waveform lies between

0.1 Hz and 40 Hz.

BCG waves change with respiration and from

one patient to another making BCG analysis a rather

difficult task. Moreover, usually the raw BCG signal

is also corrupted with movement artifacts, which

makes the recognition of characteristic BCG

waveforms almost impossible (see Fig. 3). In order

to eliminate the respiration effect, seen as a drift

from the baseline of the signal, various bandpass

digital filters have been used and reported in

literature with the most common frequency range

0 10 20 30 40 50 60 70 80 90 100

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Frequency (Hz)

Figure 2: Power spectrum of the BCG wave shown in Fig.

1. As it can be seen, the spectra power are mostly within

20Hz.

BCG signal

ECG signal

movement artifact

breath cycle

Figure 3: One dimensional BCG (top) and ECG (bottom)

signals from a normal healthy subject. Notice the

breathing effect on the BCG signal as a drift from the

baseline and the movement artifact that causes

abnormalities in the signal and makes the detection of the

BCG peaks difficult.

being between 1 Hz and 20 Hz. Filtering above 1 Hz

should eliminate the respiration effect but as

different people have different respiration

frequencies the filter could be selected at a lower or

higher cut-off frequency, depending on the

parameters studied. Because the BCG signal has

components below 1 Hz as well and any kind of

digital filtering will affect those components, it is

important to know how the BCG waveform will be

affected by filtering at different cut-off frequencies

in order to know the tradeoff between the selected

cut-off frequency and the changes suffered by the

BCG waveform due to filtering.

Traditionally the physicians interpret different

parameters of the measured BCG waveforms and

calculate relevant indicators to determine whether or

not the heart shows signs of cardiac diseases.

INFLUENCES OF DIGITAL BAND-PASS FILTERING ON THE BCG WAVEFORM

The various reported off-line analysis methods

use different relations between the BCG peaks to

classify the waveforms. The average cardiac stroke

volume can be estimated as follows (Starr et al

1940):

3/2

7(3 2)ACSV I J AC=+

(1)

where I and J are the BCG waves (in mm), A the

subject’s aortic internal diameter (in cm

) and C the

duration of the cycle (in seconds). From the ACSV,

the cardiac output can be further calculated (Brown,

Hoffman and De Lalla 1950, Starr et al 1940 ).

The amplitudes of the H-I, I-J, and J-K segments

and their expiratory and inspiratory ratios HI

/HI

/IJ

, JK

/JK

were used to express respiratory

variation in BCG waves and the HI

/IJ

and

/IJ

ratios of the averaged mean amplitudes

were used as amplitude ratios (Onodera 1964,

Scarborough 1953, March 1955). The time

amplitudes (TA) obtained by dividing the amplitude

of a segment by its interval measured in expiratory

and inspiratory phases investigated on H-I, I-J, and

J-K intervals have also been reported. Other

intervals measured in the literature are: the P-H

interval measured from the beginning of the P wave

of the electrocardiogram to the tip of the H of the

Ballistocardiogram the Q-H, Q-I, Q-J and Q-K

measured from the beginning of the Q wave of the

electrocardiogram to the H, I, J, and K tips of the

Ballistocardiogram (Onodera 1964).

3 PATIENTS AND METHODS

In this study we used a subset of 15 subjects from

the signal database recorded during the ProHeMon

project (Koivistoinen et al. 2004). All the

measurements were conducted by an experienced

research nurse at the Department of Clinical

Physiology of the University Hospital from

Tampere, Finland. The study protocol was approved

by the Ethical Committee of Tampere University of

Tampere and a written consent was obtained from

each subject measured. Part of the measurement

protocol included the parallel measurement of ECG,

impedance cardiogram (ICG) and two BCG

channels. The subjects were divided into three

groups (5 subjects in each group):

1. 20-30-year old healthy students

2. healthy 50-70-year old men

3. 50-70 year old men with myocardial infarct

in their medical history

Figure 4: Recording setup. In this case, no ICG leads were

connected.

All measurements were done with CircMon™, a

commercially available circulation monitor (

Medical Ltd). The ECG and ICG leads were

connected to their own dedicated channels of

CircMon and two BCG signals were connected to

auxiliary input channels. A measurement chair with

electromechanical film (EMFi™) (Kirjavainen

1987) foils and a dedicated BCG amplifier specially

designed and built for this study were used to record

and amplify the BCG signals from the back and the

seat of the chair (Junnila et al 2004, Barna et al

2005, Junnila et al 2005). The quality of the signals

was visually inspected and assessed by specialist

medical doctors. In this setup CircMon functioned as

an A/D converter and a sampling frequency of 200

Hz was used for each recording. The recordings

lasted for about 13 minute per subject. At the

beginning of the recording, the subject was placed in

supine position and no BCG signal was recorded.

This study does not include the study of the ICG

signals obtained. The gain of the BCG amplifier was

increased after the first group was recorded, so the

absolute signal values between groups are not

comparable.

The BCG signal, was filtered offline with a

Parks-McClellan optimal equiripple band-pass Finite

Impulse Response (FIR) filter designed in Matlab®

7.2 (The MathWorks Inc.). The advantage of using

an FIR filter is that the phase of the input signal is

not distorted and the delay introduced by the filter

can be easily calculated, the information about the

time location of the BCG waveform being

preserved. The lower cut-off frequency of the pass-

band was varied between 0.7 Hz and 4.6 Hz with a

step of 0.1 Hz and the upper cut-off frequency was

fixed at 20.5 Hz. A ripple of 0.1 dB was set for the

pass-band and attenuation of 60 dB for both stop-

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

0 10 20 30 40 50 60 70 80 90 10

−120

−100

−80

−60

−40

−20

Magnitude (dB)

Frequency (Hz)

Figure 5: One FIR filter used in study. This filter have

passband from 4.6 to 20.5 Hz.

bands. The transition band width was set to 0.6 Hz

(see Fig. 5 for example). The order of the filters used

was rather high (N = 848), which resulted in a high

computation time at filtering.

After filtering, the locations of the BCG

waveforms were determined using the R spikes of

the parallel ECG tracing. The BCG signal was

divided into sections of 151 samples. A mean BCG

waveform (BCGM

) was calculated as the mean of

50 % of the most similar BCG waveforms found

from the artifact free epochs of the recording. Cross

correlation was used as the measurement of the

similarity, and no visual inspection was involved.

For each mean BCG waveforms the following

parameters were calculated:

- the amplitudes of the mean H-I and I-J waves

- HI

/IJ

ratios of the mean H-I and I-J waves

4 RESULTS

In this section, a sample recording is first analyzed,

followed to an overall assessment for all the

recordings involved in this study.

In the Figure 6, the original signal (A01) with six

heartbeats is shown. The respiratory signal can be

clearly seen as a low frequency drift from the

baseline of the signal. In this example, the

measurement of characteristic BCG waveforms is

not difficult, because the heart originated component

has relatively high amplitudes compared to the

respiratory signal. Figure 7 shows the same section

of the BCG signal, band-pass filtered at different

cutt-off frequencies. When the lower cut-off

frequency is between 1.3 Hz (row one, column two)

and 2.5 Hz (row four, column four), the shape of the

signal is almost constant. Therefore, an optimal

lower cut-off frequency of about 1.3 Hz is

recommended. By studying the changes in

amplitudes of the different waveforms and HI

/IJ

ratio, we can find a more accurate cut-off frequency.

0 1 2 3 4 5 6 7

−0.2

−0.15

−0.1

−0.05

0.05

0.1

0.15

0.2

Amplitude

Time (s)

Figure 6: Raw BCG signal (subject A01).

The changes of different parameters resulted from

filtering, and expressed as function of the lower cut-

off frequency are shown in figure 8. As seen from

the previous graphics, the parameters remain

relatively constant between 1.3 Hz and 2.5 Hz, with

significant changes occurring between 1 Hz and 1.2

Hz, and after 2.6 Hz. A lower cut-off frequency can

be therefore selected between 1.3 Hz and 2.5 Hz

without greatly compromising the BCG parameters.

Figure 7: The section of the BCG-signal filtered with 40

different band pass filter. In the upper left corner is the

output of the filter with pass band from 0.7 Hz to 20.5 Hz,

and in the lower right corner is the output of the filter with

pass band from 4.6 Hz to 20.5 Hz.

INFLUENCES OF DIGITAL BAND-PASS FILTERING ON THE BCG WAVEFORM

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

0.05

0.1

0.15

0.2

Amplitude

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

0.5

1.5

Ratio

Frequenzy (Hz)

Figure 8: The different parameters as the function of the

lower cut-off frequency. Up: mean I-J amplitude (red) and

mean H-I amplitude (blue). Down: HI

/IJ

ratio.

Figure 9, shows the HI

/IJ

ratios of the all

recordings used in this study. In the most cases,

/IJ

ratios present a flat section between 1.3 Hz

to 2.2 Hz, the mean HI

/IJ

ratio and approximate

derivative of the mean HI

/IJ

, shown in Figure 10

supporting this observation.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

0.2

0.4

0.6

0.8

1.2

Frequency (Hz)

Ratio

Figure 9: HI

/IJ

ratios of the recordings used in this

study. Although up to 1Hz the ratios remain rather flat and

between 1Hz and 1.3Hz they get significantly changed,

when filtering at 1Hz the effect of respiration is still

visible in the signal, but at 1.3Hz this effect gets

eliminated (this being better for a visual inspection of

Starr classes).

Based on the observations made upon the entire

set of BCG recordings employed, the optimal lower

cut-off frequency recommended is of 1.3 Hz. For

this value, most of the signal remains unaltered, and

the major effects of the respiration are removed.

1 1.5 2 2.5 3 3.5 4 4.5

0.7

0.75

0.8

0.85

0.9

Ratio

1 1.5 2 2.5 3 3.5 4 4.5

x 10

−3

Frequency (Hz)

Figure 10: Up: the mean HI

/IJ

ratio. Down:

Approximate derivative of the mean HI

/IJ

ratio.

5 DISCUSSION

Setting the cut-off frequency of the BCG band-pass

filter is a trade-off between attenuating breathing

and altering the heart-originated components of the

raw BCG signal. Individual cut-off frequencies

might be optimal, but that would make the

comparison between patients more difficult. By

using a cut-off frequency 1.3 Hz, a reasonable

compromise was found.

Our results also show that changes of BCG

components as a result of filtering can be estimated

and accounted for in reporting BCG derived

measures and ratios. By analyzing BCG data from

recordings involving breath suppressed epoch, we

expect to refine our results in a future study.

In this study no attempts have been made to link

the amplitudes and ratio calculated from the BCG

signal to any of the cardiovascular variables such as

stroke volume

The equation presented in section “The

Ballistocardiogram” for the average cardiac stroke

volume was not feasible because no calibration has

been performed for the BCG system used and the

subjects were recorded in sitting position, unlike

Starr et al, in which the recordings were performed

in supine position. As future improvements, we plan

to combine the information available in the ECG and

ICG recordings with the one obtained from BCG..

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