ANALYSIS ALGORITHMS FOR A FIRST-AID SENSOR

Detecting Vitality Parameters such as Pulse and Respiration

Daniel Wettach, Marc Jaeger, Armin Bolz

Institute of Biomedical Engineering, University of Karlsruhe, Hertzstr. 16, Karlsruhe, Germany

Timur Oezkan

Department of Anaesthesiology, University Hospital Heidelberg, Germany

Keywords: Physiological processes, First-aid sensor, real-time sensor system, wearable sensor and system,

computational intelligence, emergency situation.

Abstract: In this paper the software algorithms necessary to analyze the signal provided by a first-aid sensor system

that detects pulse and respiration at a single point are described. In an opinion poll four of five

inexperienced first responders were interested in using this kind of system as support in emergency

situations. Especially the intelligent detection of respiration is hardly popular today and in most cases only

possible offline. The software also controls several visual indicators that assist the first aider in quickly

determining the state of the patient.

1 INTRODUCTION

In emergency situations like an accident a first aider

has to decide immediately if resuscitation of the

victims needs to be initiated. The know-how of

many first aiders is not sufficient or their uncertainty

too great to be able to make this often vitally

important decision quickly (Sefrin, 2006). Our aim

is to develop a cheap and portable sensor system that

is easy and fast to use to support the first-aider with

their vital decision. It detects if pulse and respiration

of the victim work normally and provides the first

aider with a visual aid. The sensor has to be placed

on the neck of the victim and after a few seconds it

starts to display information about pulse and

respiration.

2 SIGNAL PROCESSING

2.1 Medical Concept

It is our aim to develop a sensor which measures not

only the pulse but also the frequency of respiration,

that is why the detection of electrical body signals

e.g. like an ECG is not sufficient. One solution is to

measure mechanical changes of the tissue surface

which are caused by both pulse beat and respiration.

As possible measuring point for the sensor a point

near the clavicle is estimated to deliver the best

results because that point is close to both the

subclaviar artery and the trachea. This point is

shown in Fig. 1.

Figure 1: Possible measuring point for the sensor.

2.2 Signal Acquisition

The measuring of changes in the tissue surface is

mainly done by detecting changes in distance

between the sensor and the tissue surface. Normally

the measuring of distances can be achieved by using

an LC oscillator. Due to capacitive coupling the

resonance frequency of the oscillator changes if the

259

Wettach D., Jaeger M., Bolz A. and Oezkan T. (2008).

ANALYSIS ALGORITHMS FOR A FIRST-AID SENSOR - Detecting Vitality Parameters such as Pulse and Respiration.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 259-262

DOI: 10.5220/0001064802590262

 SciTePress

distance to any organic object in- or decreases. This

change in resonance frequency is reflected in a

change of voltage. This principle is used in homes in

the form of touchless switches. However, since the

changes in the tissue surface caused by pulse beat

and respiration are very small, the use of classical

LC oscillators is - apart from a few exceptions - not

possible. Therefore a new nonlinear oscillator has

been developed. With its help it is possible to

measure changes in submillimeter range. Because of

this high sensitivity even little muscle contractions

will be detected by the sensor. As some of these

minor contractions do belong to neither pulse nor

respiration they have to be eliminated. It is

necessary to filter and process the signal to eliminate

these artifacts. An example for the measured signal

is shown in Fig. 2. For more technical information

please refer to (Jaeger, 2007).

Figure 2: Signal measured by the sensor.

2.3 Algorithms

First the signal is band-filtered with hard coded cut-

off frequencies covering the whole spectrum of

possible pulse and respiration frequencies. In the

next step a low resolution FFT of this filtered signal

is performed. In extreme cases the respiration

frequency can be higher than the pulse frequency.

Due to this there are four possible frequency bands

(Fig.3):

- the whole frequency band from the lowest

possible frequency of the respiration to the

highest possible frequency of the pulse (WF)

- the lower frequency band of respiration

from the lowest possible frequency of the

respiration to the lowest possible frequency of

the pulse (LF),

- the upper frequency band of pulse, from

the highest possible frequency of the respiration

to the highest possible frequency of the pulse

(HF)

- and the overlapping band from the lowest

possible frequency of the pulse to the highest

possible frequency of the respiration (MF).

By calculating relations between the different

amplitudes, the frequency parts with high intensity

can be discerned. Normally there will be only two

frequency parts with high intensity representing

pulse and respiration.

Figure 3: Spectrum of the filtered signal with four

frequency bands.

If there are more than two such frequency parts,

there has to be a measurement error. If no error is

detected the frequency parts defined above can be

analyzed further. If there is a frequency part of high

intensity in the lower frequency band of respiration,

there may be only one such frequency part in the

overlapping band - the one of the pulse. If such a

frequency part is additionally detected in the upper

frequency band of the pulse there cannot be any in

the overlapping band. As soon as these conditions

are met, the frequencies for pulse and respiration are

approximately determined. With the detected

frequencies it is possible to define narrow cut-off

frequencies for pulse and respiration. With these

cut-off frequencies the input signal is filtered again,

once for pulse and once for respiration.

After this step there are two rather clear signals

which are relatively free of errors.

With these

filtered signals it is possible to determine more exact

frequencies for pulse and respiration. Therefore the

filtering is designed to adapt which means that the

cut-off frequencies of the fine filters are always

readjusted using the frequencies determined by the

fine filtered signals. Additionally the approximated

frequencies of pulse and respiration gained from the

roughly filtered input signal are used to validate the

adjustment of the cut-off frequencies for the fine

filters. The determination of the frequency of the

fine filtered signal works as follows: On the one

hand zero-crossings are counted using a hysteresis,

on the other hand a high resolution FFT for the

filtered signals is processed. Both results undergo a

plausibility check.

The frequency determination by counting zero-

crossings is done by using hysteresis. A zero-

crossing is only interpreted as such if the signal does

not only drop below a lower hysteresis border but

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then also rises over an upper hysteresis border. The

point in time in which the signal rises over the upper

hysteresis border is used as zero-crossing. The time

difference between two such points can be

interpreted as the periodic time of the signal. The

reciprocal of this periodic time is the frequency of

the signal. To get a more stable frequency value the

last two periodic times determined in this way are

averaged. If the signal does not pass a hysteresis

border for a certain period of time its quality is

assumed to be insufficient and the frequency value

will be set to zero.

When the frequency is determined by using a high

resolution FFT the frequency part with the highest

amplitude is used as result. To get a more reliable

value the algorithm averages out the last five

determined frequency values.

Figure 4: Logical organization of the plausibility check.

To eliminate remaining errors a plausibility

check is done using the determined frequency values

by counting zero-crossings and FFT. Fig. 4 shows

the logical organization of the plausibility check.

As soon as both methods - counting zero-

crossings and FFT - calculate approximately the

same frequency value the signal is assumed to be

free of errors and the value calculated by FFT will

be indicated as final result. If this value deviates too

much from the previous shown result an error will

be assumed. In this case the last correct value prior

to the error is indicated until both methods once

again calculate an approximately equal value. If the

value determined by counting zero-crossings drops

below a predetermined border, the signal is assumed

to be too weak and the final frequency value will be

set to zero.

Fig. 5 shows the chronological sequence of

possible results of the two methods counting zero-

crossings and FFT and the corresponding result of

the plausibility check (frequency line in Fig. 5).

Figure 5: Possible chronological result of the plausibility

check.

2.4 Visualization

The sensor uses LEDs to indicate the state of a

patient. For both pulse and respiration there is a

LED showing if the respective function works

normally. If either pulse or respiration are abnormal,

it will be shown by an additional LED. A fourth

LED indicates that the sensor system is working

correctly to avoid any delay due to unrecognized

malfunctions. The sensor with its LEDs is shown in

Fig. 6.

Figure 6: The Sensor with the four LEDs.

If the LEDs indicate that pulse and/or respiration

does not work normally and the patient does not

show any reactions to external stimulation the first

aider has to initiate cardiopulmonary resuscitation

(CPR).

ANALYSIS ALGORITHMS FOR A FIRST-AID SENSOR - Detecting Vitality Parameters such as Pulse and Respiration

261

3 APPLICATION

The following diagram (Fig. 7) shows the sequence

from the arrival of a first aider at an accident up to

the corresponding action he has to take.

Figure 7: First aider mode of operation.

4 RESULTS

The developed algorithms have been tested with

several signals recorded by the sensor. The time it

took the software to detect each the pulse and the

respiration signals has been put in relation to the

overall time of the measurement. The results are

shown in Table 1. The sensor is able to detect pulse

in 91.15% and respiration in 81.15% of the time the

sensor is active. The timeframe during which pulse

and respiration are not detected is mostly at the

beginning of the measurement because at least one

or two cycles are necessary to calculate useful

values. The lower percentage for respiration

detection can be explained with the lower breathing

frequency due to which the initial cycles take longer

to complete.

Table 1: Test cases for pulse and respiration.

Signal

length in s

Pulse detected

in %

Respiration

detected in %

75.txt 132,92 92,33 87,71

84.txt 191,55 97,49 92,69

87.txt 97,90 95,86 85,41

90.txt 80,63 94,51 78,69

137.txt 76,13 96,69 79,64

190.txt 119,59 97,12 71,99

carotis.txt 29,22 83,06 57,56

kieferwinkel.txt 169,61 97,70 91,16

sternoclaido.txt 199,36 83,74 82,68

subclavia.txt 74,99 73,00 84,00

5 DISCUSSION

The results shown in Table 1 are not satisfying

because they were not verified by comparing them

to actual pulse and respiration data detected by other

means. In addition the sensor has to be tested in

extreme situations for instance on board of rescue

helicopters and ambulances again while comparing

the sensor data to actual pulse and respiration data.

According to these test results - like possible

deviations of the sensor detected data from the

actual pulse and respiration - the algorithms will

have to be improved. The main task in the future

will be to find ways to correct inaccuracies caused

by small movements. This could be done for

example by integrating a neural fuzzy system into

the software that can use additional criteria to

ascertain that the calculated values are correct.

Another possibility is to include an acceleration

sensor in the system that is able to detect certain

movements and to correct the data accordingly.

REFERENCES

P. Sefrin, 2006, Bei der Reanimation gilt ab sofort: Zuerst

30 mal das Herz massieren und dann zweimal

beatmen, Aerztezeitung

M. Jaeger, M. Mueller, D. Wettach, T. Oezkan, J. Motsch,

T. Schauer, R. Jaeger, A. Bolz, 2007, First-Aid sensor

system: New methods for single-point detection and

analyzation of vitality parameters such as pulse and

respiration, IEEE EMB, ISBN 1-4244-0788-5

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