CONTINUOUS ANALYSIS OF REPOLARIZATION

CHARACTERISTICS DURING INSULIN INDUCED

HYPOGLYCEMIA

J. A. Lipponen, P. A. Karjalainen, M. P. Tarvainen

Department of Physics and Mathematics, University of Eastern Finland, Kuopio, Finland

J. Kemppainen, H. Mikkola, T. K¨arki

Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland

T. Laitinen

Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland

Keywords:

ECG, Hypoglycemia, QT-time, Principal component regression.

Abstract:

Hypoglycemia has been shown to affect ECG. Reported changes are prolongation of QT-interval and increased

R/T amplitude ratio. These ECG changes are suggested to be connected to so-called dead in bed syndrome.

Continuous analysis of ECG changes and blood glucose values, during insulin induced hypoglycemia is pre-

sented. Altogether 22 subjects were analyzed in three different groups; 1) healthy group 2) diabetic patients

diagnosed less 5 years ago and 3) chronic diabetics diagnosed over than 5 years ago. The results showed that

20 of 22 subjects’ QT-time was prolonged during hypoglycemia. In addition, in group 3 changes were smaller

than in groups 1 and 2.

1 INTRODUCTION

The dead in bed syndrome which refers sudden death

in type 1 diabedic patients have been widely studied

in past decades. Earlier studies have shown that hypo-

glycemia affects somehow on the autonomic nervous

system and cardiac repolarization. It have been hy-

pothesized that these hypoglycemia related changes

could be connected to dead in bed syndrome. Thus

cardiac repolarization characteristics have been stud-

ied and QT time prolongation and T-wave ﬂattening

during hypoglycemia have been reported (Laitinen

et al., 2008). In addition a connection between hy-

poglycemia and vector electrocardiogram parameters

such as QRS-T-angle has been found (Koivikko et al.,

2008).

Repolarization characteristic have been estimated

by averaging T-waves during few minutes period

(Murphy et al., 2004) and then averaged T-wave sec-

tions are annotated by hand. Because normal varia-

tion of ECG parameters is very large, averaging can

remove some information and thus disturb results.

We used an advanced principal component regres-

sion (PCR) based method (Lipponen et al., 2010) to

analyze repolarization characteristics. By using the

PCR method we can analyze ECG parameters beat-

by-beat, and this enables the compararison of repo-

larization characteristic parameters such as QT-times

and blood glucose values in given time instant.

Hypoglycemia may result in seizure or unaware-

ness, and can thus be fatal e.g. in driving conditions

(Cox et al., 1993). Since hypoglycemia seems to af-

fect ECG, were there have been some studies which

tries to predict hypoglycemic events using these ECG

changes (Nguyen et al., 2008). However, in almost all

studies euglycemic and hypoglycemic clamps are an-

alyzed separately, and thus, the key information; how

long hypoglycemic event should last before remark-

able ECG changes occur have not yet been reported.

We used continuous measurements and beat-by-beat

analysis, which gives us the opportunity to compare

estimated repolarisation parameters with blood glu-

cose values continuously, and thus, hopefully answer

the question is it possible to predict hypoglycemic

107

A. Lipponen J., A. Karjalainen P., P. Tarvainen M., Kemppainen J., Mikkola H., Kärki T. and Laitinen T..

CONTINUOUS ANALYSIS OF REPOLARIZATION CHARACTERISTICS DURING INSULIN INDUCED HYPOGLYCEMIA.

DOI: 10.5220/0003122201070111

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011), pages 107-111

ISBN: 978-989-8425-35-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

events using ECG changes.

Many of the earlier studies have used diabetic

or/and healthy test subjects (Laitinen et al., 2008;

Koivikko et al., 2008). However, it is known chronic

diabetics who have suffered from diabetes for a long

time have lower autonomic response to hypoglycemia

than subjects with sorter history of diabetes. In this

reason our dataset contains three different groups of

subjects: 1). healthy normal subjects, 2). diabet-

ics who have suffered diabetes less than 5 years and

3). chronic diabetics who have suffered from diabetes

over 5 years.

2 METHODS

Advanced PCR based method was used for analyzing

repolarization characteristics beat-by-beat (Lipponen

et al., 2010). In the PCR method each T-wave was

modeled using three optimal orthogonal basis vec-

tors. These basis vectors were obtained as the most

signiﬁcant eigenvectors of correlation matrix com-

puted from 1000 previous T-wave segments. Use of

such a large number of T-wave segments was possi-

ble because no remarkable heart rate or morphology

changes where present in used measurements, and on

the other hand such a large amount of prior infor-

mation maximize the denoising effect of the model.

Similar PCR approach was applied to model the QRS

complexes.

From each estimated waveforms, Q-wave onset,

R-wave peak, T-wave peak and T-wave offset were

then extracted. From these extracted time points QT

interval, RR interval adn R/T-wave amplitude ratio

time series weere then formed. In addtition, heart

rate corrected QTc time series was formed by using

Friedricia’s method.

For time series trend estimation, smoothness pri-

ors method was used (Tarvainen et al., 2002). Before

the trend estimation each time series was transformed

evenlly sampled time series by using 4Hz cubic spline

interpolation. Used trend estimation method reﬂects

time-varying lowpass ﬁlter with adjustable cutoff fre-

quency which can be changed by using a smooth-

ing parameter α. Because in these time series the

effects due to off the glucose concentration changes

are shown in very low frequency range, relatively low

cutoff frequency was used.

Glucose values were measured at 5 minute inter-

vals, but values were then interpolated such that sam-

ple rate was 4 Hz, same as for all time series. Al-

though second order and seven point Savitzky-Golay

smoothing was done to beforehand to reduce mea-

surement errors, because it is highly presumable that

blood glucose value doesn’t change rapidly during 5

minutes such a smoothing is recommended.

3 MATERIALS

ECG measurements were recorded in Turku Univer-

sity Hospital and altogether 27 subjects participated

test sessions. Continuous measurements of biosig-

nals such as ECG and EEG were acquired during the

test, along with the blood glucose measurements at 5

minute intervals. In this paper, we consentrate only

on analyzis of ECG signal. ECG was recorded using

a modiﬁed chest lead V5 with sample rate 128 Hz.

Figure 1: Measurement protocol. Target glucose zone is

presented by light blue, driving simulation by red and reac-

tion time tests by dark blue.

Table 1: Ages, sexes and mean duration of diabetes (years)

of test subjects in different groups.

Healthy T1DM T1DMc

number 9 6 7

age 43.0 ± 8.9 40.5 ± 9.8 49.4 ± 11.1

sex f/m 2/7 0/6 0/7

dignosis - 3.2 ± 2.3 22.7 ± 12.7

Subjects were divided into three groups: 1) 9

nondiabetic healthy subjects (Healthy), 2) 6 diabet-

ics whose diabetes were diagnosed less than 5 years

ago (T1DM), 3) 7 chronic diabetics, diagnosied over

5 years ago and who have suffered hypoglycemic

events repeatedly (T1DMc). Characteristics of dif-

ferent groups are presented in table 1. Unfortunately

T-wave was almost invisible in ﬁve subjects ECG and

results of repolarization characteristics were not reli-

able so those measurements were removed from ﬁnal

analysis.

Protocol of the measurement is presented in Fig-

ure 1. Firstly, blood glucose value was adjusted

range of 5-7 mmol to normoglycemic section. Nor-

molglycemic section lasted approximately85 minutes

and during this period ﬁrst driving and reaction time

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

108

Figure 2: Representative time series of three different subjects. Subject 1 is from healthy group, subject 2 is from T1DM group

and subject 3 is from T1DMc group. In uppermost axes there are glucose values and timeseries are presented in four lower

axes. Original timeseries are presented by blue line and trend is as red line. Time series from top to bottom are QT-interval,

RR-interval, heart rate corrected QT-time (QTc) and R/T amplitude ratio (R/T amp).

tests were done. Secondly insulin infusion was in-

creased and blood glucose concentration started to de-

crease (decreasing state), are the period second driv-

ing and reaction time tests were made. During the de-

creasing state blood glucose value was 5 - 3.5 mmol

and it lasted approximately 40 minutes. However,

reactions to insulin infusion differ between individ-

uals, and thus, this section time differs between in-

dividuals. After the decreasing state blood glucose

was in hypoglycemic state and third driving and reac-

tion time tests were made. Third stage lasted approx-

imately 55 minutes and target blood glucose concen-

tration was below 3.0 mmol.

4 RESULTS

In Figure 2, representative time series from each

group are shown. First subject is from healthy group,

second subject is from T1DM group and third subject

is from T1DMc group. In uppermost axes there are

subject’s glucose values and different time series are

presented in lower axes. Time series are QT-interval,

RR-interval, heart rate corrected QT-interval and R/T

amplitude ratio. In addition also the trends of each

timeseries are presented by red solid line. Trend was

calculated by using a smoothing parameter α = 10

which means a cutoff frequency 0.001 Hz.

In Figure 3 mean glucose values and mean QTc-

CONTINUOUS ANALYSIS OF REPOLARIZATION CHARACTERISTICS DURING INSULIN INDUCED

HYPOGLYCEMIA

109

Figure 3: Glucose values and change of QTc-times during the measurement, for the three groups. Upper axes there are each

individual subjects glucose values by thin line and group mean and standarddeviations of thick blue line. Lower axes there

are trends of QTc-times form each subjects by thin line and group mean values and standarddeviations by thick blue line.

times for each group are presented as thick blue line.

Each subject s glucose values and trend of the QTc-

times are presented as thin lines. QTc times are pre-

sented as changes from baseline, where the mean of

he normoglycemic section was taken as the baseline

value. Reaction to insulin infusion is highly vari-

able between individuals and thus QTc time series are

timescaled using measured glucose values and mean

section lengths. That is, time when blood glucose was

last time more than 5 mmol was set to end of the sec-

tion 1 (i.e. 85 minutes), time when blood glucose was

ﬁrst time lower than 3.5mmol was set to end of the

second section (i.e. 125 minutes) and rest of the data

is scaled to section 3 which lasts 55 minutes.

5 DISCUSSION

Time series analysis of repolarization characteristics

have been presented. As can be seen in Figure 2 nor-

mal variation of QTc and R/T amplitude ratio time

series is quite large and thus changes affected by glu-

cose concentration might be hard to ﬁnd without beat-

to-beat analysis. Especially diabetic groups changes

are only visible in low frequency trend component

and normal variation is much bigger than changes

affected by hypoglycemia. In Figure 3 group anal-

yses of QTc time series are shown. Although mea-

surements were made by using scripted protocol, glu-

cose variation between individuals, is so large, that in

group analysis we have to scale time series using glu-

cose values. Time scaling was done so that the end

of the section one glucose was 5 mmol and end of the

section 2 glucose value was 3.5 mmol in all subjects.

Without the time scaling time series between the sub-

jects cannot be compared in time domain.

In healthy normal group QTc time increased over

10 ms during hypoglycemia when comparing to nor-

moglycemic section. In diabetic groups (T1DM,

T1DMc) QTc time was not increasing by one sub-

ject/group. However, in both groups mean value

was clearly higher than baseline value which can be

clearly seen in Figure 3. When comparing differences

between the tree groups it can be seen that largest re-

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

110

sponses were in healthy group and lowest in T1DMc

group. Subjects in T1DMc group have suffered from

diabetes for a long time and they might be habituated

to hypoglycemic events and thus autonomic response

are lower than other in groups.

Prediction of hypoglycemic events by using ECG

parameters seem to be quite challenging, because

changes originating from glucose are delayed so that

they occur normally more than 10 minutes after the

glucose value has decreased below 3.5 mmol, which

can be seen in Figure 3. Furthermore, changes are

most intensive in healthy group and lower in dia-

betic groups were glucose prediction is needed. How-

ever changes are visible and thus some intelligent al-

gorithms might recognize hypoglycemic events early

enough to prevent some dangerous situations.

ACKNOWLEDGEMENTS

This study was supported by Academy of Fin-

land (project 126873, 1.1.2009-31.12.2011; project

123579, 1.1.2008-31.12.2011)

REFERENCES

Cox, D., Gonder, L., and Clarke, W. (1993). Driving

decrements in type i diabetes during moderate hypo-

glycemia. Diabetes, 42:239–43.

Koivikko, M., Karsikas, P., Salmela, P., Tapanainen,

J., Ruokonen, A., Seppnen, T., Huikuri, H., and

Perkiomaki, J. (2008). Effects of controlled hypogly-

caemia on cardiac repolarisation in patients with type

1 diabetes. Diabetologia, 51:426–435.

Laitinen, T., Lyyra-Laitinen, T., Huopio, H., Vauhkonen,

I., Halonen, T., Hartikainen, J., Niskanen, L., and

Laakso, M. (2008). Electrocardiographic alterations

during hyperinsulinemic hypoglycaemia in healthy

subjects. Ann Noninvasive Electrocardiol, 13:97–105.

Lipponen, J., Tarvainen, M., Laitinen, T., Lyyra-Laitinen,

T., and Karjalainen, P. (2010). A principal component

regression approach for estimation of ventricular re-

polarization characteristics. IEEE Trans Biomed Eng,

57:1062–1069.

Murphy, N., Ford-Adams, M., Ong, K., Harris, N., Keane,

S., Davies, C., Ireland, R., MacDonald, I., Knight,

E., Edge, J., Heller, S., and Dunger, D. (2004). Pro-

longed cardiac repolarisation during spontaneous noc-

turnal hypoglycaemia in children and adolescents with

type 1 diabetes. Diabetologia, 47:1940–1947.

Nguyen, H., Ghevondian, N., Nguyen, S., and Jones, T.

(2008). Detection of hypoglycemic episodes in chil-

dren with type 1 diabetes using an optimal bayesian

neural network algorithm. In 30th Annual Inter-

national IEEE EMBS Conference Vancouver, British

Columbia, Canada.

Tarvainen, M., Ranta-aho, P., and Karjalainen, P. (2002).

An advanced detrending method with application to

hrv analysis. IEEE Trans Biomed Eng, 49:172–175.

CONTINUOUS ANALYSIS OF REPOLARIZATION CHARACTERISTICS DURING INSULIN INDUCED

HYPOGLYCEMIA

111