HEART RATE VARIABILITY IN SIESTA POLYSOMNOGRAMS

A Preliminary Study

Xos

e A. Vila

, Arturo J. M

endez

, Abraham Otero

, Leandro Rodr

ıguez-Li

nares

and Mar

ıa J. Lado

Department of Computer Science, ESEI, University of Vigo, Campus As Lagoas s/n, 32004 Ourense, Spain

Department of Information and Communications Systems Engineering, University San Pablo CEU, 28668 Madrid, Spain

Keywords:

Sleep apnea, Heart rate variability, HRV, ECG.

Abstract:

Nowadays, sleep apnea is a disease with a high prevalence. Its diagnosis requires to admit the patient in

a hospital sleep unit and to conduct a polysomnography during the night. For this reason, many efforts have

been devoted to alternative techniques to diagnose apnea from other signals, such as ECG or oxygen saturation,

easier to obtain outside a hospital. The aim of this work is to investigate if these recordings behave similar

to overnight ECGs.This paper presents the results of a small study (only 7 patients) conducted on short naps

using heart rate variability (HRV) parameters. The results indicate that the spectral parameters are different

for obstructive sleep apnea (OSA) and healthy patients. Relationship with the apnea/hypoapnea index (AHI)

was also different. This is a promising starting point for more extensive studies in the future.

1 INTRODUCTION

One of the ﬁelds where computerized techniques are

becoming increasingly popular is the automated

analysis of physiological signals, such as

electrocardiograms (ECG) (Roche et al., 2003).

Analysis of ECG may provide information related to

different respiratory events, such as obstructive sleep

apnea (OSA), that is characterized by a cessation of

breathing during sleep.

Although precise OSA diagnosis needs a

nocturnal polysomnography, there are evidences that

heart rate variability (HRV) could offer valuable

information in relation with OSA (Penzel et al.,

2003). Spectral analysis of HRV can be a suitable

tool for the detection of OSA, since it may provide

a quantitative analysis and evaluation of the

neurovegetative nervous system. Sympathovagal

balance can be evaluated with the low frequency

(LF) components (ranging from 0.04 to 0.15 Hz) and

the parasympathetic tone can be estimated using the

high frequency (HF) components (greater than 0.15

Hz). Other indexes can be used, such as LF/HF ratio,

which can be used as an indicator of the status of the

neurovegetative control system (Gula et al., 2003;

unes et al., 2010). The very low frequency (VLF)

band (0.003-0.04 Hz) has also been used by other

authors (Park et al., 2008).

Over the last decades, an increasing number

of researchers have devoted their efforts to the

automatic detection of OSA. In 2000, the Computers

in Cardiology conference proposed a competition for

classifying potential apneic patients using only the

ECG (Moody et al., 2000). Among the proposed

systems, one was based on the ratio of the content

of two spectral regions between 0.01 to 0.05 Hz, and

between 0.005 and 0.01 Hz (Drinnan et al., 2000),

while another one used an algorithm based on QRS

changes.

Most of the papers cited so far deal with the

diagnosis of nocturnal sleep apnea, usually performed

by means of polysomnography. However, up to now,

no attention has been paid to the apneic events that

can occur during the siesta period, a short nap in the

early afternoon.

Siesta is a Spanish habit that has been proved

to contribute to increase productivity, to improve

alertness and to reduce risk of accidents (Korman

et al., 2007). Furthermore, daytime sleep can lower

blood pressure and provide better cardiovascular

recovery from psychological stress (Brindle and

Conklin, 2011). We think that it can be interesting

to study HRV in this type of recordings in order to

identify apneic events, and to conﬁrm if they behave

in a similar way than overnight polysomnograms.

In this work, we present a preliminary study

of HRV indexes on several siesta ECG recordings

obtained both from apneic and normal subjects.

Spectral parameters LF, HF, LF/HF ratio and VLF

were calculated and analyzed to determine possible

variations during siesta time.

334

A. Vila X., J. Méndez A., Otero A., Rodríguez-Liñares L. and J. Lado M..

HEART RATE VARIABILITY IN SIESTA POLYSOMNOGRAMS - A Preliminary Study.

DOI: 10.5220/0003735703340337

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2012), pages 334-337

ISBN: 978-989-8425-89-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2 MATERIALS AND METHODS

2.1 Database

Seven ECG recordings selected from the

polysomnographic database of the Sleep Unit of

the University Hospital Complex of Santiago de

Compostela (CHUS) were used. This database was

obtained using a SOMNOscreen

polysomnograph,

built by SOMNOmedics GmbH.

The selected recordings correspond to siestas

taken by patients suffering different levels of apnea,

and by normal, healthy subjects. Average duration

of ECG recordings is 158±52 minutes. Subjects’

mean age is 61.85±5.78 years, average weight is

96.71±18.08 kg, average body mass index (BMI)

is 32.6±4.3 kg/m

, and mean apnea/hypopnea index

(AHI) is 35.6±38.3.

2.2 Methods

Apneic and hypoapneic episodes were detected

employing a previously developed algorithm (Otero

et al., 2011). Each recording was divided into

5-minute intervals, being each of them labeled as

normal (NOR) (suffering from respiratory airﬂow

limitation less than 10% of the interval), borderline

(BDL) (suffering from respiratory airﬂow limitation

between 10%-20% of the interval), or apneic (APN)

(suffering from respiratory airﬂow limitation more

than 20% of the interval). A total of 69 5-minute

episodes were labeled as NOR, 60 were considered

to be BDL, and 93 were classiﬁed as APN.

Afterwards, beat positions were estimated (Otero

et al., 2009) and instantaneous heart rate was

calculated. Heart rate signal was automatically

ﬁltered and manually checked to remove artifacts

or incorrect values. Then, an interpolation using a

cubic spline algorithm at 4 Hz. was applied and

HRV analysis was performed, employing the RHRV

software (Rodr

ıguez-Li

nares et al., 2011). This

software can be freely downloaded from the R-CRAN

repository (http://cran.r-project.org).

From this signal, spectral power was estimated

applying Short-Time Fourier Transform (STFT) using

a Hamming window with mean substraction. Two

different frequency analysis were performed: (1)

using window length and shifting values of 60 and

2 seconds, which provided values for LF and HF

peaks, LF/HF ratio and total power spectrum and (2)

using window length and shifting values of 300 and

10 seconds which yielded the very low frequency

components (VLF peaks). This data were processed

with two different analyses: global and 5-minute

interval analysis.

3 RESULTS

3.1 Global Analysis

For each ECG recording, the previously calculated

spectral parameters were analyzed. Table 1 shows the

results obtained for each recording.

Relationship between spectral parameters and

AHI index were also obtained and represented

in Figure 1. Besides, both correlation and R

coefﬁcients and corresponding p-values were also

calculated for the the spectral parameters vs. AHI (see

Table 2). It can be observed that HRV and VLF values

increase with AHI index. However, this effect cannot

be observed for LF and HF parameters, which present

approximately constant values with independence of

the AHI index. This is consistent with the fact

that episode duration for apneic events is around 30

seconds, and this affects heart rate signal in spectral

bands corresponding to VLF values.

Figure 1: Spectral parameters versus AHI.

One conclusion that can be drawn from Table 2,

is that, for VLF and HRV values, the association with

the AHI index is stronger than in the rest of spectral

parameters. In fact, a percentage of 85% for VLF and

87% for HRV are directly related to the AHI index.

Moreover, according to the determination coefﬁcient

, 67% of VLF values and 70% of HRV values can

be explained in terms of AHI values.

One of the main goals of this work was to test

if OSA patients and healthy subjects show different

spectral parameters in short nap ECG recordings.

To assess this point, polysomnograms were divided

into two groups attending to the AHI value: ECGs

with AHI≤20 (P20, P89, P91 and P94), and with

AHI>20 (P17, P24, and P28). Average values of the

HEART RATE VARIABILITY IN SIESTA POLYSOMNOGRAMS - A Preliminary Study

335

Table 1: Spectral parameters for the ECG recordings and mean values for each patient.

Patient AHI LF HF LF/HF VLF HRV

P17 76.4 100596±40781 149277±52534 0.77±0.44 684936±194697 540531±125789

P20 16.5 41386±26277 75209±26898 0.57±0.32 449147±263491 276512±155409

P24 43.6 101119±116014 194149±199706 0.49±0.22 328105±248998 516604±446838

P28 97.7 75712±34125 119715±62307 0.72±0.32 758674±154819 535527±162262

P89 2.8 62620±37349 111660±28171 0.57±0.34 398049±187583 303736±108416

P91 2.7 94788±52122 119965±35293 0.80±0.54 391216±209751 376232±102424

P94 9.4 45262±24613 61541±20971 0.76±0.39 427871±155148 276887±427871

≤20 58857±41096 88592±36979 0.68±0.42 419218±208201 304463±126673

>20 92743±88118 162923±153428 0.61±0.32 521760±296559 526474±338824

Table 2: Correlation and R

coefﬁcients for the spectral parameters vs. AHI.

LF HF LF/HF VLF HRV

Estimate 6.4E-04 3.8E-04 47.24 2.0E-04 2.6E-04

coefﬁcient 0.02 0.03 -0.17 0.67 0.70

p-value 0.34 0.33 0.75 0.02 0.01

Correlation coefﬁcient 0.42 0.44 0.15 0.85 0.87

spectral parameters were calculated for both groups,

and results are presented in the lower part of Table 1.

These results were also evaluated employing

a t-test that estimates 95% conﬁdence intervals

(95%CIs) and the p-value. Statistically signiﬁcant

differences were found for all spectral values, being

the p-value<2.2e-16 in all cases. This indicates the

capability of spectral analysis to discriminate between

both types of ECG recordings.

Figure 2: Comparison of episode parameters.

3.2 Five-minute Interval Analysis

As our database contains a low number of ECG

recordings, to increase the number of samples,

recordings were divided into 5-minute intervals. This

allowed to verify if HRV indexes show distinct

behaviour in apneic intervals comparing to the

baseline. Then, statistical analysis was performed,

and the results can be observed in Figure 2 and in

Table 3 for each type of episode.

Statistical analysis was performed to assess

if there were signiﬁcant differences between

NOR, BDL and APN episodes. Results yielded

p-values<0.001 for LF and HF, while VLF only

discriminates between normal and apneic episodes,

and HRV allows to distinguish between normal

intervals and the two other types. No signiﬁcant

differences were found when comparing LF/HF ratio

for the three categories of episodes.

4 DISCUSSION

Results suggest a positive correlation between VLF

and HRV indexes and AHI, while other indexes show

low correlation with AHI. Other works also show

correlation between VLF and AHI. Usual duration

of apneic episodes is about 20-40 seconds, which

corresponds to a range of 0.025-0.05 Hz. Then, a

sequence of apneic episodes modulates the heart rate

signal, affecting its spectrum, mainly in the VLF

band, since frequency range of this band matches the

range of typical apneic episodes. As the VLF band

usually carries more power than LF and HF bands,

global HRV power presents a similar correlation with

AHI. Although all HRV indexes show higher values

in apneic patients than in healthy subjects (only the

LF/HF ratio decreases), conclusions must be drawn

with caution due to the scarcity of data. Nevertheless,

our experiments gave results similar to other authors’

(Park et al., 2008; Roche et al., 2003).

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

336

Table 3: Results of the 5-minute interval analysis.

EPISODE LABEL LF HF LF/HF VLF HRV

APN 48234±25319 72448±34804 0.69±0.29 558386±342620 303962±71490

BDL 34327±22208 53066±26431 0.65±0.41 409091±319387 256597±97733

NOR 25319±14824 38920±15508 0.63±0.19 352645±177317 239728±82953

We compared HRV indexes in segments with

and without apneic episodes using a 5-minutes

interval analysis. Results show an increase in all

indexes in apneic segments. Borderline segments,

corresponding to intervals with few apneic events,

give intermediate HRV indexes (bigger than normal

intervals and lower than apneic ones). We have

not found a similar analysis in the literature, but,

if we identify borderline intervals as “mild” apnea

intervals, our results could be compared with the

ones from (Gula et al., 2003; Park et al., 2008) that

show increments in HRV indexes in “severe” apneic

patients, compared to “mild” ones.

5 CONCLUSIONS

In this paper we present a preliminary study of apneic

patients by means of HRV using polysomnograms

acquired during siesta time. Results indicate

variations in some spectral indexes when apneic

events are present, as observed in other overnight

studies. This is an interesting result because it could

allow to signiﬁcantly increase the number of patients

under observation in a sleep unit.

Although results related to the ECG siesta

recordings are promising, we must be cautious since

a more exhaustive analysis should be performed.

However, results presented in this paper suggest the

possibility of identifying apneic events in daytime

sleep, thus allowing the clinicians to use automated

systems to detect apnea in short naps.

ACKNOWLEDGEMENTS

This work has been supported by Xunta de Galicia

(PGIDIT06SIN30501PR) and the Spanish MEC and

European FEDER (TIN2009-14372-C03-03).

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