Parkinson and REM Sleep Behaviour Disorder: HRV Difference

During Polysomnography

Parisa Sattar

1,2,3 a

, Giulia Baldazzi

, Nicla Mandas

4,3

, Elisa Casaglia

, Michela Figorilli

Monica Puligheddu

and Danilo Pani

Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy

Department of Medical Sciences and Public Health, Sleep Disorder Research Center,

University of Cagliari, Cagliari, Italy

Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy

The Hardon Academy, Scuola Universitaria Superiore IUSS Pavia, Italy

nicla.mandas@iusspavia.it

Keywords: Heart Rate Variability, Parkinson Disease, REM Sleep Behavior Disorder, RBD.

Abstract: Approximately 40% to 70% of patients affected by Parkinson’s disease (PD) suffer from autonomic

dysfunction that could be related to REM sleep behavior disorder (RBD). In this work, polysomnographic

recordings were analyzed to study heart rate variability (HRV) during different sleep stages in a cohort of 20

participants, ten with Parkinson Disease with RBD (RBDpd) and ten unaffected (CG). HRV analysis was

performed by considering the first 5 min epoch from each stage (i.e., wake, N2, N3, and REM), including

time and frequency domain indexes, and entropy measures. Statistical analysis was carried out to assess any

possible significant difference between CG and RBDpd groups, but also between the wake and REM stages

in each group. Significant differences of the combined effect of RBD and PD emerged in both time and

frequency domains, but also when considering nonlinear parameters during REM and awake phases.

Accordingly, a comparison of wake and REM phase showed significant differences in all HRV parameters

for CG that was absent in the RBDpd group. Our findings reveal the potentiality of HRV as a digital biomarker

for RBDpd, by indicating distinct dysfunction of both parasympathetic and sympathetic activities in the

RBDpd group, partially in line with previous studies.

1 INTRODUCTION

Parkinson disease (PD) is one of the most common

neurodegenerative diseases that is often associated to

cardiac autonomic dysfunction. According to the

statistics, 40-70% of the PD patients experience

autonomic dysfunction (Chaudhuri, Healy, and

Schapira 2006). The type of autonomic dysfunction

can be well understood by analyzing the sympathetic

and parasympathetic activity of PD patients. Various

studies have used heart rate variability (HRV)

indexes to study the alterations in cardiovascular

autonomic system as it is a simple and non-invasive

method. Moreover, it is also one of the most

https://orcid.org/0000-0002-9461-0568

https://orcid.org/0000-0003-1275-4961

https://orcid.org/0000-0002-6837-6608

https://orcid.org/0000-0003-1924-0875

promising quantitative indicators of autonomic

balance based on cardiac rhythm (Acharya et al.

2006). HRV is a measure of the change in R-R

intervals duration and, indirectly, of the underlying

neurophysiological phenomena. Indeed, HRV is

driven by the autonomic nervous system (ANS)

activation, which reflects changes in parasympathetic

(PNS) and sympathetic nervous systems (SNS)

activities (Shaffer and Ginsberg 2017). HRV can be

evaluated using time domain, frequency domain, and

nonlinear measures.

Several studies have also employed HRV to

explore neurodegeneration, sleep and its associated

disorders such as rapid eye movement (REM) sleep

366

Sattar, P., Baldazzi, G., Mandas, N., Casaglia, E., Figorilli, M., Puligheddu, M. and Pani, D.

Parkinson and REM Sleep Behaviour Disorder: HRV Difference During Polysomnography.

DOI: 10.5220/0011838700003414

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS, pages 366-370

ISBN: 978-989-758-631-6; ISSN: 2184-4305

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

behavior disorder (RBD) or PD (Stein and Pu 2012).

HRV alterations have been investigated in PD

patients during wake stage , whereas other studies

also performed HRV analysis across combined non-

REM stages (i.e., N1, N2, and N3) versus REM

(Covassin et al. 2013). However, it is essential to

identify the alteration in HRV in different non-REM

phases because almost all PD patients experience

tremor and altered muscle tone, that impact both

REM and non-REM sleep phases. Moreover, studies

also reported that PD patients have a high probability

of developing RBD within 10 years after the

appearance of first motor signs (Jauregui-Barrutia et

al. 2010).

Interestingly, several studies on HRV revealed

that PD patients without RBD have reported

modulation in frequency components during

wakefulness compared to healthy subjects (Ke et al.

2017; Valenza et al. 2016). Moreover, HRV analysis

highlighted a stronger autonomic dysfunction

according to PD severity (Devos et al. 2003).

However, these studies only considered the impact of

PD on HRV and did not consider the impact of the

presence of RBD. On the other hand, in (Bugalho et

al. 2018; Sauvageot, Vaillant, and Diederich 2011)

considered both the impact of PD and RBD, reporting

variation in HRV without comparing it with healthy

participants.

From the aforementioned studies, it is evident that

the information about the variation in HRV due the

combined impact of PD and RBD was not completely

described. In addition, it could also be important to

analyze the SNS and PNS regulation in PD patients

with RBD (RBDpd) across each sleep phase, to

improve our knowledge about the development of

RBD and PD. Thus, in this study we aimed at

performing a preliminary evaluation of the combined

effect of PD and RBD using different HRV indexes

during sleep and wake stages.

2 METHODS

2.1 Participant

The study was approved by the Independent Ethical

Committee of the Cagliari University Hospital (AOU

Cagliari) and performed following the principles

outlined in the Helsinki Declaration of 1975, as

revised in 2000. The data from 20 participants

without cardiological disorders were taken from the

Neurology Unit of the University Hospital of

Monserrato, Cagliari, Italy. The diagnosis of RBD

was based on the criteria of the International

Classification of Sleep Disorders (ICSD-3).

Participants were divided into two groups: the

control group (CG) was composed of ten participants,

80% females (mean age: 59.4 ± 4.9), without

neurological disorders, and the affected group was

composed of ten RBDpd patients, 70% females

(mean age: 70.5 ± 9.4), without other neurological

comorbidities. PD patients ranging between 1-3 in

HY scale, and between 0-55 in UPDRS scale, were

included in this study.

2.2 Heart Rate Variability Analysis

Full night video polysomnography exam was

performed, using EEG and PSG Holter Morpheus by

Micromed (Micromed S.p.A., Italy).

Sleep RT

program (Micromed S.p.A., Italy) was used to

perform sleep staging and produce the hypnogram,

further reviewed by an expert neurologist, in

accordance with the 2013 American Academy of

Sleep Medicine guidelines (van Hout 2013). ECG

was recorded, resampled at 512 Hz to perform HRV

analysis. The hypnogram was used to extract the ECG

of wake stage (before, during and after sleep) and

different sleep phases (N2, N3 and REM). To be

consistent in the analysis across the different patients

and sleep stages, we used the first 5-min artifact-free

epoch only.

A custom implementation of a wavelet-based

ECG delineator was employed to mark R-peak

locations (Martínez et al. 2004) and to obtain the

tachogram. An automatic tachogram correction

algorithm was introduced to compensate R-peak

misdetections, comparable to the commonly used

approaches in the field (Mendez et al. 2009). As such,

all the R-R intervals exceeding 150% of the average

R-R interval were considered as associated to the

presence of one or more false negatives; this

condition was managed by correcting the tachogram

with additional R-R intervals. Conversely, those

intervals below 15% of the average R-R interval were

considered as associated to the presence of a false

positive; this condition was managed by

automatically correcting the tachogram by discarding

those extra annotations. After all, only normal-to-

normal (NN) R-R intervals were maintained.

HRV analysis was first performed by using time

domain indexes, i.e., mean NN interval (NNmean),

root mean square of the differences between adjacent

NN intervals (RMSD), percentage of adjacent NN-

interval pairs with differences greater than 50 ms

(pR50), and percentage of adjacent NN-interval pairs

with differences greater than 20 ms (pR20).

Parkinson and REM Sleep Behaviour Disorder: HRV Difference During Polysomnography

367

We also evaluated HRV frequency-domain

indexes. To this aim, cubic spline interpolation of the

NN intervals was performed to obtain a tachogram

with a proper sampling frequency of 4 Hz. The power

spectrums in very low frequency (VLF) band [0.0033

0.04] Hz, low frequency (LF) band [0.046 0.158] Hz,

and high frequency (HF) band [0.158 0.400] Hz were

computed using the Welch’s periodogram method.

We also evaluated the power ratio between LF and

HF (LF/HF) and between VLF and LF (VLF/LF),

along with the normalized HF (nHF) and LF (nLF):

Finally, two nonlinear HRV indexes, i.e.,

approximate entropy (ApEn) and sample entropy

(SE), were used to examine the irregularity of the

tachogram. Both entropies calculate irregularity of a

signal by exploiting two parameters (i.e., the signal

length, m, and the tolerance, r). ApEn could be

sensitive to the data size whereas SE is independent

from data (Delgado-Bona and Marshak 2019).

2.3 Statistical Analysis

Statistical analysis was performed by using the

pairwise, non-parametric Wilcoxon rank sum test for

independent populations. Results were considered

significant for p < 0.05. Statistical differences were

investigated between CG and RBDpd groups, by

considering each HRV parameter computed in the

different sleep phases (i.e., wake, N2, N3 and REM)

separately. Furthermore, pairwise statistical analysis

was also carried out to investigate any possible

significant discrepancy between REM and awake

stages, independently in CG and RBDpd.

3 RESULTS

3.1 HRV Analysis Across the Groups

From Figure 1, we can perform a first comparison of

time-domain HRV indexes across groups (i.e., CG vs.

RBDpd). As can be seen, pR20 and pR50 were

significantly lower in RBDpd as compared to CG

group during wake phase (p<0.001 and p<0.02,

respectively). Conversely, pR20 was significantly

higher in RBDpd during REM phase (p<0.04). There

were no significant differences in other phases or

time-domain indexes.

Figure 1: HRV indexes for CG (in grey) and RBDpd (in

white) groups in different stages. The significant result for

CG vs. RBDpd analysis is represented as (*), whereas (#)

is adopted for wake vs. REM analysis.

The results of frequency-domain indexes are also

shown in Figure 1. A significant decrease was

observed in RBDpd group on nLF and LF/HF ratio

during REM phase, compared to CG, whereas nHF

was significantly higher in RBDpd compared to CG

in the same phase (p<0.02 for all). No significant

differences during REM phase were found during the

other phases.

Finally, Figure 1 also reports the results of the

nonlinear indexes. ApEn and SE both showed a

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significant increase in RBDpd in comparison to CG,

but only during wakefulness (p<0.01 and p<0.03,

respectively).

3.2 HRV Analysis Within Groups

Figure 1 also highlights the significant differences in

HRV indexes within CG and RBDpd groups

comparing wake and REM phases . The results show

a significant decrease in all time-domain indexes in

REM phase when compared to wake phase in CG

group, except the NNmean one (p<0.001 for pR20,

pR50, and p<0.002 for RMSSD).

Similarly, significant differences were also

observed in CG for all frequency-domain indexes,

except for VLF/LF ratio. Indeed, nHF was

significantly higher in wake phase compared to REM

phase (p<0.002). However, nLF and LF/HF ratio

were significantly lower in wake phase as compared

to REM phase in the same group (p<0.002).

For nonlinear indexes, both ApEn and SE were

significantly higher in wake phase compared to REM

phase in CG group (p<0.001 for both). Conversely,

no significant results were found between the two

phases in the RBDpd group.

4 DISCUSSION

Based on our findings, HRV seems to be a reliable

digital biomarker to differentiate the PD people with

RBD from the unaffected ones. We found significant

differences of the combined effect of RBD and PD in

time-domain, frequency-domain and nonlinear

parameters between REM phase and wakefulness.

From the significant reduction of pR20 and pR50

in RBDpd group during wakefulness, we can deduce

a lower PNS activity than in the CG. These results are

in line with a previous study (Devos et al. 2003).

Moreover, during the REM phase, the pR20 is higher

in the RBDpd group, thus indicating an increased

PNS activity in contrast to CG, which was not

previously described in the scientific literature. Being

the decrease in PNS activity also associated with

stress condition, it may indicate that the wake phase

is more critical/stressful for RBDpd patients than

REM phase (Wang et al. 2018). In addition, for the

CG, most of the time-domain indexes were

significantly lower in REM phase, which implies a

reduced PNS activity compared to wakefulness. This

trend was absent in RBDpd group, thereby reflecting

possible PNS dysfunction.

Frequency-domain analysis showed that, during

REM phase, nLF decreased whereas nHF increased

in RBDpd compared to CG, thus indicating

alterations in SNS and PNS activity, respectively.

These findings are also in agreement with previous

studies (Ke et al. 2017; Valenza et al. 2016).

Interestingly, the behavior of nHF also complies with

the outcome of time-domain indexes, indicating

dominant PNS activity in RBDpd group during wake

phase, which emphasizes novel aspects of combined

influence of RBD and PD compared to CG.

Accordingly, the LF/HF ratio, which is a reliable

measure of SNS/PNS balance, it was found

considerably lower in RBDpd population compared

to CG, highlighting a disrupted PNS and SNS

response in RBDpd group.

Finally, the reduced value of nonlinear

parameters in RBDpd suggested a lack of normal

HRV during REM phase as compared to CG.

5 CONCLUSIONS

In this work, we used different HRV indexes to

analyze the effect of both PD and RBD when

compared to unaffected people, by considering wake,

non-REM, and REM phases. From our statistical

results, HRV seems to be a good digital biomarker to

differentiate between these populations, by indicating

distinct dysfunctions of PNS as well as SNS in the

affected people. However, the study also includes a

few limitations. First, the study did not consider the

disease severity, which could also impact the HRV.

Thus, conclusions cannot be generalized for all

RBDpd patients. Second, this study only focused on

the combined impact of RBD and PD. Finally, the

dataset size is limited. However, this preliminary

study proves that HRV is a potential digital biomarker

for RBDpd which can need to be further investigated

by analyzing different populations such as patients

with only PD or RBD. In future works, it would be

interesting to compare both the combined and

individual impact of RBD and PD that can assist in

early detection of phenoconversion with an increased

number of participants.

ACKNOWLEDGEMENTS

P. Sattar gratefully acknowledges PON R&I 2014-

2020, action IV.4, for her Ph.D. scholarship.

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