Sleep problems are one of the most common medical complaints today. Polysomnography (PSG) as the current standard for sleep analysis is expensive, intrusive and complex. Thus, finding a reliable and unobtrusive method for longer-term home use is important. Ballistocardiography (BCG) based methods have shown potential in sleep analysis recently. The usability and performance of a BCG based method in qualitative and quantitative analysis of sleep was evaluated. The method was validated in a clinical test on 20 subjects using PSG as a reference. Heart rate (HR), heart rate variability (HRV), respiratory rate (RR), respiratory rate variability (RRV), respiratory depth (Rdepth) and movement were utilized for sleep stage detection.
The BCG parameter accuracy was presented as the mean error from PSG with 95% confidence interval. The errors were -0.1 ± 4.4 beats per minute for HR, -0.9 ± 14.7 ms for high frequency (HF) HRV, -3.0 ± 29.9 ms for low frequency (LF) HRV, 0.3 ± 4.5 breaths per minute for RR and -40 ± 424 ms for RRV respectively. Correlation coefficient was 0.97 for HR, 0.67 for HF HRV, 0.71 for LF HRV, 0.54 for RR and 0.49 for RRV. HR, RRV and Rdepth were typically at an increased level in REM sleep and wakefulness and decreased in deep sleep. RRV was at its highest during wakefulness. HRV was at a decreased level in REM and wakefulness and increased in deep sleep. Movement was higher during wakefulness than in sleep

Koivisto, Tero

Meriheinä, Ulf

Nurmi, Sami

Palva, Lauri

Saaresranta, Tarja

Aaltodoc Publication Archive

ISBN 978-952-60-6842-8 (pdf) ISSN-L 1799-4896 ISSN 1799-4896 (printed) ISSN 1799-490X (pdf)  Aalto University School of Electrical Engineering Aalto University School of Electrical Engineering www.aalto.fi BUSINESS + ECONOMY  ART + DESIGN + ARCHITECTURE  SCIENCE + TECHNOLOGY  CROSSOVER  DOCTORAL DISSERTATIONS Aalto-ST 7/2016  Aalto University School of Electrical Engineering Validation of an Accelerometer Based BCG Method for Sleep Analysis Sami Nurmi, Tarja Saaresranta, Tero Koivisto, Ulf Meriheinä, Lauri Palva TECHNICAL REPORT SCIENCE + TECHNOLOGY ???????????????????????????????????????????????????????????????????????? ?? ????? ???????????? ????????? ?????? ?? ???? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????Aalto University publication series SCIENCE + TECHNOLOGY 7/2016  © Sami Nurmi  ISBN 978-952-60-6842-8 (pdf) ISSN-L 1799-4896 ISSN 1799-4896 (printed) ISSN 1799-490X (pdf) http://urn.fi/URN:ISBN:978-952-60-6842-8  Unigrafia Oy Helsinki 2016  Finland  Aalto University, University of Turku, Murata Electronics Oy  ????????????????????????????????????????????????????????????????????????????????????????? ???? ?????? ????? ???????????? ???? ???????? ??? ??? ????????????????????????????????????? ?? ????? ???????????? ????? ??? ?????? ?? ???? ????????????????????????? ?? ????? ??? ???????????????? ?????????? ?? ????? ??? ??????????????? ?????? ?????????????? ??????? ??????? ??? ?????? ?? ??????? ? ????????????????????????????????????? ???????????????????Sleep problems are one of the most common medical complaints today. Polysomnog-raphy (PSG) as the current standard for sleep analysis is expensive, intrusive andcomplex. Thus, ﬁnding a reliable and unobtrusive method for longer-term home useis important. Ballistocardiography (BCG) based methods have shown potential insleep analysis recently. The usability and performance of a BCG based methodin qualitative and quantitative analysis of sleep was evaluated. The method wasvalidated in a clinical test on 20 subjects using PSG as a reference. Heart rate(HR), heart rate variability (HRV), respiratory rate (RR), respiratory rate variabil-ity (RRV), respiratory depth (Rdepth) and movement were utilized for sleep stagedetection.The BCG parameter accuracy was presented as the mean error from PSG with 95%conﬁdence interval. The errors were -0.1 ± 4.4 beats per minute for HR, -0.9 ± 14.7ms for high frequency (HF) HRV, -3.0 ± 29.9 ms for low frequency (LF) HRV, 0.3 ±4.5 breaths per minute for RR and -40 ± 424 ms for RRV respectively. Correlationcoefﬁcient was 0.97 for HR, 0.67 for HF HRV, 0.71 for LF HRV, 0.54 for RR and 0.49for RRV. HR, RRV and Rdepth were typically at an increased level in REM sleep andwakefulness and decreased in deep sleep. RRV was at its highest during wakefulness.HRV was at a decreased level in REM and wakefulness and increased in deep sleep.Movement was higher during wakefulness than in sleep.????????????????????? ??????????????????????????????????? ????????????????? ?????????????????????????????????????????????? ????????????????????????? ??????????????????????????????????????????? ???????? ????????????????????? ???????? ???????????????? ? ???????????????????????????????????????????PrefaceThis report presents ﬁndings from the sleep study on Nocturnal SleepQuality and Quantity Analysis with Ballistocardiography. It summa-rizes the experimental part of the author’s master’s thesis on the subject.[1] The study was conducted for Aalto University School of ElectricalEngineering in collaboration with the University of Turku and MurataElectronics Oy (MFI). M.Sc. (Tech.) Sami Nurmi organized the studyand wrote the thesis and this report for Aalto University. MD, Ph.D.Tarja Saaresranta was responsible for the study and M.Sc. (Tech.) TeroKoivisto acted as the project manager from the University of Turku. M.Sc.(Tech.) Ulf Meriheinä provided the support for the project from MurataElectronics Oy and similarly D.Sc. (Tech.) Lauri Palva contributed fromAalto University School of Electrical Engineering.Espoo, 23.05.2016Sami NurmiIntroductionSleep problems are increasing in our society and they are one of the mostcommon medical complaints today. Sleep disorders can have a severeimpact on the health and quality of life if they are not treated. Sleepis an unconscious state and therefore sleep problems may easily remainunrecognised without proper analysis. [2][3]Polysomnography (PSG) is currently the primary method used in sleepanalysis. However, it is expensive, intrusive and complex. An overnightsleep study can cost well over half a thousand euros. Accurate measuringrequires laboratory environment and multiple sensors placed on the body.The monitoring and analysis is performed by highly trained techniciansand doctors and the sleep is visually analyzed from the measurementsignals. Although PSG gives a lot of information about sleep, it does notprovide a normal environment and is not suitable for continuous long-term measurements. [4]Finding new ways to measure sleep effectively has become more impor-tant as it has been proven that sleep has a large impact on the health andeveryday life. Ballistocardiography (BCG) based methods have shown in-creasing potential for sleep analysis. [5][6] BCG measures the mechanicalforces originating from the body. It is an inexpensive and unobtrusivemethod that is also viable for long-term use. It enables pre-emptive de-tection of sleep problems and effective post-monitoring of recovery. Inthis study, the usability and performance of an accelerometer based BCGmethod was evaluated for sleep analysis. [1]MeasurementsA clinical test was performed in order to validate cardiac and respira-tory parameters of Murata BCG sensor product and deﬁne the parametercharacteristics in sleep stages. PSG method was used as a reference. Thetest group consisted of 20 subjects with 17 men and 3 women. Healthy24 - 46 year old adults were included. Subjects consuming excessiveamounts of caffeine or alcohol, experiencing sleep problems, restless legssyndrome (RLS), grinding of teeth, loud snoring or using medication withCNS effects were excluded from the test. The tests included overnightmeasurements of sleep with the BCG and PSG devices in a sleep labora-tory. Each test subject was measured for a single night.Murata SCA11H BCG Sensor Node was used as the BCG measurementdevice [7]. The BCG node (83.7 mm x 40.7 mm x 17.6 mm) consists ofMurata SCA10H BCG Sensor Module, WiFi module and a power cord.The module includes Murata SCA61T accelerometer and a microproces-sor. The analog one-axis accelerometer detects the BCG signal with aresolution of 90 μg and operates with 1 kHz sampling frequency. Thedetected acceleration signal can be processed with an intelligent algo-rithm in real time. The BCG algorithm reports multiple parameters at1 Hz frequency. The output parameters in BCG mode are timestamp,heart rate (HR), respiratory rate (RR), relative cardiac stroke volume(SV), heart rate variability (HRV), signal strength, bed occupancy statusand beat-to-beat intervals (b2b). The BCG node was attached under thebed mattress with another node as a backup, as seen in the section a) ofFigure 1.Figure 1. Left: a) BCG equipment setup. Right: b) PSG equipment setup. [1]The PSG equipment provided measurement signals from a total of 18channels. Six electrodes were used for the measurement of brain activitywith electroencephalography (EEG), two electrodes for the measurementof eye movements with electro-oculography (EOG) and two electrodes forthe measurement of mandibular muscle tension with electromyography(EMG). Three electrodes were used to measure cardiac function with elec-trocardiography (ECG). Thorax and abdomen belts were added to mea-sure respiratory effort. Other devices included in the standard PSG setup,although not utilized in the analysis, were a TCM4 radiometer for tran-scutaneous CO2 level detection, a ﬁnger-probe pulse oximeter to measurearterial oxygen saturation, and nasal prongs attached to a pressure sen-sor for nasal air ﬂow and end-tidal CO2 measurements. All devices werelinked to a connector unit. A complete PSG equipment setup on a testsubject can be seen in the section b) of Figure 1.HR and HRV were calculated from the ECG signal. HRV at around 0.15- 0.4 Hz was deﬁned as the high frequency (HF) component and HRV ataround 0.05 - 0.15 Hz as the low frequency (LF) component. RR and RRVwere calculated from the thorax belt signal. Respiration depth (Rdepth)was related to the amplitude difference between expiration and inhalationpeaks in the signal. Relative HRV was calculated according to Equation1.HRV =HFHRVLFHRV ∗Rdepth (1)Rdepth was added in to ﬁlter out the HRV modulation caused by respi-ration.The same parameters were calculated from the BCG recording. SVmodulation was used to calculate the Rdepth. The parameters were syn-chronized by timestamp and low-pass ﬁltered to be comparable to therespective BCG recordings.Sleep stages were visually scored from the EEG, EOG and EMG record-ings in 30 second epochs by a skilled technician. The American Academyof Sleep Medicine (AASM) criteria were used in the scoring [8]. Thesleep stages were divided into light sleep, deep sleep, REM sleep andwakefulness periods. Typical levels of the BCG output parameters werecalculated in each sleep stage. All 20 tests were individually processed ina similar way, and the data was compiled together for result analysis. [1]ResultsThe RR, RRV, Rdepth, HR, HF HRV and LF HRV parameters, that werecalculated from BCG and PSG recordings, were compared together forvalidation. Rdepth could not be detected accurately with the thorax beltdue to the amplitude variations caused by test subject posture and move-ments. The mean error (μ), standard deviation (2σ) of the error andcorrelation (r) were calculated for the comparison of each parameter. Thestandard deviation was multiplied by two to deﬁne the maximum dif-ference of the parameter values with 95 % conﬁdence interval (CI). Theresults are presented in Table 1.HR and HRV correlation to the reference were good (r > 0.7). Therespiratory measurements with both, the PSG and BCG, methods wereless accurate than the cardiac measurements. Due to this, RR and RRVcorrelation to the reference was slightly lower. However, the differenceTable 1. BCG parameter validation results for HR, HF HRV, LF HRV, RR and RRV. [1]Mean Error (μ) 95% CI (2σ) correlation (r)HR (bpm) - 0.1 ± 4.4 0.97HF HRV (ms) - 0.9 ± 14.7 0.67LF HRV (ms) - 3.0 ± 29.9 0.71RR (bpm) 0.3 ± 4.5 0.54RRV (ms) - 40 ± 424 0.49between BCG and PSG measurements was not signiﬁcant for any of thecompared parameters.Typical levels of HR, HRV, RR, RRV and Rdepth in each sleep stagewere calculated. Figure 2 presents the results from a single night.Figure 2. The levels of HR, HRV, RR, RRV, Rdepth and movement are graphicallypresented for one night. The sleep stages are indicated by colors. [1]The typical parameter levels in the sleep stages are summarized inTable 2.It can be seen that HR was typically at a high level during wakefulnessand REM sleep compared to deep and light sleep. HRV was typicallyat the highest level during deep sleep and at the lowest in REM andwakefulness. There was no clear difference in RR between the sleepstages. RRV was typically at the lowest level during deep sleep andincreased in REM and wakefulness. It was clearly higher in wakefulnesscompared to REM sleep. Rdepth was typically at the lowest level duringdeep sleep and at the highest level during REM and wakefulness. Thedetected movement from BCG was also added in the evaluation. ThereTable 2. Typical HR, HRV, RR, RRV and Rdepth in each sleep stage. The levels have beencategorized from the lowest to the highest as low, decreased, medium, increasedand high. [1]Light Sleep Deep Sleep REM Sleep WakefulnessHR Decreased Decreased Increased IncreasedHRV Medium Increased Decreased DecreasedRR Medium Medium Medium MediumRRV Medium Low Increased HighRdepth Medium Decreased Increased Increasedwas signiﬁcantly more movement during wakefulness than in sleep. [1]ConclusionsThe currently prevalent method for sleep analysis is PSG. However, itis too expensive, intrusive and complex for common use. PSG is mainlyused for single night measurements in a laboratory environment. Anaccelerometer based BCG method for sleep analysis was evaluated in thiswork. BCG provides an inexpensive and unobtrusive way to measuresleep over multiple nights in home environment. Sleep can be measuredwith a single BCG device without attaching electrodes on the body orrequiring skilled technicians for operation and maintenance. The ﬁrstobjective of this study was to validate the BCG ouput parameters onhealthy subjects using PSG as a reference. The second objective was totest the BCG method for sleep analysis and evaluate the parameters insleep stages.A clinical test was prepared and performed on 20 healthy subjects in asleep laboratory. Sleep was recorded overnight using Murata BCG SensorNode and a PSG system. According to the results, the HR, HF HRV andLF HRV parameters measured with BCG were accurate and correlatedwell with the reference. RRV and Rdepth correlation to the referencewas weaker but there was no signiﬁcant difference between the BCG andPSG values. Rdepth was left out of validation due to inaccurate PSGmeasurement. The BCG parameters were reliable enough to be used forsleep analysis.Typical HR, HRV, RR, RRV and Rdepth levels were calculated in foursleep stages: light sleep, deep sleep, REM sleep and wakefulness. HRwas typically increased in REM sleep and wakefulness. HRV was typi-cally decreased in REM and wakefulness and clearly increased in deepsleep. RRV was typically increased in REM and wakefulness. It was atthe highest level in wakefulness and at the lowest level in deep sleep.Rdepth was typically decreased in deep sleep and increased in REM andwakefulness. Increased movement was detected in wakefulness comparedto the sleep period. Movement and RRV could be used to distinguishREM sleep and wakefulness from each other. These parameters providedenough information to identify the sleep stages and differentiate betweensleep and wakefulness.Further studies should expand clinical testing of the Murata BCG sen-sor product. More diverse and extensive test groups could be selected.Increasing the amount of test subjects would give more credibility on theresults. Future tests could include children, elderly people, and personswith detected sleep problems. These studies should focus on detectinginsomnia, arrhythmias or disorders related to the respiratory system.References[1] S. Nurmi, “Nocturnal sleep quality and quantity analysis with ballistocar-diography,” Master’s thesis, School of Electrical Engineering, Espoo, Finland,2016.[2] M. Partinen and C. Hublin, “Chapter 61: Epidemiology of sleep disorders,”Principles and Practice of Sleep Medicine, pp. 694 – 715, 2011.[3] Sleep problem statistics in the US. Accessed: 01.05.2016. [Online].Available: http://www.statisticbrain.com/sleeping-disorder-statistics/[4] J. Paalasmaa, “Monitoring sleep with force sensor measurement,” Ph.D. dis-sertation, University of Helsinki, Finland, 2014.[5] J. Alihanka, K. Vaahtoranta, and I. Saarikivi, “A new method for long-termmonitoring of the ballistocardiogram, heart rate, and respiration,” AmericanJournal of Physiology, vol. 240, no. 5, pp. 384 – 392, 1981.[6] O. Polo, “Partial upper airway obstruction during sleep. Studies with thestatic charge-sensitive bed (SCSB),” Acta Physiol Scand Suppl, vol. 606, pp.1 – 118, 1992.[7] Murata SCA11H contactless bed sensor. Accessed: 01.05.2016. [Online].Available: http://www.murata.com/products/sensor/accel/sca10h_11h[8] C. Iber, S. Ancoli-Israel, A. Chesson, and S. Quan, The AASM manual forthe scoring of sleep and associated events: rules, terminology and technicalspeciﬁcations, American Academy of Sleep Medicine, Westchester, IL, USA,2007.ISBN 978-952-60-6842-8 (pdf) ISSN-L 1799-4896 ISSN 1799-4896 (printed) ISSN 1799-490X (pdf)  Aalto University School of Electrical Engineering  www.aalto.fi BUSINESS + ECONOMY  ART + DESIGN + ARCHITECTURE  SCIENCE + TECHNOLOGY  CROSSOVER  DOCTORAL DISSERTATIONS Aalto-ST 7/2016  Aalto University School of Electrical Engineering Validation of an Accelerometer Based BCG Method for Sleep Analysis Sami Nurmi, Tarja Saaresranta, Tero Koivisto, Ulf Meriheinä, Lauri Palva TECHNICAL REPORT SCIENCE + TECHNOLOGY 

Validation of an Accelerometer Based BCG Method for Sleep Analysis

Aaltodoc

ISBN 978-952-60-6842-8 (pdf) 
ISSN-L 1799-4896 
ISSN 1799-4896 (printed) 
ISSN 1799-490X (pdf) 
 
Aalto University 
School of Electrical Engineering 
Aalto University School of Electrical Engineering 
www.aalto.fi 
BUSINESS + 
ECONOMY 
 
ART + 
DESIGN + 
ARCHITECTURE 
 
SCIENCE + 
TECHNOLOGY 
 
CROSSOVER 
 
DOCTORAL 
DISSERTATIONS 
A
a
lto
-S
T
 7
/2
0
1
6
 
 
Aalto University School of Electrical Engineering 
Validation of an 
Accelerometer Based 
BCG Method for Sleep 
Analysis 
Sami Nurmi, Tarja Saaresranta, Tero Koivisto, 
Ulf Meriheinä, Lauri Palva 
TECHNICAL REPORT SCIENCE + 
TECHNOLOGY 

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Aalto University publication series 
SCIENCE + TECHNOLOGY 7/2016 
 
© Sami Nurmi 
 
ISBN 978-952-60-6842-8 (pdf) 
ISSN-L 1799-4896 
ISSN 1799-4896 (printed) 
ISSN 1799-490X (pdf) 
http://urn.fi/URN:ISBN:978-952-60-6842-8 
 
Unigrafia Oy 
Helsinki 2016 
 
Finland 
 
Aalto University, University of Turku, Murata Electronics Oy 
 
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???????????? ??????? ??????? ??? ?????? ?? ??????? ? ?????????????????
???????????????????? ??????????
?????????
Sleep problems are one of the most common medical complaints today. Polysomnog-
raphy (PSG) as the current standard for sleep analysis is expensive, intrusive and
complex. Thus, finding a reliable and unobtrusive method for longer-term home use
is important. Ballistocardiography (BCG) based methods have shown potential in
sleep analysis recently. The usability and performance of a BCG based method
in qualitative and quantitative analysis of sleep was evaluated. The method was
validated in a clinical test on 20 subjects using PSG as a reference. Heart rate
(HR), heart rate variability (HRV), respiratory rate (RR), respiratory rate variabil-
ity (RRV), respiratory depth (Rdepth) and movement were utilized for sleep stage
detection.
The BCG parameter accuracy was presented as the mean error from PSG with 95%
confidence interval. The errors were -0.1 ± 4.4 beats per minute for HR, -0.9 ± 14.7
ms for high frequency (HF) HRV, -3.0 ± 29.9 ms for low frequency (LF) HRV, 0.3 ±
4.5 breaths per minute for RR and -40 ± 424 ms for RRV respectively. Correlation
coefficient was 0.97 for HR, 0.67 for HF HRV, 0.71 for LF HRV, 0.54 for RR and 0.49
for RRV. HR, RRV and Rdepth were typically at an increased level in REM sleep and
wakefulness and decreased in deep sleep. RRV was at its highest during wakefulness.
HRV was at a decreased level in REM and wakefulness and increased in deep sleep.
Movement was higher during wakefulness than in sleep.
????????????????????? ??????????????????????????????????? ??
??????????????? ?????????????????????????????
????????????????? ????????????????????????? ?????????????????????
?????????????????????? ???????? ????????????????????? ???????? ??????????
?????? ? ???????????????????????????????????????????

Preface
This report presents findings from the sleep study on Nocturnal Sleep
Quality and Quantity Analysis with Ballistocardiography. It summa-
rizes the experimental part of the author’s master’s thesis on the subject.
[1] The study was conducted for Aalto University School of Electrical
Engineering in collaboration with the University of Turku and Murata
Electronics Oy (MFI). M.Sc. (Tech.) Sami Nurmi organized the study
and wrote the thesis and this report for Aalto University. MD, Ph.D.
Tarja Saaresranta was responsible for the study and M.Sc. (Tech.) Tero
Koivisto acted as the project manager from the University of Turku. M.Sc.
(Tech.) Ulf Meriheinä provided the support for the project from Murata
Electronics Oy and similarly D.Sc. (Tech.) Lauri Palva contributed from
Aalto University School of Electrical Engineering.
Espoo, 23.05.2016
Sami Nurmi
Introduction
Sleep problems are increasing in our society and they are one of the most
common medical complaints today. Sleep disorders can have a severe
impact on the health and quality of life if they are not treated. Sleep
is an unconscious state and therefore sleep problems may easily remain
unrecognised without proper analysis. [2][3]
Polysomnography (PSG) is currently the primary method used in sleep
analysis. However, it is expensive, intrusive and complex. An overnight
sleep study can cost well over half a thousand euros. Accurate measuring
requires laboratory environment and multiple sensors placed on the body.
The monitoring and analysis is performed by highly trained technicians
and doctors and the sleep is visually analyzed from the measurement
signals. Although PSG gives a lot of information about sleep, it does not
provide a normal environment and is not suitable for continuous long-
term measurements. [4]
Finding new ways to measure sleep effectively has become more impor-
tant as it has been proven that sleep has a large impact on the health and
everyday life. Ballistocardiography (BCG) based methods have shown in-
creasing potential for sleep analysis. [5][6] BCG measures the mechanical
forces originating from the body. It is an inexpensive and unobtrusive
method that is also viable for long-term use. It enables pre-emptive de-
tection of sleep problems and effective post-monitoring of recovery. In
this study, the usability and performance of an accelerometer based BCG
method was evaluated for sleep analysis. [1]
Measurements
A clinical test was performed in order to validate cardiac and respira-
tory parameters of Murata BCG sensor product and define the parameter
characteristics in sleep stages. PSG method was used as a reference. The
test group consisted of 20 subjects with 17 men and 3 women. Healthy
24 - 46 year old adults were included. Subjects consuming excessive
amounts of caffeine or alcohol, experiencing sleep problems, restless legs
syndrome (RLS), grinding of teeth, loud snoring or using medication with
CNS effects were excluded from the test. The tests included overnight
measurements of sleep with the BCG and PSG devices in a sleep labora-
tory. Each test subject was measured for a single night.
Murata SCA11H BCG Sensor Node was used as the BCG measurement
device [7]. The BCG node (83.7 mm x 40.7 mm x 17.6 mm) consists of
Murata SCA10H BCG Sensor Module, WiFi module and a power cord.
The module includes Murata SCA61T accelerometer and a microproces-
sor. The analog one-axis accelerometer detects the BCG signal with a
resolution of 90 μg and operates with 1 kHz sampling frequency. The
detected acceleration signal can be processed with an intelligent algo-
rithm in real time. The BCG algorithm reports multiple parameters at
1 Hz frequency. The output parameters in BCG mode are timestamp,
heart rate (HR), respiratory rate (RR), relative cardiac stroke volume
(SV), heart rate variability (HRV), signal strength, bed occupancy status
and beat-to-beat intervals (b2b). The BCG node was attached under the
bed mattress with another node as a backup, as seen in the section a) of
Figure 1.
Figure 1. Left: a) BCG equipment setup. Right: b) PSG equipment setup. [1]
The PSG equipment provided measurement signals from a total of 18
channels. Six electrodes were used for the measurement of brain activity
with electroencephalography (EEG), two electrodes for the measurement
of eye movements with electro-oculography (EOG) and two electrodes for
the measurement of mandibular muscle tension with electromyography
(EMG). Three electrodes were used to measure cardiac function with elec-
trocardiography (ECG). Thorax and abdomen belts were added to mea-
sure respiratory effort. Other devices included in the standard PSG setup,
although not utilized in the analysis, were a TCM4 radiometer for tran-
scutaneous CO2 level detection, a finger-probe pulse oximeter to measure
arterial oxygen saturation, and nasal prongs attached to a pressure sen-
sor for nasal air flow and end-tidal CO2 measurements. All devices were
linked to a connector unit. A complete PSG equipment setup on a test
subject can be seen in the section b) of Figure 1.
HR and HRV were calculated from the ECG signal. HRV at around 0.15
- 0.4 Hz was defined as the high frequency (HF) component and HRV at
around 0.05 - 0.15 Hz as the low frequency (LF) component. RR and RRV
were calculated from the thorax belt signal. Respiration depth (Rdepth)
was related to the amplitude difference between expiration and inhalation
peaks in the signal. Relative HRV was calculated according to Equation
1.
HRV =
HFHRV
LFHRV ∗Rdepth (1)
Rdepth was added in to filter out the HRV modulation caused by respi-
ration.
The same parameters were calculated from the BCG recording. SV
modulation was used to calculate the Rdepth. The parameters were syn-
chronized by timestamp and low-pass filtered to be comparable to the
respective BCG recordings.
Sleep stages were visually scored from the EEG, EOG and EMG record-
ings in 30 second epochs by a skilled technician. The American Academy
of Sleep Medicine (AASM) criteria were used in the scoring [8]. The
sleep stages were divided into light sleep, deep sleep, REM sleep and
wakefulness periods. Typical levels of the BCG output parameters were
calculated in each sleep stage. All 20 tests were individually processed in
a similar way, and the data was compiled together for result analysis. [1]
Results
The RR, RRV, Rdepth, HR, HF HRV and LF HRV parameters, that were
calculated from BCG and PSG recordings, were compared together for
validation. Rdepth could not be detected accurately with the thorax belt
due to the amplitude variations caused by test subject posture and move-
ments. The mean error (μ), standard deviation (2σ) of the error and
correlation (r) were calculated for the comparison of each parameter. The
standard deviation was multiplied by two to define the maximum dif-
ference of the parameter values with 95 % confidence interval (CI). The
results are presented in Table 1.
HR and HRV correlation to the reference were good (r > 0.7). The
respiratory measurements with both, the PSG and BCG, methods were
less accurate than the cardiac measurements. Due to this, RR and RRV
correlation to the reference was slightly lower. However, the difference
Table 1. BCG parameter validation results for HR, HF HRV, LF HRV, RR and RRV. [1]
Mean Error (μ) 95% CI (2σ) correlation (r)
HR (bpm) - 0.1 ± 4.4 0.97
HF HRV (ms) - 0.9 ± 14.7 0.67
LF HRV (ms) - 3.0 ± 29.9 0.71
RR (bpm) 0.3 ± 4.5 0.54
RRV (ms) - 40 ± 424 0.49
between BCG and PSG measurements was not significant for any of the
compared parameters.
Typical levels of HR, HRV, RR, RRV and Rdepth in each sleep stage
were calculated. Figure 2 presents the results from a single night.
Figure 2. The levels of HR, HRV, RR, RRV, Rdepth and movement are graphically
presented for one night. The sleep stages are indicated by colors. [1]
The typical parameter levels in the sleep stages are summarized in
Table 2.
It can be seen that HR was typically at a high level during wakefulness
and REM sleep compared to deep and light sleep. HRV was typically
at the highest level during deep sleep and at the lowest in REM and
wakefulness. There was no clear difference in RR between the sleep
stages. RRV was typically at the lowest level during deep sleep and
increased in REM and wakefulness. It was clearly higher in wakefulness
compared to REM sleep. Rdepth was typically at the lowest level during
deep sleep and at the highest level during REM and wakefulness. The
detected movement from BCG was also added in the evaluation. There
Table 2. Typical HR, HRV, RR, RRV and Rdepth in each sleep stage. The levels have been
categorized from the lowest to the highest as low, decreased, medium, increased
and high. [1]
Light Sleep Deep Sleep REM Sleep Wakefulness
HR Decreased Decreased Increased Increased
HRV Medium Increased Decreased Decreased
RR Medium Medium Medium Medium
RRV Medium Low Increased High
Rdepth Medium Decreased Increased Increased
was significantly more movement during wakefulness than in sleep. [1]
Conclusions
The currently prevalent method for sleep analysis is PSG. However, it
is too expensive, intrusive and complex for common use. PSG is mainly
used for single night measurements in a laboratory environment. An
accelerometer based BCG method for sleep analysis was evaluated in this
work. BCG provides an inexpensive and unobtrusive way to measure
sleep over multiple nights in home environment. Sleep can be measured
with a single BCG device without attaching electrodes on the body or
requiring skilled technicians for operation and maintenance. The first
objective of this study was to validate the BCG ouput parameters on
healthy subjects using PSG as a reference. The second objective was to
test the BCG method for sleep analysis and evaluate the parameters in
sleep stages.
A clinical test was prepared and performed on 20 healthy subjects in a
sleep laboratory. Sleep was recorded overnight using Murata BCG Sensor
Node and a PSG system. According to the results, the HR, HF HRV and
LF HRV parameters measured with BCG were accurate and correlated
well with the reference. RRV and Rdepth correlation to the reference
was weaker but there was no significant difference between the BCG and
PSG values. Rdepth was left out of validation due to inaccurate PSG
measurement. The BCG parameters were reliable enough to be used for
sleep analysis.
Typical HR, HRV, RR, RRV and Rdepth levels were calculated in four
sleep stages: light sleep, deep sleep, REM sleep and wakefulness. HR
was typically increased in REM sleep and wakefulness. HRV was typi-
cally decreased in REM and wakefulness and clearly increased in deep
sleep. RRV was typically increased in REM and wakefulness. It was at
the highest level in wakefulness and at the lowest level in deep sleep.
Rdepth was typically decreased in deep sleep and increased in REM and
wakefulness. Increased movement was detected in wakefulness compared
to the sleep period. Movement and RRV could be used to distinguish
REM sleep and wakefulness from each other. These parameters provided
enough information to identify the sleep stages and differentiate between
sleep and wakefulness.
Further studies should expand clinical testing of the Murata BCG sen-
sor product. More diverse and extensive test groups could be selected.
Increasing the amount of test subjects would give more credibility on the
results. Future tests could include children, elderly people, and persons
with detected sleep problems. These studies should focus on detecting
insomnia, arrhythmias or disorders related to the respiratory system.
References
[1] S. Nurmi, “Nocturnal sleep quality and quantity analysis with ballistocar-
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Aalto University School of Electrical Engineering 
Validation of an 
Accelerometer Based 
BCG Method for Sleep 
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Sami Nurmi, Tarja Saaresranta, Tero Koivisto, 
Ulf Meriheinä, Lauri Palva 
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Validation of an Accelerometer Based BCG Method for Sleep Analysis

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