J. Cent. South Univ. (2013) 20: 2423−2431 DOI: 10.1007/s11771­013­1752­z

Automatic detection of respiratory rate from electrocardiogram, respiration induced plethysmography and 3D acceleration signals

LIU Guan­zheng(刘官正) 1 , WU Dan(吴丹) 2 , MEI Zhan­yong(梅占勇) 2 , ZHU Qing­song(朱青松) 2 , WANG Lei(王磊) 2, 3

1. Biomedical Engineering Program, Sun Yat­sen University, Guangzhou 510006, China; 2. Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China;

3. The Shenzhen Key Laboratory for Low­cost Healthcare, Shenzhen 518055, China;

© Central South University Press and Springer­Verlag Berlin Heidelberg 2013

Abstract: Respiratory monitoring is increasingly used in clinical and healthcare practices to diagnose chronic cardio­pulmonary functional diseases during various routine activities. Wearable medical devices have realized the possibilities of ubiquitous respiratory monitoring, however, relatively little attention is paid to accuracy and reliability. In previous study, a wearable respiration biofeedback system was designed. In this work, three kinds of signals were mixed to extract respiratory rate, i.e., respiration inductive plethysmography (RIP), 3D­acceleration and ECG. In­situ experiments with twelve subjects indicate that the method significantly improves the accuracy and reliability over a dynamic range of respiration rate. It is possible to derive respiration rate from three signals within mean absolute percentage error 4.37% of a reference gold standard. Similarly studies derive respiratory rate from single­lead ECG within mean absolute percentage error 17% of a reference gold standard.

Key words: respiration inductive plethysmography; respiratory rate; electrocardiogram; 3D acceleration; activity

1 Introduction

Noninvasive respiratory monitoring has witnessed a rapid surge of interest in recent years. The necessity for early detection and diagnosis of chronic diseases, such as sleep apnea [1] and COPD [2], has fostered the development of different methods for measuring respiratory activity, especially in ambulatory settings. In general, the capnograph devices such as BIOPAC­MP150 system with CO2 100C module [3] are routinely used in hospital and healthcare centers, as the golden standard for respiratory monitoring. Other well­accepted respiratory monitoring techniques and devices are electrical impedance tomography (EIT), thermistors for airflow measurements and piezoelectric transducers. Recently, non obtrusive respiration rate monitoring methods such as respiration inductive plethysmography (RIP), 3D­acceleration derived respiration rate (ADR) and ECG­derived respiration rate

(EDR), were increasingly demonstrated [4]. The appearance of the respiratory cycle in the heart

rate signal (respiratory sinus arrhythmia) has been known for many years [5−10]. Many techniques have been developed to derive respiration rate from electro­ cardiogram (ECG) signal. KHALED and FARGES [5] used an eighth order band­pass filter to derive respiration rate from ECG signal as early as 1992. MASON and TARASSENKO [6] used beat morphological feature to derive respiration rate. The wavelet transform has also been widely applied to derive respiration rate from ECG signal in clinic [7−9]. Furthermore, to find a measure of respiratory rate from a single­lead ECG recording that was the robust against noise from activities of daily living, BOYLE et al [10] compared performance among six different algorithms from band­pass filter and wavelet transform. In this work, ECG derived respiration rate (EDR) method was compared with respiration inductive plethysmography (RIP) and acceleration derived respiration rate (ADR) methods.

Foundation item: Project(2012M510207) supported by the China Postdoctoral Science Foundation; Projects(60932001, 61072031) supported by the National Natural Science Foundation of China; Project(2012AA02A604) supported by the National High Technology Research and Development Program of China; Project (2013ZX03005013) supported by the Next Generation Communication Technology Major Project of National Science and Technology, China; Project supported by the “One­hundred Talent” and the “Low­cost Healthcare” Programs of Chinese Academy of Sciences

Received date: 2012−05−23; Accepted date: 2012−09−18 Corresponding author: WANG Lei, Professor, PhD; Tel: +86−15818518450; E­mail: wang.lei@siat.ac.cn; liugzh3@mail.sysu.edu.cn

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RIP is a latest respiration measure method. The RIP method consists of resistive bands that change electrical properties based on the chest/abdomen wall movements during breathing [11]. The RIP­based methods have been proved to be an effective respiration monitoring method [12−14]. ZHANG et al [13] presented a RIP­based wearable respiration monitoring device, which was applied in respiration biofeedback training. In previous study [14], we have described a wearable wireless device for monitoring different respiration activities such as rhythm, breathing mode and depth during sleep, based on the digital RIP method. In this work, we aimed to estimate the robust performance of the RIP method against noise from activities of daily living.

The acceleration sensor has been also used to derive respiration rate in recent years. Accelerometers worn on the torso can measure inclination/angular changes during breathing, and then obtain respiratory rate [15−17]. HUNG et al [15] proposed a new approach based on a chest biaxial accelerometer to derive respiratory rate during static activities. BATES et al [16] designed a 3D­accelerator from wireless sensor devices to derive respiration rate, which tracked the axis of rotation and obtained regular rates of breathing motion. ANMIN et al [17] suggested that the angle and principal components analysis (PCA) methods could derive precisely respiratory rate from 3­D acceleration signals during static activities. The aforementioned methods mainly used spatial acceleration information. Therefore, when the subject was immobilizing, the respiration rate estimation inevitably deteriorated because the magnitude of the movement­induced signal greatly exceeds that due to breathing, and the posture and orientation of the 3­D accelerometer were shifting during the disturbance. In order to ignore these problems, several authors simply removed the acceleration signal episodes that were contaminated by motion artifacts [16−17]. However, in this work, a novel spectrum analysis method was presented to derive respiration rate from 3D acceleration signals.

Versatile respiration rate detection methods have aroused widely attentions in recently years. To improve the respiratory rate accuracy, DASH et al [18] compared the performance of breathing rate detection algorithms from three different physiological signals: the ECG, the photoplethysmogram (PPG) and the piezoelectric pulse transducer (PZO) signal during static activities. BOYLE et al [10] took the lead in respiratory rate obtained from one­lead ECG during various routine (static and dynamic) activities within mean absolute percentage error 17% of a reference gold standard.

A wearable device was introduced in this work to sense the ECG, the RIP and the acceleration signals [4].

In order to improve the accuracy and reliability, three signals were mixed to derive respiratory rate across all activities. Moreover, the differences in performances were analyzed among the EDR, RIP and ADR methods.

2 System and methods

The complete system was comprised of hardware modules for signal acquisition and processing algorithms for respiration rate estimation. A waist­worn device was designed to monitor RIP, ECG and 3D acceleration signals based on the previous BSN platform developed by LIU et al [4].

2.1 RIP method and EDR method RIP method measures changes in rib cage and

abdominal cross­sectional areas which are translated into lung volume [19]. It has been an important method for respiration signal monitoring. The EDR method uses various algorithms to obtain respiration rate from beat morphology, heart rate, or a combination of both [14]. In this work, the RIP and ECG signals were monitored by waist­worn device based on our general BSN platform. The RIP method estimated of respiration rate from RIP signal by using power spectrum method.

In our previous study, respiratory rate was derived from ECG signals during static activities by using two algorithms based on beat morphology and RR interval, respectively [20]. There was no significant difference between this method and other conventional yet cumbersome methods to quantify the respiration rate, considering it is more accurate for wearable devices to obtain RR­interval than R amplitude. The power spectrum analysis of the beat to beat heart rate variability was chosen to derive respiration rate during static activities (e.g, sitting, recover and specific breathing). However, the RR­interval cannot be detected during dynamic activities, e.g, walking and running. Thus, a 0.2−0.8 Hz Butterworth band­pass filter combining with power spectrum [14] was chosen to derive respiration rate from the ECG signal during dynamic activities.

2.2 Acceleration derived respiration rate (ADR) The 3D acceleration signals were monitored using

the waist­worn device during different routine activities. In general, abdomen motion includes body motion and respiration motion, and the frequency band for respiration varies from 0.1 Hz to 0.6 Hz during various body activities. Figure 1 demonstrates the procedures that respiration rate is derived from abdomen 3D acceleration signals during different routine activities. Firstly, according to one­minute energy expenditure (EE), the band­pass filter was designed to obtain three respiration vectors from x, y, z coordinates of 3D

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Fig. 1 Step­by­step procedures for motion­derived respiratory rate estimation (a), step­by­step signal processing results for real­world signals: raw 3D acceleration signals (b), three vectors derived from 3D acceleration signals by using the band­pass filter based on one­minute energy expenditure (c), respiration wave derived from three vectors by using the PCA method (d) and reference reparation wave from BIOPAC­MP150 CO2 module (e)

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acceleration signals, respectively. Then, the principal components analysis (PCA) was used to obtain the weights of every vector, and to extract the respiratory wave accurately. At last, respiration rate was computed by power spectrum analysis. 2.2.1 Energy expenditure algorithm

Acceleration signals from three channels were acquired at a sampling rate of 30 samples per second (Sps). Acceleration of a moving object consisted of two part: its own gravity and the acceleration caused by human movements, which were called static accelerations and dynamic accelerations, respectively. It is the latter that was concerned in our experiments. Therefore, a high pass filter (−3 dB bandwidth 1 Hz) was employed to eliminate the static portions. Then, the acceleration signal Ai was defined as [21]

2 / 1 2 2 2 ] )) ( ) 1 ( ( )) ( ) 1 ( ( )) ( ) 1 ( [( ) ( i z i z i y i y i x i x i A − + + − + + − + = ∆ (1)

The energy expenditure for abdomen part over one minute was calculated as

∑ = ) ( E i A E ∆ (2)

The routine activities were classified into three types based on energy expenditure as following:

If EE<100, the activity was considered to be a low EE;

If 100≤EE<400, the activity was considered to be a middle EE;

If EE≥400, the activity was considered to be a high EE; 2.2.2 Adaptive digital filter

The frequency of respiratory rate is approximately 0.1 Hz to 0.6 Hz during different routine activities. According to the different EE values of abdomen motion, the parameters of Butterworth band­pass filter were adaptively selected to derive respiration vectors from x, y and co­ordinates of 3D acceleration signals separately. The parameters of Butterworth digital filter are shown in Fig. 2, whist the pass­band ripple Rp denotes the maximum permissible pass­band loss in decibels, and the

Fig. 2 Power spectral density curves of four digital filters: (a) Parameters of first filter; (b) Parameters of second filter; (c) Parameters of third filter; (d) Parameters of fourth filter

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stop­band attenuation Rs denotes the number of decibels that the stop­band is down from the pass­band.

To derive the respiration vectors, the adaptive digital filter was chosen based on the one­minute energy expenditure (EE) as following:

1) When the energy expenditure is high, the second band­pass filter was automatically chosen to derive respiration vectors from 3­D acceleration, such as during running at 6 km/h.

2) When the energy expenditure is middle, the third band­pass filter was automatically chosen to derive respiration vectors from 3­D acceleration, such as during walking at 2 km/h.

3) When the energy expenditure is low, the fourth band­pass filter was automatically chosen to derive respiration vectors from 3­D acceleration, such as during specific breathing, sitting and recover.

The parameters of Butterworth band­pass filter denote as:

X=[frequency band, stop band, Rp, Rs] (3)

2.2.3 Principal component analysis Because of the unpredicted posture changes during

routine activities and the geometric deployment of the accelerometer, a PCA­based method was proposed to obtain the weight of the respiration vectors from the x, y, and x axes accelerations.

Principal component analysis (PCA) is generally used for dimension reduction of multivariate datasets [22].

Firstly, denoting the three co­ordinate acceleration vectors time series as matrixΣ = vx (k); vy (k); vz (k).

Secondly, the eigenvectors and corresponding eigenvalues ( 3 2 1 , , λ λ λ ) of Σ was computed based on the PCA method [22].

Thirdly, the weights for three co­ordinate acceleration vectors were obtained as

1,2,3) ( 3 2 1

= + +

= i i i λ λ λ

λ η (4)

At last, x0 represents the respiratory time sequence by

z y x x v v v 3 2 1 0 η η η + + = (5)

where the vx, vy and vz denotes the vectors from x, y and z co­ordinates of 3D acceleration filtered using adaptive band­pass filter. 2.2.4 Respiratory rate Respiratory rate was computed by using power

spectrum. The fast Fourier transform is carried out using a sliding window for 1 min; and the window length for power spectrum is 1 800 sampling points. The sliding window has a length of 60 s.

2.3 Mixed respiratory rate derived method According to the features of three respiratory rate

derived methods a mixed three signals method was taken as follows:

1) When the activity is specific breathing, respiratory rate was obtained by RIP method.

2) When the activity is sitting and recover (low EE), respiratory rate was obtained by combination of EDR method and RIP method.

3) When the activity is waking (middle EE), respiratory rate was obtained by combination of ADR method and RIP method.

4) When the activity is running (high EE), respiratory rate was obtained by ADR method.

3 Experiments

3.1 Protocol of experiment In total, twelve healthy subjects with a mean age of

25.5 (between 21 and 32) participated in the experiments, and the protocol was given in Table 1. All subjects were consented on a voluntary basis and the study was subjected to internal review. When subjects were asked to breath at the fix frequency (specific breathing), a music metronome was used to regulate his/her breaths at fixed respiratory rates (i.e., 8, 12, 16, 20, 24 breaths per minute).

Table 1 Experimental protocol

Activity Protocol Mainly motion

Golden standard

Sitting Sitting for 5 min Breathing motion

Walking for 3 min at 2 km/h

Treadmill Running for 5 min at

6 km/h

Body motion

Recover Sitting for 3 min Breathing motion

Specific breathing

Breathing for 2 min with fixed breathing rate (8, 12, 16, 20 and 24 breath per min)

Breathing motion

CO2 analysis with Biopac CO2100C module

The controlled 30 min recordings followed the prescribed protocols as shown in Fig. 3:

The reference of respiration (golden standard) was obtained by using MP150 from BIOPAC systems Inc., with CO2100C analysis module. Then, the respiration rate was computed by using power spectrum method.

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Fig. 3 Raw data for 30 min controlled respiration studies (Golden standard means reference respiratory signal by using MP150 from BIOPAC systems Inc., with CO2100C analysis module; ECG, RIP, 3­D acceleration (x, y, z) signals was obtained by using our waist­worn device

3.2 Statistical analysis The three methods (RIP, EDR, and ADR methods)

were employed and the accuracy of each method was expressed by determining the average percentage error of the measurements when compared with the reference value (golden standard), calculated by subtracting the test value from the reference value and dividing the results by the reference value of each measurement, and then took the average of this [4]. Absolute value was used to avoid error offsetting between each other.

100 ) (

O

r D × −

= V V V

E (6)

where E is the error; VD is the derived value; Vr is the reference value; VO is the observed value.

The statistical tests (with SPSS v17.0) were conducted for all the tests. Mean and standard deviations were used to evaluate the mean absolute error between the derived results and the reference values. When two datasets were compared, two sample t­tests were performed for each individual method. The significance level chosen was α=0.05.

4 Results

A complete comparison for the three methods

considered required statistical comparisons of detection accuracy and consistency for a variety of breathing conditions. At last, according to those features, a high accuracy respiratory rate was obtained with the mixed respiratory rate method.

4.1 Overall performance of all activities Figure 4 demonstrates the Bland­Altman of three

methods across all activities. The results indicate that RIP method with errors smaller than 5 breathes per minute can be used to respiration rate monitoring during all activates. Figure 5 depicts the mean absolute errors of three methods between the derived results and the reference respiration signals for all activities during 30 min controlled test. The RIP method has the least mean absolute error with the reference signal at 4.63% across all activities tested. Multiple comparison testing indicates that there is no significant difference in the reference respiration rate between the EDR and ADR methods. Thus, considering overall performance across all activities, the RIP method, which has the best robustness, is the best respiratory rate monitoring method; and the EDR and ADR methods give equivalent performance in deriving respiration rate.

4.2 Differences among routine activities The dataset was partitioned into the following

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Fig. 4 Bland­Altman plots of three methods across all activities (specific breathing, sitting, walking, running and recover): (a) Bland­Altman plot between RIP and golden standard; (b) Bland­Altman plot between EDR and golden standard; (c) Bland­Altman plot between ADR and golden standard (RIP means digital respiratory inductive plethysmography derived respiration rate; EDR means ECG­derived respiration rate; ADR means tri­acceleration derived respiration rate)

Fig. 5 Mean absolute errors of three methods during all activities (specific breathing, sitting, walking, running and recover).

activity categories: sitting, walking, running, recover and specific breathing. Figure 6 indicates the differences among three methods for each activity tested. Firstly, for the low EE activities (e.g., sitting, recover and specific breathing), the ADR method has the largest mean absolute error with the reference signal at above 10%; however, the ADR method has the least mean absolute error during dynamic activities such as walking and running. Secondly, the EDR method has the largest mean absolute error with the reference signal at the beyond 25% during dynamic activities such as walking and running, but it has the least mean absolute error with the reference signal at below 5% during sitting. Third, the RIP method has the least mean absolute error with the reference signal at below 5% during recover and specific breathing.

Therefore, the most obvious difference is that the EDR method is easily affected by various noises, especially for motion artifact during dynamic activities (e.g., walking and running); the ADR method is robust against motion artifact.

4.3 Mixed three signals respiratory rate derived method According to the differences of three respiration rate

derived methods, the mixed respiratory rate derived method makes full use of the advantages of each method. For a range of common activities of daily life tested in the study (specific breathing, sitting, walking, running and recover), it is possible to derive respiration rate within mean absolute percentage error 4.37% of a reference gold standard. By the way, it improves the respiration rate monitoring reliability.

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5 Conclusions

1) The EDR method is easily affected by motion artifact such as walking and running.

2) The ADR method has best anti­motion artifact affect ability. In addition, multi­channel respiratory rate monitoring has reliability and robustness. Thus, three signals are mixed to derive respiratory rate. The respiratory rate could be accurately and reliably derived

from the three signals across all activities by the wearable medical device.

3) However, the proposed method obviously obtains better accuracy within the mean absolute percentage error of 4.37%. The advantage of our approach is to fully utilize the characteristics of each signal derived respiratory rate during different activities, and each daily activity could be able to accurately determine by acceleration signal. At the same time, other important health information is also obtained such as heart rate, activity,

Fig. 6 Mean absolute errors of three methods during different routine activate: (a) Sitting; (b) Recover; (c) Specific breathing at fixed respiration frequencies (8, 12, 16, 20, 24 breathes per min); Walking at speed of 2 km/h (d) and running at speed of 6 km/h (e) on a treadmill)

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energy expenditure by our wearable medical device.

References

[1] YOUNES M. Role of respiratory control mechanisms in the pathogenesis of obstructive sleep disorders [J]. J Appl Physiol, 2008, 105: 1389−1405.

[2] PATEL S, MANCINELLI C, BONATO P, HEALEY J, MOY M. Using wearable sensors to monitor physical activities of patients with COPD: A comparison of classifier performance [J]. Wearable and Implantable Body Sensor Networks, 2009: 234−239.

[3] www.biopac.com [4] LIU G Z, HUANG B Y, WANG L. A wearable respiratory

biofeedback System based on generalized body sensor network [J]. Telemedicine and E­health, 2011, 17(5): 348−357.

[5] KHALED Z, FARGES G. First approach for respiratory monitoring by amplitude demodulation of the electrocardiogram [C]// Proc 14th Annu Int Conf IEEE Engineering in Medicine and Biology Society. Paris, France, 1992: 2535−2536

[6] MASON C, TARASSENKO L. Quantitative assessment of respiratory derivation algorithms [C]// Proc 14th Annu Int Conf IEEE Engineering in Medicine and Biology Society. Istanbul, Turkey, 2001: 1998−2001.

[7] ROCHE F, PICHOT V, SFORZA E, COURT­FORTUNE I, DUVERNEY D, COSTES F, GARET M, BARTHELEMY J C. Predicting sleep apnoea syndrome from heart period: A time­frequency wavelet analysis [J]. Eur Respir J, 2003, 22: 937−942.

[8] RAYMOND B, CAYTOM R, BATES R, CHAPELL M. Screening for obstructive sleep apnoea based on the electrocardiogram—The computers in cardiology challenge [C]// 27th Annual Meeting of Computers in Cardiology. Cambridge, MA, 2000: 267−270.

[9] SCHRADER M, ZYWIETZ C, von EINEM V, WIDIGER B, JOSEPH G. Detection of sleep apnea in single channel ECGs from the PhysioNet data base [C]// 27th Annual Meeting of Computers in Cardiology. Cambridge, MA, 2000: 263−266.

[10] BOYLE J, BIDARGADDI N, SARELA A, KARUNANITHI M. Automatic Detection of Respiration Rate From Ambulatory Single­Lead ECG [J]. IEEE Trans Information and Technology in Biomedicine, 2009, 13: 890−896.

[11] MACK D C, PATRIE J T, SURATT P M, FELDER R A, ALWAN M. Development and preliminary validation of heart rate and breathing rate detection using a passive, ballistocardiography­based sleep monitoring system [J]. IEEE Transactions on Information Technology in Biomedicine, 2009, 13(1): 111−120.

[12] NAM S H, YIM T C, et al. The preliminary study of unobtrusive respiratory monitoring for e­health [C]// 27th Annual International

Conference of the IEEE Engineering in Medicine and Biology Society. Shanghai, China, 2005: 3796−3798.

[13] ZHANG Z B, WANG W D, WANG B Q, WU H, LIU H Y, ZHANG Y K. A prototype of wearable respiration biofeedback platform and its preliminary evaluation on cardiovascular variability [C]// 3rd International Conference on Bioinformatics and Biomedical Engineering (ICBBE 2009). Beijing, China, 2009: 1397−1400.

[14] WU D, WANG L, ZHANG Y T. A wearable respiration monitoring system based on digital respiratory inductive plethysmography [C]// Conf 2009 IEEE Engineering in Medicine and Biology Society, 2009: 4844−4847.

[15] HUNG P D, BONNET S, GILLEMAUD R, CASTELLI E, YEN P T N. Estimation of respiratory waveform using an accelerometer [C]// 30th Annual International Conference of the IEEE Engineering­in­ Medicine­and­Biology­Society. Vancouver, Canada, 2008: 1493− 1496.

[16] BATES A, LING M J, MANN J, ARVIND D K. Respiratory rate and flow waveform estimation from tri­axial accelerometer data [C]// 2010 International Conference on Body Sensor Networks (BSN). Singapore, Singapore, 2010: 144−150.

[17] ANMIN J, BIN Y, GEERT M, HARIS D. Performance evaluation of a tri­axial accelerometry­based respiration monitoring for ambient assisted living [C]// 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBC 2009, Minneapolis, MN, USA, 2009: 5677−5680.

[18] DASH S, SHELLEY K H, SIVERMAN D G, CHON K H. Estimation of respiratory rate from ECG, photoplethysmogram, and piezoelectric pulse transducer signals: A comparative study of time­frequency methods [J]. IEEE Trans Biomedical Engineering, 2010, 57(5): .

[19] FIAMMA M N, SAMARA Z, BACONNIER P, SIMILOWSK T, STRAUS C. Respiratory inductive plethysmography to assess respiratory variability and complexity in humans [J]. Respiratory Physiology & Neurobiology, 2007, 156(2): 234−239.

[20] WU D, LIU G Z, WONG M Y M, ZHANG Y T. The accuracy of respiratory rate estimation using electrocardiography and photoplethysmography [C]// 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB 2010). Corfu, Greece, 2010: 1−4.

[21] LIN S J, WANG L, HUANG B Y, ZHANG Y T, WU X M, ZHAO J P. A pilot study on BSN­based ubiquitous energy expenditure monitoring [C]// Conference of 2009 IEEE Body Sensor Networks, 2009: 1−4.

[22] DUDA R O, HART P E, STORK D G. Pattern classification [M]. 2nd Edition. Beijing: China Machine Press and Citic Publishing House, 2003: 568−569.

(Edited by HE Yun­bin)