J. Cent. South Univ. (2013) 20: 2423−2431 DOI: 10.1007/s11771-013-1752-z
Automatic detection of respiratory rate from electrocardiogram, respiration induced plethysmography and 3D acceleration signals LIU Guan-zheng(刘官正)1, WU Dan(吴丹)2, MEI Zhan-yong(梅占勇)2, ZHU Qing-song(朱青松)2, WANG Lei(王磊)2, 3 1. Biomedical Engineering Program, Sun Yat-sen University, Guangzhou 510006, China; 2. Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China; 3. The Shenzhen Key Laboratory for Low-cost Healthcare, Shenzhen 518055, China; © Central South University Press and Springer-Verlag Berlin Heidelberg 2013 Abstract: Respiratory monitoring is increasingly used in clinical and healthcare practices to diagnose chronic cardio-pulmonary 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), 3D-acceleration and ECG. In-situ 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 single-lead 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 BIOPAC-MP150 system with CO2 100C module [3] are routinely used in hospital and healthcare centers, as the golden standard for respiratory monitoring. Other well-accepted 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), 3D-acceleration derived respiration rate (ADR) and ECG-derived 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 electrocardiogram (ECG) signal. KHALED and FARGES [5] used an eighth order band-pass 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 single-lead ECG recording that was the robust against noise from activities of daily living, BOYLE et al [10] compared performance among six different algorithms from band-pass 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 “One-hundred Talent” and the “Low-cost 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; E-mail:
[email protected];
[email protected]
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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 RIP-based methods have been proved to be an effective respiration monitoring method [12−14]. ZHANG et al [13] presented a RIP-based 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 3D-accelerator 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 3-D 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 movement-induced signal greatly exceeds that due to breathing, and the posture and orientation of the 3-D 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 one-lead 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].
J. Cent. South Univ. (2013) 20: 2423−2431
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 waist-worn 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 cross-sectional 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 waist-worn 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 RR-interval 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 RR-interval cannot be detected during dynamic activities, e.g, walking and running. Thus, a 0.2−0.8 Hz Butterworth band-pass 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 waist-worn 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 one-minute energy expenditure (EE), the band-pass filter was designed to obtain three respiration vectors from x, y, z coordinates of 3D
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Fig. 1 Step-by-step procedures for motion-derived respiratory rate estimation (a), step-by-step signal processing results for real-world signals: raw 3D acceleration signals (b), three vectors derived from 3D acceleration signals by using the band-pass filter based on one-minute energy expenditure (c), respiration wave derived from three vectors by using the PCA method (d) and reference reparation wave from BIOPAC-MP150 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]
A(i) [(x(i 1) x(i))2 ( y(i 1) y(i))2 (z(i 1) z(i))2 ]1/ 2 (1) The energy expenditure for abdomen part over one minute was calculated as
EE A(i)
(2)
The routine activities were classified into three types based on energy expenditure as following: If EE
