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`A.|G. Comtois, et al. “A Comparative Evaluation of Adaptive Noise
`Cancellation Algorithms for MinimizingMotion Artifacts in a Forehead-
`Mounted Wearable Pulse Oximeter” 28" Annual International Conference
`
`of the [EEE Engineering in Medicine and Biology Society, 2007, August
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`11. G. Comtois, et al. “A Comparative Evaluation of Adaptive Noise Cancellation
`Algorithms for Minimizing Motion Artifacts in a Forehead-Mounted Wearable Pulse
`Oximeter” was published in the 28" Annual International Conference ofthe IEEE
`Engineering in Medicine and Biology Society, 2007. The 28" AnnualInternational
`Conference of the IEEE Engineering in Medicine and Biology Society, 2007 was held
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`Executed on: as¢ Yel \ 20it
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`A Comparative Evaluation of Adaptive Noise Cancellation_Retatea articles
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`Algorithmsfor Minimizing Motion Artifacts in a Forehead-—geactns A Wearable Active noise
`
`=
`Recognition
`| Reflectance
`|
`cancellation
`Mounted Wearable Pulse Oximeter Pulse Oximeter_|using MEMSof Physical
`
`
`Activities and
`for Remote...
`acceleramete..,
`
`9
`Paper
`Citations
`
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`Patent
`Citations
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`3
`Author(s)
`
`Gary Comtois; Yitzhak Mendelson;
`
`Piyush Ramuka
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`Abstract:
`
`Wearable physiological monitoring using a pulse oximeter would enable field medics to monitor multiple injuries simultaneously, thereby prioritizing
`medical intervention when resourcesarelimited. However, a primary factorlimiting the accuracy of pulse oximetry is poor signal-to-noiseratio since
`photoplethysmographic (PPG) signals, from which arterial oxygen saturation (SpO,) and heart rate (HR) measurements are derived, are compromised
`by movementartifacts. This study was undertakento quantify SpO, and HR errors induced bycertain motion artifacts utilizing accelerometry-based
`adaptive noise cancellation (ANC). Since the fingers are generally more vuinerable to motion artifacts, measurements were performed using a custom
`forehead-mounted wearable pulse oximeter developed for real-time remote physiological monitoring and triage applications. This study revealed that
`processing motion-corrupted PPG signals by least mean squares (LMS)and recursive least squares (RLS) algorithms canbeeffective to reduce SpO,
`and HR errors during jogging, but the degree of improvement dependsonfilter order. Although both algorithms produced similar improvements,
`implementing the adaptive LMS algorithm is advantageoussince it requires significantly less operations.
`
`Published in: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th AnnualInternational Conferenceof the IEEE
`
`Date of Conference: 22-26 Aug. 2007
`
`INSPEC Accession Number: 9910101
`
`Date Addedto IEEE Xp/ore: 22 October 2007
`
`DOI: 10.1109/IEMBS.2007.4352592
`
`ISBN Information:
`
`ISSN Information:
`
`PubMedID: 18002258
`
`Publisher: [EEE
`
`‘= Contents
`
`|. introduction
`The implementation of wearable diagnostic devices would enablereal-time remote physiological
`assessment and triage of military combatants, firefighters, miners, mountaineers, and other individuals
`oe
`.
`.
`7
`a
`operating in dangerous and high-risk environments. This, in turn, would allow first responders and
`front-line medics working under stressful conditions to better prioritize medical intervention when
`resourcesarelimited, thereby extending moreeffective care to casualties with the most urgent needs.
`
`Read document
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`a.
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`IEEE Xplore Document - A Comparative Evaluation of Adaptive Noise Cancellation Algorithms for Minimizing Motion Artifacts in a Forehead-Mounted...
`eerece en
`
`IEEE Keywords
`Noise cancellation, Biomedical monitoring, Pulse measurements, Heart rate, Least squares
`approximation,Injuries, Limiting, Signal to noise ratio, Fingers, Motion measurement
`
`|
`INSPEC: Controlled Indexing
`plethysmography, accelerometers, blood vessels, cardiology, least mean squares methods, oximetry,
`oxygen, patient diagnosis
`
`References
`.
`Citations
`
`Keywords
`
`Backto Top
`
`INSPEC: Non-Controlled Indexing
`recursive least square algorithm, adaptive noise cancellation algorithms, motion artifacts, wearable
`pulse oximeter, photoplethysmographic signals, arterial oxygen saturation, heart rate, least mean
`squaresalgorithm
`
`Algorithms, Artifacts, Clothing, Diagnosis, Computer-Assisted, Forehead, Humans, Monitoring,
`Ambulatory, Movement, Reproducibility of Results, Sensitivity and Specificity
`
`Authors
`
`Gary Comtois
`MemberIEEE, Graduate Student, Department of Biomedical Engineering,
`Worcester Polytechnic Institute, Worcester, MA 01609 USA.
`comtoisg@wpi.edu
`
`Yitzhak Mendelson
`MemberJEEE, Professor, Department of Biomedical Engineering, Worcester
`Polytechnic Institute, Worcester, MA 01609 USA. phone: 508-831-5103;fax:
`508-831-5541; email: yn@wpi.edu
`
`Piyush Ramuka
`Graduate Student, Department of Biomedical Engineering, Worcester
`Polytechnic Institute, Worcester, MA 01609 USA. pramuka@wpi.edu
`
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`Proceedingsof the 29th AnnualInternational
`Conference of the IEEE EMBS
`Cité Internationale, Lyon, France
`August 23-26, 2007.
`
`ThD13.1
`
`A Comparative Evaluation of Adaptive Noise Cancellation
`Algorithms for Minimizing Motion Artifacts in a Forehead-Mounted
`Wearable Pulse Oximeter
`
`Gary Comtois, Member IEEE, Yitzhak Mendelson, Member IEEE, Piyush Ramuka
`
`Abstract— Wearable physiological monitoring using a pulse
`oximeter would enable field medics to monitor multiple injuries
`simultaneously, thereby prioritizing medical intervention when
`resources are limited. However, a primary factor limiting the
`accuracy of pulse oximetry is poor signal-to-noise ratio since
`photoplethysmographic (PPG) signals,
`from which arterial
`oxygen saturation (SpO.) and heart rate (HR) measurements
`are derived, are compromised by movement artifacts. This
`study was undertaken to quantify SpO, and HRerrors induced
`by certain motion artifacts utilizing accelerometry-based
`adaptive noise cancellation (ANC). Since the fingers are
`generally more vulnerable to motion artifacts, measurements
`were performed using a custom forehead-mounted wearable
`pulse oximeter developed for real-time remote physiological
`monitoring and triage applications. This study revealed that
`processing motion-corrupted PPG signals by least mean
`squares (LMS) and recursive least squares (RLS) algorithms
`can be effective to reduce SpO, and HRerrors during jogging,
`but
`the degree of improvement depends on filter order.
`Although both algorithms produced similar improvements,
`implementing the adaptive LMS algorithm is advantageous
`sinceit requires significantly less operations.
`
`I.
`
`INTRODUCTION
`
`HE implementation of wearable diagnostic devices
`would enable real-time remote physiological assessment
`and triage of military combatants, firefighters, miners,
`mountaineers, and other individuals operating in dangerous
`and high-risk environments. This, in turn, would allow first
`responders and front-line medics working under stressful
`conditions to better prioritize medical
`intervention when
`resources are limited, thereby extending more effective care
`to casualties with the most urgent needs.
`Employing commercial off-the-shelf (COTS) solutions,
`for example finger pulse oximeters to monitor arterial blood
`oxygen saturation (SpO.) and heart rate (HR), or adhesive-
`type disposable
`electrodes
`for ECG monitoring,
`are
`
`Manuscript received April 2, 2007. This work is supported by the U.S.
`Army MRMC under Contract DAMD17-03-2-0006, The views, opinions
`and/or findings are those of the author and should not be construed as an
`official Department of the Army position, policy or decision unless so
`designated by other documentation.
`in the Department of Biomedical
`Y¥. Mendelson is a Professor
`Engineering, Worcester Polytechnic Institute, Worcester, MA 01609 USA
`(phone: 508-831-5103; fax: 508-831-5541; email: ym@wpi.edu).
`G. Comtois is a graduate student
`in the Department of Biomedical
`Engineering, Worcester Polytechnic Institute, Worcester, MA 01609 USA
`(comtoisg@wpi.edu).
`in the Department of Biomedical
`P. Ramuka is a graduate student
`Engineering, Worcester Polytechnic Institute, Worcester, MA 01609 USA
`(pramuka@wpi.edu).
`
`impractical for field applications because they limit mobility
`and can interfere with regular activity. Equally important,
`since these devices are designed for clinical settings where
`patient movements
`are
`relatively constrained, motion
`artifacts during field applications can drastically affect
`measurement accuracy while subjects remain active.
`Practically, the primary factor limiting the reliability of
`pulse oximetry is attributed to poor signal-to-noise ratio
`(SNR) due to motion artifacts. Since pholoplethysmographic
`(PPG) signals, which are used to determine SpO. and HR,
`are obscured during movements, the implementation of a
`robust pulse oximeter
`for
`field applications
`requires
`sophisticated
`noise
`rejection
`algorithms
`to
`eliminate
`erroneous readings and preventfalse alarms.
`To minimize the effects of motion artifacts in wearable
`pulse oximeters, several groups proposed various algorithms
`to accomplish adaptive noise cancellation (ANC)utilizing a
`noise reference signal obtained from an accelerometer
`(ACC)that is incorporated into the sensor to represent body
`movements [1]-[3]. These groups demonstrated promising
`feasibility for movement artifact rejection in PPG signals
`acquired from the finger. However,
`they did not present
`quantifiable data showing whether accelerometry-based
`ANC resulted in more accurate determination of SpO, and
`HR derived from PPG signals acquired from more motion-
`tolerant body locations that are more suitable for mobile
`applications.
`
`I]. BACKGROUND
`
`Generally, linear filtering with a fixed cut-off frequency is
`not effective in removing in-band noise with spectral overlap
`and temporal similarity that is common between the signal
`and artifact. Thus, we utilized ANC techniquesto filter noisy
`PPG waveforms acquired during field experiments. The
`performance of
`this
`signal processing approach was
`evaluated based on its potential
`to lower SpO. and HR
`measurementerrors.
`
`Among the most popular ANC algorithms are the least
`mean squares (LMS) and recursive least squares (RLS)
`algorithms. Briefly,
`to
`attenuate
`the
`in-band
`noise
`component in the desired signal, these algorithms assume
`that
`the reference noise received from the ACC is
`statistically correlated with the additive noise componentin
`the corrupted PPG signal, whereas the additive noise is
`uncorrelated with the noise-free PPG signal. An error signal
`is used to adjust continuously the filter’s tap-weights in
`order to minimize the SNR of the noise-corrected PPG
`signal.
`
`1-4244-0788-5/07/$20.00 ©2007 IEEE
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`The performance of ANC algorithms is highly dependerit
`on various filter parameters,
`including filter order
`(M).
`Accordingly, careful consideration must be given to the
`selection of these parameters and the trade-off between
`algorithm complexity and its computation time.
`Although the basic principles of the LMS and RLS
`techniques share certain similarities,
`the LMS algorithm
`attempts to minimize only the currenterror value, whereasin
`the RLS algorithm, the error considered is the total error
`from the beginning to the current data point. Furthermore,
`the performance of each algorithm depends on different
`parameters. For example, the step size (u) has a profound
`effect on the convergence behavior of the LMSalgorithm.
`Similarly, the forgetting factor (1) determines how the RLS
`algorithm treats past data inputs.
`Compared to the LMSalgorithm, the RLS algorithm has
`generally a faster convergence rate and smaller error.
`However, this advantage comes at the expense of increasing
`complexity and longer computational time which increases
`rapidly and non-linearly with filter order.
`
`III. METHODS
`
`To simulate movementartifacts, we performed a series of
`outdoor and indoor experiments that were intended to
`determine the effectiveness of using the accelerometer-based
`ANC algorithms
`in processing motion-corrupted PPG
`signals acquired by a forehead pulse oximeter. The focus of
`this study was to compare the performanceofeach algorithm
`by quantifying the improvement in SpO, and HR accuracy
`generated during typical activities that are expected to
`induce considerable motion artifacts in the field.
`Data were collected by a custom forehead-mounted pulse
`oximeter developed in our laboratory as a platform for real-
`time remote physiological monitoring andtriage applications
`[4]-[6]. The prototype wearable system is comprised ofthree
`units: A battery-operated optical Sensor Module (SM)
`mounted on the forehead, a belt-mounted Receiver Module
`(RM) mounted on the subject’s waist, and a Personal Digital
`Assistant (PDA) carried by a remote observer. The red (R)
`and infrared (IR) PPG signals acquired by the small (0 =
`22mm) and lightweight (4.52) SM are transmitted wirelessly
`via an RF link to the RM. The data processed by the RM can
`be transmitted wirelessly over a short range to the PDA or a
`PC, giving the observer the capability to periodically or
`continuously monitor the medical condition of multiple
`subjects. The system can be programmed to alert on alarm
`conditions, such as sudden trauma, or when physiological
`values are out of their normal range. Dedicated software was
`used to filter the reflected PPG signals and compute SpO,
`and HR based on the relative amplitude and frequency
`content of the PPG signals. A triaxial MEMS-type ACC
`embedded within the SM was used to get a quantitative
`measure of physical activily. The information obtained
`through the tilt sensing property of the ACC is also used to
`determine body posture. Posture and acceleration, combined
`with physiological measurements, are valuable indicators to
`assess thestatus of an injured personin the field.
`
`collected
`and PPG data were
`accelerations
`Body
`concurrently from 7 healthy volunteers during 32 jogging
`experiments. These jogging experiments comprised 16
`treadmill,
`12
`indoor,
`and 4 outdoor exercises. Each
`experiment comprised a 1-minute free jogging at speeds
`corresponding to 3.75—6.5 mph, framed by 2-minute resting
`intervals. For validation,
`reference SpO, and HR were
`acquired concurrently from the Masimo transmission pulse
`oximeter sensor attachedto the subject’s fingertip which was
`kept in a relatively stationary position throughoutthe study.
`We chose the Masimopulse oximeter because it employs
`unique signal extraction technology (SET®) designed to
`greatly extend its utility into high motion environments. A
`Polar™ ECG monitor, attached across the subject’s chest,
`provided reference HR data.
`The ACC provided reference noise inputs to the ANC
`algorithms. The X, Y, and Z axes of the triaxial ACC were
`oriented according to the anatomical planesas illustrated in
`Fig. 1. Accelerations generated during movement depend
`upon the types ofactivity performed. Generally, during
`jogging, acceleration is greatest
`in the vertical direction,
`although the accelerations in the other
`two orthogonal
`directions are not negligible. Therefore, the noise reference
`input applied to the ANC algorithms was obtained by
`summing all
`three orthogonal
`axes of the ACC. By
`combining signals
`from ali
`three axes, measurements
`become insensitive to sensor positioning and inadvertent
`sensor misalignment that may occur during movements. To
`compensate for differences in response times, the SpO, and
`HR measurements
`acquired from each
`device were
`processed using an 8-second weighted moving average.
`
`ACCaxial
`orientations
`
`Finger
`Sensor ~~,
`
`Pulse Oximeter
`
`S Masimo
`
`Fig. 1: Experimental setup for data collection
`
`The outputs of the MEMS ACC and raw PPGsignals were
`acquired in real-lime al a rale of 80 s/s using a cuslom
`written LabVIEW® program. Data were processed. off-line
`using Matlab programming. The ANC algorithms were
`implemented in Matlab with parameters optimized for
`computational speed and measurement accuracy, The LMS
`algorithm was implemented using a constant p of 0.016. The
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`the RLS algorithm were
`for
`selected filter parameters
`X.=0.99 and an inverse correlation matrix P = 0.1. These
`filter parameters were found to be optimal in preliminary
`experiments. For comparison, data were processed by each
`algorithm using variable orderfilters.
`
`IV. RESULTS
`
`SpO, and HR data were derived from the R and IR PPG
`signals utilizing custom extraction algorithms. SpO, root
`mean squared errors (RMSE) were quantified based on the
`differences between the readings measured by the custom
`and Masimo pulse oximeters, whereas HR errors were
`defined with respect
`to the Polar HR monitor. For
`comparison, RMSE were determined by processing the PPG
`signals off-line either with or without the ANC algorithms.
`Fig. 2 shows a representative tracing of SpO, and HR
`measurements obtained from the custom pulse oximeter with
`and without ANC. Reference measurements obtained
`simultaneously from the Masimo pulse oximeter and Polar
`HR monitor during resting and outdoor jogging were also
`included for comparison.
`
`
`
`SpO,(%)
`
`HR(bpm)
`
`Sanne msatid
`
`frequency band ranging between 1.5-3.0 Hz. Further
`analysis of the data showed that in 8 out of the 32 jogging
`experiments (25%),
`the cardiac-synchronized frequencies
`and movement-induced acceleration frequencies shared a
`common band.
`The averaged errors observed from the series of 32
`experiments are summarized in Figures 3 and 4. Analysis of
`the data clearly revealed that utilizing either the LMS or
`RLSalgorithm to process the noise-corrupted PPG signals
`can improve both SpO, and HR accuracy during jogging.
`Although the degree of improvement varied, because
`different methods are employed to compute SpO. and HR
`from the PPG signal,
`these
`figures
`show that
`the
`performanceof both algorithms depends on filter order used
`to implementeach algorithm.
`20
`
`15
`
`10
`
`
`
`SpO2RMSE
`
`(°%)
` HRRMSE(%)
`Filter Order (M)
`
`8
`12
`16
`20
`24
`Filter Order (M)
`
`Fig, 3. Averaged Sp,errors for varying filter orders, Error bars indicate
`+1SD. For comparison, M = 0 represents the error obtained without ANC.
`
`
`
`
`8
`
`12
`
`16
`
`Fig. 4. Averaged HRerrors for varying filter orders. Error bars indicate
`+1SD, For comparison, M = 0 represents the error obtained without ANC.
`
`V. DISCUSSION
`
`Masimo Jogging
`
`Pulse oximeters are used routinely in many clinical
`settings where patients are at rest. Their usage in other areas
`is limited because of motion artifacts which is the primary
`5
`contributor to errors and high rates of false alarms. In order
`Tima (s)
`to design wearable cost-effective devices that are suitable for
`Fig, 2. Representative SpO, (top) and HR (bof/om) measurements obtained
`field deployment, it is important to ensure that the deviceis
`during outdoor jogging. Filter order M= 16
`robust against motion induced disturbances. PPG signals
`Spectral analysis of the data using FFT revealed that
`recorded from the forehead are generally less prone to
`during jogging frequency components associated with body
`movement artifacts compared to PPG signals recorded from
`acceleration and the subject’s HR sharedarelatively small
`
`io
`
`2
`
`3
`
`4
`
`6
`
`7
`
`Resting
`9
`
`8
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`a finger. Nonetheless, morphological distortions of the
`underlying PPG waveforms,
`from which SpO, and HR
`measurements are derived, could lead to measurement
`errors, false alarms, and frequent dropouts when subjects
`remain active. For example, as shown in Fig. 2,it is evident
`that the Masimo pulse oximeter, which employs advanced
`signal extraction technology designed to greatly extend its
`utility into high motion environments, was clearly unable to
`accurately track SpO, and HR while the subject was jogging.
`Although to a
`lesser
`extent, we
`also noticed more
`pronounced fluctuations in SpO. recorded by the wearable
`forehead pulse oximeter during jogging. These fluctuations
`are likely caused by PPG waveforms obscured by motion
`artifacts associated with heavier breathing.
`To address the need to improve the performance of a
`prototype reflectance pulse oximeter during jogging, we
`investigated the effectiveness of a MEMS ACC as a noise
`reference input to two popular ANC algorithms. We chose
`the LMSand RLSadaptive routines since other investigators
`showed the promising utility ofthese algorithms to reduce
`errors attributed to motion artifacts in pulse oximeters [1]-
`[3].
`Analysis of the data acquired during jogging experiments
`showed that ANC implemented using the LMS and RLS
`algorithms can help to improve considerably the accuracy of
`a pulse oximeter, as shown in Fig. 2. However, although the
`differences are not considered clinically significant, we
`found that processing the corrupted PPG signals by each
`algorithm produced slightly different improvements. These
`differences are anticipated since different computational
`principles are employed by a pulse oximeter.
`Since ANC-based filtering implements an adaptive notch
`filter with a notch frequency corresponding to the dominant
`frequency of the measured ACC signal, we expected that an
`overlap of the HR and movement-induced ACC frequencies
`would attenuate
`the
`fundamental
`cardiac-synchronized
`frequency of the PPG signals and,
`therefore significantly
`affecting SpO. and HR measurements. However, separate
`analysis of
`the data
`from experiments where body
`accelerations
`and cardiac rhythms were
`found to be
`synchronized confirmed that applying either the LMS or
`RLS algorithm did not adversely impact the ability to obtain
`accurate SpO, and HR readings while subjects remain
`active.
`Asshown in Fig. 3 and Fig. 4, we found that the degree of
`improvement depends on the filter order
`(M) used to
`implement each adaptive algorithm, however filters order
`greater
`than
`24
`produced
`diminished improvements.
`Furthermore, we also found that the LMS algorithm was
`slightly more effective in reducing HR errors compared to
`the RLS implementation.
`it is important to take into
`Given similar performances,
`consideration the
`complexity of
`the LMS and RLS
`algorithms and the trade-off between algorithmic complexity
`and computation time. These principal
`tradeoffs
`are
`important since our goal is to implement ANC to improve
`the performance of a wearable pulse oximeter during
`motion. For example, compared to the LMSalgorithm, the
`RLS algorithm has a faster convergence rate which is
`
`essential in real-time applications. However, this comes at
`the expense of a longer computational time since the RLS
`algorithm requires M* operations per iteration. Considering
`for example that an implementation based on a 24"'-order
`filter would provide an acceptable error reduction,
`this
`implies that
`the LMS algorithm would require only 24
`operations compared to 576 operations that will be required
`to
`implement
`an adaptive RLS algorithm. Table
`1
`summarizes the relative execution times of the LMS and
`RLS adaptive algorithms for processing one datapoint.
`
`
`LMS(ms)
`1.0
`1.8
`3.2
`
`6.2
`
`VI. CONCLUSIONS
`
`This study was designed to investigate the performance of
`accelerometry-based ANC implemented using the LMS and
`RLSalgorithms as an effective method to minimizing both
`SpO.,
`and HR errors
`induced
`during movement.
`Measurements were performed using a custom, forehead-
`mounted wearable pulse oximeter that was developed in our
`laboratory to serve as a platform for
`real-time remote
`physiological monitoring and triage applications. The results
`obtained in this study revealed that processing motion-
`corrupted PPG signals by the LMS and RLSalgorithm can
`reduce HR and SpO,errors during jogging. Although both
`algorithms
`produced
`similar
`improvements,
`the
`implementation of the adaptive LMSalgorithm is preferred
`since it requires significantly less operations.
`
`REFERENCES
`
`{!]
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`[5]
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`1531
`
`0010
`
`FITBIT, Ex. 1033
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`FITBIT, Ex. 1033
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`