Preliminary Study of Motion Artifact Rejection for NIBP measurement in an Ambulance

Koo, Yoonseo; Kang, Ji Hee; Shin, Il Hyung; Jung, Min Yang; Suh, Gil Joon; Kim, Hee Chan

doi:10.1109/iembs.2007.4352388

Cited by 6 publications

(8 citation statements)

References 6 publications

(3 reference statements)

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“…Recently, there have been several attempts to improve the performance of the oscillometric algorithms and remove artifacts by incorporating additional sensors such as ECG, pulse oximeter, and accelerometer within the cuff [24], [59], [177]- [179]. In [59], oscillometric pulses were recorded in synchrony with the patient's ECG.…”

Section: Artifact Removal Using Auxiliary Sensorsmentioning

confidence: 99%

“…An FIR least mean squares adaptive filter that takes into account the feedback from the accelerometer was used to suppress the motion artifact in the recorded cuff deflation curve. In [24], an accelerometer was used to remove the motion artifacts from oscillometric recordings in a moving vehicle. A transfer function was found between the accelerometer output and motion artifact imposed on the recorded cuff deflation curve through several experiments.…”

Section: Artifact Removal Using Auxiliary Sensorsmentioning

confidence: 99%

“…For such patients, the recorded BP signals may be so weak or erratic that the reliability of the conventional BP estimation algorithms is heavily degraded. Fourthly, BP readings are very sensitive to the posture of body and arm, body movements, and vibrations during mobility in a vehicle or during walking [23], [24]. Therefore, reliable estimation of BP is a major challenge and is still an unsolved problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Oscillometric Blood Pressure Estimation: Past, Present, and Future

Forouzanfar

Dajani

Groza

et al. 2015

IEEE Rev. Biomed. Eng.

156

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The use of automated blood pressure (BP) monitoring is growing as it does not require much expertise and can be performed by patients several times a day at home. Oscillometry is one of the most common measurement methods used in automated BP monitors. A review of the literature shows that a large variety of oscillometric algorithms have been developed for accurate estimation of BP but these algorithms are scattered in many different publications or patents. Moreover, considering that oscillometric devices dominate the home BP monitoring market, little effort has been made to survey the underlying algorithms that are used to estimate BP. In this review, a comprehensive survey of the existing oscillometric BP estimation algorithms is presented. The survey covers a broad spectrum of algorithms including the conventional maximum amplitude and derivative oscillometry as well as the recently proposed learning algorithms, model-based algorithms, and algorithms that are based on analysis of pulse morphology and pulse transit time. The aim is to classify the diverse underlying algorithms, describe each algorithm briefly, and discuss their advantages and disadvantages. This paper will also review the artifact removal techniques in oscillometry and the current standards for the automated BP monitors.

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Section: Artifact Removal Using Auxiliary Sensorsmentioning

confidence: 99%

Section: Artifact Removal Using Auxiliary Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Oscillometric Blood Pressure Estimation: Past, Present, and Future

Forouzanfar

Dajani

Groza

et al. 2015

IEEE Rev. Biomed. Eng.

156

View full text Add to dashboard Cite

show abstract

“…Yoonseo et al have studied the rejection of motion artifacts in blood pressure measurements while patient is transported to a hospital in an ambulance [3].…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, the existing techniques use conventional filters such -Kalman filter and pulse matching algorithm -to remove the motion artifacts during the blood pressure measurement [3]. The use of such techniques, however, is not appropriate due to a significant overlap in the noise frequencies and the oscillometric signal.…”

Section: Introductionmentioning

confidence: 99%

A Ubiquitous Real-Time Motion Artifact Rejection technique for Remote NIBP Monitoring of Hypertensive Patients

2009

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Abstract-The periodic and frequent measurements of blood pressure for hypertensive patients are must to do their effective and efficient monitoring. Existing non-invasive blood pressure measurement instruments mandate a relaxed posture -it is considered a prerequisite to factor out motion artifacts in the reading -before taking the final reading. This requirement causes not only a great discomfort for the patients but also renders the equipment useless in providing ubiquitous and real-time onthe-move remote monitoring of the patients. In this paper, we propose a novel technique to get accurate measurement of the blood pressure while walking with the free end motion of the arm. The approach utilizes an accelerometer mounted on the cuff wrapped over the upper arm in order to determine the noise superimposed over the oscillometric signal of the patient's blood pressure. The system is tested on a mannequin's limb simulated to have arm movements with the oscillometric signal. The results of our analysis show that the system is robust in determining the correct blood pressure irrespective of the noise level produced due to the physical movement of the arm. We believe that our system will become a cardinal component of a cost-efficient, highly-reliable ubiquitous remote patient monitoring system.

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Signal quality measures for unsupervised blood pressure measurement

et al. 2012

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Accurate systolic and diastolic pressure estimation, using automated blood pressure measurement, is difficult to achieve when the transduced signals are contaminated with noise or interference, such as movement artifact. This study presents an algorithm for automated signal quality assessment in blood pressure measurement by determining the feasibility of accurately detecting systolic and diastolic pressures when corrupted with various levels of movement artifact. The performance of the proposed algorithm is compared to a manually annotated reference scoring (RS). Based on visual representations and audible playback of Korotkoff sounds, the creation of the RS involved two experts identifying sections of the recorded sounds and annotating sections of noise contamination. The experts determined the systolic and diastolic pressure in 100 recorded Korotkoff sound recordings, using a simultaneous electrocardiograph as a reference signal. The recorded Korotkoff sounds were acquired from 25 healthy subjects (16 men and 9 women) with a total of four measurements per subject. Two of these measurements contained purposely induced noise artifact caused by subject movement. Morphological changes in the cuff pressure signal and the width of the Korotkoff pulse were extracted features which were believed to be correlated with the noise presence in the recorded Korotkoff sounds. Verification of reliable Korotkoff pulses was also performed using extracted features from the oscillometric waveform as recorded from the inflatable cuff. The time between an identified noise section and a verified Korotkoff pulse was the key feature used to determine the validity of possible systolic and diastolic pressures in noise contaminated Korotkoff sounds. The performance of the algorithm was assessed based on the ability to: verify if a signal was contaminated with any noise; the accuracy, sensitivity and specificity of this noise classification, and the systolic and diastolic pressure differences between the result obtained from the algorithm and the RS. 90% of the actual noise contaminated signals were correctly identified, and a sample-wise accuracy, sensitivity and specificity of 97.0%, 80.61% and 98.16%, respectively, were obtained from 100 pooled signals. The mean systolic and diastolic differences were 0.37 ± 3.31 and 3.10 ± 5.46 mmHg, respectively, when the artifact detection algorithm was utilized, with the algorithm correctly determined if the signal was clean enough to attempt an estimation of systolic or diastolic pressures in 93% of blood pressure measurements.

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Preliminary Study of Motion Artifact Rejection for NIBP measurement in an Ambulance

Cited by 6 publications

References 6 publications

Oscillometric Blood Pressure Estimation: Past, Present, and Future

Oscillometric Blood Pressure Estimation: Past, Present, and Future

A Ubiquitous Real-Time Motion Artifact Rejection technique for Remote NIBP Monitoring of Hypertensive Patients

Signal quality measures for unsupervised blood pressure measurement

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