Adaptive template matching of photoplethysmogram pulses to detect motion artefact.

OBJECTIVE: The photoplethysmography (PPG) signal, commonly used in the healthcare settings, is easily affected by movement artefact leading to errors in the extracted heart rate and SpO2 estimates. This study aims to develop an online artefact detection system based on adaptive (dynamic) template matching, suitable for continuous PPG monitoring during daily living activities or in the intensive care units (ICUs). APPROACH: Several master templates are initially generated by applying principal component analysis to data obtained from the PhysioNet MIMIC II database. The master template is then updated with each incoming clean PPG pulse. The correlation coefficient is used to classify the PPG pulse into either good or bad quality categories. The performance of our algorithm was evaluated using data obtained from two different sources: (i) our own data collected from 19 healthy subjects using the wearable Sotera Visi Mobile system (Sotera Wireless Inc.) as they performed various movement types; and (ii) ICU data provided by the PhysioNet MIMIC II database. The developed algorithm was evaluated against a manually annotated 'gold standard' (GS). MAIN RESULTS: Our algorithm achieved an overall accuracy of 91.5% ± 2.9%, with a sensitivity of 94.1% ± 2.7% and a specificity of 89.7% ± 5.1%, when tested on our own data. When applying the algorithm to data from the PhysioNet MIMIC II database, it achieved an accuracy of 98.0%, with a sensitivity and specificity of 99.0% and 96.1%, respectively. SIGNIFICANCE: The proposed method is simple and robust against individual variations in the PPG characteristics, thus making it suitable for a diverse range of datasets. Integration of the proposed artefact detection technique into remote monitoring devices could enhance reliability of the PPG-derived physiological parameters.

Multi-parameter vital sign database to assist in alarm optimization for general care units.

Continual vital sign assessment on the general care, medical-surgical floor is expected to provide early indication of patient deterioration and increase the effectiveness of rapid response teams. However, there is concern that continual, multi-parameter vital sign monitoring will produce alarm fatigue. The objective of this study was the development of a methodology to help care teams optimize alarm settings. An on-body wireless monitoring system was used to continually assess heart rate, respiratory rate, SpO2 and noninvasive blood pressure in the general ward of ten hospitals between April 1, 2014 and January 19, 2015. These data, 94,575 h for 3430 patients are contained in a large database, accessible with cloud computing tools. Simulation scenarios assessed the total alarm rate as a function of threshold and annunciation delay (s). The total alarm rate of ten alarms/patient/day predicted from the cloud-hosted database was the same as the total alarm rate for a 10 day evaluation (1550 h for 36 patients) in an independent hospital. Plots of vital sign distributions in the cloud-hosted database were similar to other large databases published by different authors. The cloud-hosted database can be used to run simulations for various alarm thresholds and annunciation delays to predict the total alarm burden experienced by nursing staff. This methodology might, in the future, be used to help reduce alarm fatigue without sacrificing the ability to continually monitor all vital signs.

Assessment of pre-ejection period in ambulatory subjects using seismocardiogram in a wearable blood pressure monitor.

The ability to monitor arterial blood pressure continuously with unobtrusive body worn sensors may provide a unique and potentially valuable assessment of a patient's cardiovascular health. Pulse wave velocity (PWV) offers an attractive method to continuously monitoring blood pressure. However, PWV technologies based on timing measurements between the ECG and a distal PPG suffer from inaccuracies on mobile patients due to the confounding influence of pre-ejection period (PEP). In this paper, we presented a wearable, continuous blood pressure monitor (ViSi Mobile) that can measure and track changes in PEP. PEP is determined from precordial vibrations captured by an accelerometer coupled to the patient's sternum. The performance of the PEP measurements was evaluated on test subjects with postural change and patient activity. Results showed potential to improve cNIBP accuracy in active patients.

Assessing the challenges of a pulse wave velocity based blood pressure measurement in surgical patients.

Development of a continuous noninvasive blood pressure (cNIBP) monitor that is unobtrusive to patients is an attractive alternative to the cuff based measurements performed on medical-surgical floors in the hospital. Pulse wave velocity (PWV) provides a means to continuously monitor blood pressure in these patients. However, a PWV based cNIBP monitor faces a number of challenges in order to accurately measure blood pressure. In our study, we investigated some of the challenges faced by a body-worn cNIBP monitor (i.e. ViSi Mobile) on data collected on patients undergoing surgery. Results indicated that 1) pulse arrival time (PAT) values from ViSi Mobile were well correlated with PAT values obtained from an invasive reference; 2) the reciprocal of the PAT measurements were linearly correlated with blood pressure but the calibration curve was altered by administration of certain vasoactive substances; and 3) there are deterministic correlations between systolic pressure, diastolic pressure and the corresponding mean arterial pressure over a wide range of blood pressure values.

Towards the prevention of pressure ulcers with a wearable patient posture monitor based on adaptive accelerometer alignment.

Pressure ulcers are a serious problem affecting over a million patients every year. Despite accepted guidelines for assessing and repositioning high-risk patients, the prevalence of pressure ulcers continues to rise. This paper presents a wearable, wireless vital sign monitor capable of continuously measuring the duration and orientation of a patient's posture throughout the patient's stay in a hospital. Patient posture is determined using a tri-axial accelerometer attached to a patient's torso. A novel set of algorithms are used to process the accelerometer signals to adaptively identify accelerometer alignment on the patient, calculate patient spine angle, and classify patient orientation. A unique pressure ulcer risk index based on these variables is presented to assess a patient's risk for developing pressure ulcers. Experimental results from an 18 subject trial are also presented.

Utility of the photoplethysmogram in circulatory monitoring.

The photoplethysmogram is a noninvasive circulatory signal related to the pulsatile volume of blood in tissue and is displayed by many pulse oximeters and bedside monitors, along with the computed arterial oxygen saturation. The photoplethysmogram is similar in appearance to an arterial blood pressure waveform. Because the former is noninvasive and nearly ubiquitous in hospitals whereas the latter requires invasive measurement, the extraction of circulatory information from the photoplethysmogram has been a popular subject of contemporary research. The photoplethysmogram is a function of the underlying circulation, but the relation is complicated by optical, biomechanical, and physiologic covariates that affect the appearance of the photoplethysmogram. Overall, the photoplethysmogram provides a wealth of circulatory information, but its complex etiology may be a limitation in some novel applications.

Motion based adaptive calibration of pulse transit time measurements to arterial blood pressure for an autonomous, wearable blood pressure monitor.

This paper presents a novel adaptive algorithm for calibrating non-invasive pulse transit time (PTT) measurements to arterial blood pressure (BP). This new algorithm allows complete calibration of PTT to BP without the use of an oscillometric blood pressure cuff or external pressure sensor. Further, the algorithm can be used to continually update the identified parameters in the calibration equation while the patient is wearing the device. The technique utilizes natural patient motion to generate a known change in the transmural pressure (input) acting on the arteries monitored by our device to produce a measurable change in pulse transit time (output). The natural motion includes varying the height of the sensor relative to the heart to alter hydrostatic pressure at the measurement site and adjusting proximal joint posture to vary the external arterial pressure at the measurement site. This new algorithm is applied to a unique wearable sensor architecture that combines two in-line PPG sensors, one located at the ulnar artery of the wrist and one located at the digital artery of the little finger along with a multi-axis accelerometer for height measurement. Initial human subject tests results using the new algorithm and device will be presented.

Adaptive hydrostatic blood pressure calibration: development of a wearable, autonomous pulse wave velocity blood pressure monitor.

A technique for calibrating non-invasive peripheral arterial sensor signals to peripheral arterial blood pressure (BP) is proposed. The adaptive system identification method utilizes a measurable intra-arterial hydrostatic pressure change in the sensor outfitted appendage to identify the transduction dynamics relating the peripheral arterial blood pressure and the measured arterial sensor signal. The proposed algorithm allows identification of the calibration dynamics despite unknown physiologic fluctuations in arterial pressure during the calibration period under certain prescribed conditions. By employing unique wearable sensor architecture to estimate pulse wave velocity (PWV), this technique is used to calibrate peripheral pulse transit time measurements to arterial blood pressure. This sensor architecture is comprised of two inline photoplethysmograph sensors one in the form of a wristwatch measuring the pulse waveform in the ulnar artery and one in the form of a ring measuring the pulse waveform from the digital artery along the base of the little finger. Experimental results using the proposed algorithm to calibrate PTT to BP on human subjects will be presented.

Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics.

A method for estimating pulse wave velocity (PWV) using circulatory waveform signals derived from multiple photoplethysmograph (PPG) sensors is described. The method employs two wearable in-line PPG sensors placed at a known distance from one another at the ulnar and digital artery. A technique for calibrating the measured pulse wave velocity to arterial blood pressure using hydrostatic pressure variation is presented. Additionally, a framework is described for estimating local arterial dynamics using PPG waveforms and multi-channel blind system ID. Initial results implementing the method on data derived from a human subject at different arterial pressures is presented. Results show that the method is capable of measuring the changes in arterial PWV that result from fluctuations in mean arterial pressure.

Laguerre-model blind system identification: cardiovascular dynamics estimated from multiple peripheral circulatory signals.

This paper presents a method for comparing multiple circulatory waveforms measured at different locations to improve cardiovascular parameter estimation from these signals. The method identifies the distinct vascular dynamics that shape each waveform signal, and estimates the common cardiac flow input shared by them. This signal-processing algorithm uses the Laguerre function series expansion for modeling the hemodynamics of each arterial branch, and identifies unknown parameters in these models from peripheral waveforms using multichannel blind system identification. An effective technique for determining the Laguerre base pole is developed, so that the Laguerre expansion captures and quickly converges to the intrinsic arterial dynamics observed in the two circulatory signals. Furthermore, a novel deconvolution method is developed in order to stably invert the identified dynamic models for estimating the cardiac output (CO) waveform from peripheral pressure waveforms. The method is applied to experimental swine data. A mean error of less than 5% with the measured peripheral pressure waveforms has been achieved using the models and excellent agreement between the estimated CO waveforms and the gold standard measurements have been obtained.