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 SpO 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.

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).

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.

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.

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.

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.

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.

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.

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.

Adaptive left ventricular ejection time estimation using multiple peripheral pressure waveforms.

An adaptive approach is proposed for the problem of left ventricular ejection time (LVET) estimation using peripheral pressure waveform signals. The proposed algorithm, which makes use of 2 peripheral pressure measurements, makes it possible to adaptively estimate the LVET in response to different cardiovascular physiologic states. The algorithm builds on features obtained from global and branch-specific characterization of the cardiovascular circulation as well as waveform features to dramatically improve the accuracy of LVET estimation.

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.