This paper reviews recent advances in non-invasive blood pressure monitoring and highlights the added value of a novel algorithm-based blood pressure sensor which uses machine-learning techniques to extract blood pressure values from the shape of the pulse waveform. We report results from preliminary studies on a range of patient populations and discuss the accuracy and limitations of this capacitive-based technology and its potential application in hospitals and communities.

The continuous monitoring of arterial blood pressure (BP) is vital for assessing and treating cardiovascular instability in a sick infant. Currently, invasive catheters are inserted into an artery to monitor critically-ill infants. Catheterization requires skill, is time consuming, prone to complications, and often painful. Herein, we report on the feasibility and accuracy of a non-invasive, wearable device that is easy to place and operate and continuously monitors BP without the need for external calibration. The device uses capacitive sensors to acquire pulse waveform measurements from the wrist and/or foot of preterm and term infants. Systolic, diastolic, and mean arterial pressures are inferred from the recorded pulse waveform data using algorithms trained using artificial neural network (ANN) techniques. The sensor-derived, continuous, non-invasive BP data were compared with corresponding invasive arterial line (IAL) data from 81 infants with a wide variety of pathologies to conclude that inferred BP values meet FDA-level accuracy requirements for these critically ill, yet normotensive term and preterm infants.

We have described a simple approach for the analysis and isolation of multiple periodicities from a biological time series. For the estimation of the periodicities, we used simulated data and data from ongoing experiments in our laboratory. Two time series were simulated, one which consisted of only white noise and the other consisted white noise along with periodicities of 6, 11, 17 and 23 h, to demonstrate that our method can successfully isolate multiple patterns in a time series. Our method of analysis is objective, simple, flexible and adaptive since it distinctly delineates the individual contribution from an overlap of multiple periodicities. The key features of our method are: (i) identification of a reliable phase reference point, (ii) scanning the time series using a moving window in increments, (iii) use of Siegel's modification of Fisher's method to detect significant periodicit(y)ies in the time series. The use of window sizes of increasing length to examine the time series elegantly reduces noise while identifying periodicities that are otherwise not apparent. Finally, the periodogram can be smoothed in order to normalize the contribution by attendant frequency components within the waveform. A minimum critical value for relative contribution of various frequencies was calculated to delineate the periodicities that contributed significantly to the time series. We executed this method of time series analysis using MS Excel and C.

Enteral nutrition with a nasogastric/orogastric feeding tube is essential in premature infants. These feeding tubes are placed either by clinical teams in the hospital setting or by parents at home. These tubes can be placed incorrectly or may dislodge after placement. Existing methods for confirming placement such as auscultation, testing gastric aspirates, and radiography have limitations, necessitating alternative solutions to improve safety and accuracy of placement. Here, we describe the clinical feasibility of a novel feeding tube with impedance and temperature sensors. The information received from the sensors is integrated to decipher real-time position, specifically using an airway misplacement algorithm (AMA) and gastric placement algorithm (GPA). We tested the feasibility and validity of this approach in a two-part clinical study involving premature neonates. In the Phase 1 study (n = 16), feeding tubes were placed without any real-time guidance; data from this study was then used to develop the algorithms that provided real-time guidance for placements in the Phase 2 study (n = 10). Results showed AMA and GPA detected placement (or misplacement) 100% of the time, demonstrating clinical feasibility and safety of impedance and temperature integrated feeding tubes for real-time placement guidance.