RESPIRATORY WAVEFORM ESTIMATION FROM MULTIPLE ACCELEROMETERS: AN OPTIMAL SENSOR NUMBER AND PLACEMENT ANALYSIS

Abstract

Respiratory patterns are commonly measured to monitor and diagnose cardiovascular, metabolic and sleep disorders. Electronic devices such as masks used to record respiratory waveforms usually require medical staff support and obstruct the patients breathing, causing discomfort. New techniques are being investigated to overcome such limitations. An emerging approach involves accelerometers to estimate the respiratory waveform based on chest motion. However, most existing techniques employ a single accelerometer placed on an arbitrary thorax position. The present work investigates the use and optimal placement of multiple accelerometers located on the thorax and the abdomen. The study population is composed of thirty healthy volunteers in three different postures. By means of a custom-made microcontrolled system, data are acquired from an array of ten accelerometers located on predefined positions and a pneumotachograph used as reference. The best sensor locations are identified by optimal linear reconstruction of the reference waveform from the accelerometer data in the minimum mean square error sense. The analysis shows that right-hand side locations contribute more often to optimal respiratory waveform estimates, a sound finding given that the right lung has a larger volume than the left lung. In addition, we show that the respiratory waveform can be blindly extracted from the recorded accelerometer data by means of independent component analysis. In conclusion, linear processing of multiple accelerometers in optimal positions can successfully recover respiratory information in clinical settings where the use of masks may be contraindicated.

Existing System

Wireless patch sensor for remote monitoring of heart rate, respiration, activity.

Proposed System

The present work aims to investigate the accelerometer positions that provide sufficient information for a good estimation of the breathing waveform in a heterogeneous population. To this end, a custom-made microcontrolled system was developed to sample data from an array of ten accelerometers placed on the thorax and abdomen as well as from a pneumotachograph mask signal serving as reference. The accelerometer signals are linearly combined to estimate the reference signal by minimizing the mean square reconstruction error. By analyzing all possible combinations of sensor subsets in different subjects, the most frequent locations contributing to optimal reconstructions were adopted as the best accelerometer positions for estimating the respiratory waveform. The final goal is to avoid obstructive devices in clinical settings where their use may be contraindicated. In such scenarios, no reference signal may be available. This project show that the respiratory waveform can be blindly estimated by independent component analysis from four accelerometers placed on the optimal positions found by our analysis. 

Architecture

BLOCK DIAGRAM