Abstract
This manuscript addresses the problem of non-invasive fetal Electrocardiogram (ECG) signal acquisition with low power/low complexity sensors. A sensor architecture using the Compressive Sensing (CS) paradigm is compared to a standard compression scheme using wavelets in terms of energy consumption vs. reconstruction quality, and, more importantly, vs. performance of fetal heart beat detection in the reconstructed signals. We show in this paper that a CS scheme based on reconstruction with an over-complete dictionary has similar reconstruction quality to one based on wavelet compression. We also consider, as a more important figure of merit, the accuracy of fetal beat detection after reconstruction as a function of the sensor power consumption. Experimental results with an actual implementation in a commercial device show that CS allows significant reduction of energy consumption in the sensor node, and that the detection performance is comparable to that obtained from original signals for compression ratios up to about 75%.
Highlights
Wearable sensors are a viable and possible solution for continuous monitoring of physiological signals, such as the Electrocardiogram (ECG), during patient’s everyday activities
We show that a Compressive Sensing (CS) scheme based on reconstruction with an over-complete dictionary, instead of the wavelet basis considered in [8], has similar reconstruction quality to one based on wavelet compression, proving that the CS paradigm is suitable for fetal ECG (fECG) acquisition, with the advantage of a low power implementation in the sensor
Our results show that the properly designed classical CS paradigm, using an over-complete Gaussian Dictionary at the receiver, can preserve relevant signal information and provide a detection performance comparable to that obtained from original signals for compression ratios up to about 75%
Summary
Wearable sensors are a viable and possible solution for continuous monitoring of physiological signals, such as the Electrocardiogram (ECG), during patient’s everyday activities. Due to the fact that the sensors are often battery operated and have limited computational capabilities, there is increasing interest in the development of low-complexity solutions to acquire, compress and transmit the data, in order to reduce power consumption at the sensor. Wearable sensors have been employed for non-invasive abdominal recordings of fetal ECG (fECG) [1,2]. This possibility is useful for remote monitoring of fetus health during the whole pregnancy. It is clear that, for this application, an efficient sensor design and low-complexity compression algorithms in the sensor become important
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