Research >> Biomedical Signal Acquisition:

In wireless health tele-monitoring, several health indicating signals like EEG, ECG, MEG etc. are constantly acquired from the patient and transmitted to a remote medical facility for analysis. Such a system is usually called a Wireless Body Area Network (WBAN). It has several advantages:

  • The patient can live at the comfort of his/her own place without the intrusion of an external paramedic. Service of a paramedic is expensive and the patient might consider the presence as an intrusion to privacy and security.
  • In countries like India, where medical facilities at remote rural locations are limited, such WBAN's can help in monitoring the health of the populace.

The main challenge for a WBAN is to preserve its power so that is lasts longer on a single charge. There are three main power sinks - sensing, processing and transmission. Assuming that we are to monitor EEG signals, a traditional EEG unit looks like:

The signal is sampled and the sampled signal is transmitted. Signal transmission consumes a lot of power, so it needs to be reduced. To this end, Compressed Sensing (CS) based techniques project the signal onto a lower dimension - this is performed by a DSP chip. The compressed signal is finally transmitted. Since the signal is compressed, the transmission power consumption is reduced.

However, such an architecture increases the energy requirement owing to the addition of the multiplier DSP chip.

We ask the question - can we not do away with the DSP chip? can we not reduce the sensing energy as well? Our research answers both in the affirmative. There is a simple answer to both the questions - randomly under-sample the signal.

This reduces the sensing cost as well as the processing cost; we do not require the multiplier any more - we compress right when we sample! This is the schematic design

The challenge is in reconstructing the signals. Piecemeal reconstruction of signals is almost impossible in this scenario. We reconstruct the signal ensemble by accounting for inter-channel correlations. Our proposed techniques perform at par with state-of-the-art CS based methods but requires only half their energy. We can make the sensor nodes of the WBAN last about two times longer.