Novel Considerations in Reducing Noise during FECG Signal Transmission

The recent advancements in the medical field have urged towards the devastating need in remote diagnosis of already recorded Fetal electrocardiogram medical data that are ought to be transmitted along a communication medium. The data’s transmitted during such instances gets affected either by intrusion of noise sources while physically examining the patient using medical instruments or by the malicious chip intrusion for tapping the data. In addition the system architecture itself poses several challenges that affect the throughput and quality of the data at receiving end leading to misinterpretations. Hence the major motive of this paper is to analyse the Lizard Learning classifier along with its novel hybrid model followed by digital IIR filter to remove the traces of noise during the inspection stage using medical instruments and design a robust transceiver section that does not gets easily misled by third party intruders with peculiar and particular choice of VLSI components. This not only serves to be efficient in terms of throughput and quality of acquired data but also reduces area and power consumption. The simulations are carried out using 120 datasets collected from MIT-BIH Arrhythmia using MATLAB 2013b, Xilinx ISE 9.1 and Cadence of 180nm technology for synthesis of area, power and delay reports.