Fetal ECG Signal Optimization on Signal Obtained From FECG Sensor for Remote Areas with Lower Signal Strength for Its Smooth Propagation to Medical Databases

Wearable ECG sensors are growing in their popularity day by day. These ECG sensors run on batteries, hence carry a limited battery life. The ECG sensors are also used to monitor the fetal health, which are called FECG sensors. These sensors are used with the pregnant women for the fetal ECG monitoring to avoid the critical health hazards. In this research, the signal optimized for its easy transfer to the central server over the cellular connections. In this research, we are trying to increase the battery life of the FECG sensor by optimizing the signal just after it is recorded which saves the battery life by smooth signal transmission and by lowering the load on buffer memory. Wearable sensors are used as FECG data collection and transmission units. But handling data transmission process on FECG sensors consumes a handful amount of energy. In this research, the FECG signal is optimized using genetic algorithm to reduce its size with no loss of quality and detail. This reduces the load on data transmission process. Hence, it improves the performance of the wearable FECG signals. The FECG signal optimization is done using Fitness function based Genetic Algorithm because Fitness function based genetic algorithms is an effective and robust optimization technique. Using this technique, the variety of FECG signal can be optimized using the quantization scheme of fitness function based ECG data compression based on a genetic algorithm. The compression performance and convergence speed of reconstruction quality maintenance is evaluated by using the collected FECG dataset for the research project.