Reliable ECG Signals Based on GMM for Body Area Network

A distinctive and natural trust region for protected data transportations in wireless healthcare systems such as body area networks is provided by the blood flow system in a humanoid body. Regrettably, biometric signal confirmation by biological characteristics in wireless healthcare has not been broadly studied. A data authentication method using electrocardiography (ECG) signal shapes for reducing key exchange overhead is proposed in this paper. Applying stochastic pattern recognition techniques in wireless healthcare is the key impact of this study. The inter-pulse interval (IPI) signal shape at source side is summarized as a biometric authentication key using Gaussian mixture model (GMM), in the suggested method. At the destination point, a light-weight signature authentication scheme is implemented that uses IPI signals gathered locally at the receiver. The suggested authentication scheme has the benefit of high sample misalignment acceptance. The proposed authentication approach achieves a low half total error rate in ECG signals verification.