Abstract Telemedicine is venturing towards development, resulting in larger data evaluation online. Medical signals are major Para-types in telemedicine transmission channel line. Since, biomedical signals are sensitive and acute in nature, a smallest ratio of hampering causes false positive prediction on resultant diagnosis. In this paper, a machine learning algorithm is discussed to regenerate the signals under transmission. The signals are decomposed in four layer DWT prior transmission to achieve signal optimization through channel. The methodology uses Real-Time Signal Re-Generator and Validator (RTSRV) Algorithm designed on neural networking model for training and validating incoming signals. The results demonstrate a performance consistency of 1.16 over 767 EEG processed samples with 0.65sec as an average processing time for regeneration and training.
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