Key result
The RTSRV machine learning algorithm achieved a performance consistency of 1.16 over 767 EEG processed samples with an average processing time of 0.65 seconds for signal regeneration.
Why the study?
Biomedical signals are sensitive, and small transmission distortions can lead to false-positive diagnostic predictions in telemedicine.
The RTSRV machine learning algorithm demonstrates efficient signal regeneration and training for EEG samples in telemedicine environments.
May support faster EEG signal regeneration in telemedicine; leaves open prospective clinical validation.
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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Ahmed et al. (2019) studied this question. Real-Time Signal Re-Generator and Validator (RTSRV) Algorithm was evaluated on Performance consistency and average processing time. The RTSRV machine learning algorithm achieved a performance consistency of 1.16 over 767 EEG processed samples with an average processing time of 0.65 seconds for signal regeneration.
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