Smart and Sustainable Energy Systems: Enhanced Transmission of EEG Signals in Telemedicine Applications using Soft Output Viterbi Turbo Decoder

Abstract

Abstract In the era of telemedicine, where remote treatment is gaining traction, the reliable transmission of biomedical signals is paramount. Turbo Coding has emerged as a pivotal method due to its robust performance and quality of service. However, the inherent complexity of Turbo decoders presents a significant hurdle. This paper investigates the efficacy of Soft Output Viterbi Algorithm (SOVA), Logarithmic MAP (Log-MAP), and Maximum A posteriori Probability (MAP) decoding techniques within Turbo decoding, crucial for real-time telemedicine applications. Focusing specifically on EEG signal transmission, we employ wireless channels and Turbo coding to enhance reliability. Viterbi decoding is leveraged to mitigate complexity, with an in-depth analysis of the SOVA algorithm’s Bit Error Rate performance across various parameters. This research enhances telemedicine by improving the reliability of biomedical signal transmission. Through efficient decoding techniques like Soft Output Viterbi Turbo Decoder, it ensures timely and accurate healthcare delivery. By reducing the need for patient travel and optimizing energy consumption, it aligns with Smart and Sustainable Energy Systems goals. This contributes to global healthcare accessibility and sustainability by minimizing carbon footprint and resource utilization. Ultimately, it promotes efficient, dependable, and eco-friendly healthcare solutions for all.