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<p>This paper presents a high-performance wireless architecture for fetal monitoring systems, integrating Space Time Block Coding (STBC), novel Double Throughput Multiple Accumulate (DTMAC), and early Fetal Electrocardiogram (FECG) prediction unit to address challenges in data reliability, security, and processing efficiency. STBC enhances communication robustness in fading environments by leveraging spatial and temporal diversity, while LMS-based adaptive beamforming improves signal reception and enables dynamic early prediction of FECG signals. A lightweight Naïve Bayes classifier is employed to classify fetal signals with high accuracy. For secure data transmission, a low-overhead AES encryption scheme using Linear Feedback Shift Registers (LFSRs) is implemented. The DTMAC unit, designed with a Ladner-Fisher based double-precision accumulate adder, significantly accelerates multiply-accumulate operations. The architecture was modeled in Verilog HDL and synthesized using Xilinx Vivado. Evaluation against existing methods demonstrates improved performance, with lookup table (LUT) usage reduced to 543, gate count minimized to 4,692, and memory usage decreased to 164,422&#xa0;KB. Power consumption was lowered to 156.70 mW, with a 22.4% reduction in latency and an 18.6% increase in throughput. Validation was conducted using real-time FECG datasets from MIT PhysioNet. These results highlight the proposed system's efficiency in delivering secure, accurate, and real-time wireless fetal monitoring, making it a promising solution for next-generation healthcare applications.</p>

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Advanced wireless fetal monitoring system using STBC, early FECG prediction, and high-efficiency encryption hardware

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • S. Navaneethan,
  • Arti Ranjan,
  • M. Amutha,
  • Usha Bala Varanasi,
  • C. R. Bharathi

摘要

This paper presents a high-performance wireless architecture for fetal monitoring systems, integrating Space Time Block Coding (STBC), novel Double Throughput Multiple Accumulate (DTMAC), and early Fetal Electrocardiogram (FECG) prediction unit to address challenges in data reliability, security, and processing efficiency. STBC enhances communication robustness in fading environments by leveraging spatial and temporal diversity, while LMS-based adaptive beamforming improves signal reception and enables dynamic early prediction of FECG signals. A lightweight Naïve Bayes classifier is employed to classify fetal signals with high accuracy. For secure data transmission, a low-overhead AES encryption scheme using Linear Feedback Shift Registers (LFSRs) is implemented. The DTMAC unit, designed with a Ladner-Fisher based double-precision accumulate adder, significantly accelerates multiply-accumulate operations. The architecture was modeled in Verilog HDL and synthesized using Xilinx Vivado. Evaluation against existing methods demonstrates improved performance, with lookup table (LUT) usage reduced to 543, gate count minimized to 4,692, and memory usage decreased to 164,422 KB. Power consumption was lowered to 156.70 mW, with a 22.4% reduction in latency and an 18.6% increase in throughput. Validation was conducted using real-time FECG datasets from MIT PhysioNet. These results highlight the proposed system's efficiency in delivering secure, accurate, and real-time wireless fetal monitoring, making it a promising solution for next-generation healthcare applications.