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RealTime Health Monitoring Using 5G Networks: A Deep Learning-Based Architecture for Remote Patient Care

Iqra Batool

TL;DR

The paper addresses real-time remote patient monitoring by integrating a hybrid CNN-LSTM deep learning model with 5G URLLC to achieve sub-second end-to-end latency and high prediction accuracy. It proposes an edge-optimized architecture leveraging network slicing and URLLC to support real-time vital-sign analytics and prediction across heart rate, blood pressure, and respiratory rate. Evaluations on data from $1000$ ICU patients over $3$ months report an end-to-end latency of $14.4$ ms and an average prediction accuracy of $96.5\%$, outperforming three benchmarks with a $47\%$ latency reduction and $4.2\%$ accuracy gain. The work establishes a practical framework for reliable, real-time vital sign monitoring in digital healthcare and points to future directions in multimodal data fusion, adaptive learning, federated approaches, and privacy-preserving deployment.

Abstract

Remote patient monitoring is crucial in modern healthcare, but current systems struggle with real-time analysis and prediction of vital signs. This paper presents a novel architecture combining deep learning with 5G network capabilities to enable real-time vital sign monitoring and prediction. The proposed system utilizes a hybrid CNN-LSTM model optimized for edge deployment, paired with 5G Ultra-Reliable Low-Latency Communication (URLLC) for efficient data transmission. The architecture achieves end-to-end latency of 14.4ms while maintaining 96.5% prediction accuracy across multiple vital signs. Our system shows significant improvements over existing solutions, reducing latency by 47% and increasing prediction accuracy by 4.2% compared to current state-of-the-art systems. Performance evaluations conducted over three months with data from 1000 patients validate the system's reliability and scalability in clinical settings. The results demonstrate that integrating deep learning with 5G technology can effectively address the challenges of real-time patient monitoring, leading to early detection of deteriorating conditions and improved clinical outcomes. This research establishes a framework for reliable, real-time vital sign monitoring and prediction in digital healthcare.

RealTime Health Monitoring Using 5G Networks: A Deep Learning-Based Architecture for Remote Patient Care

TL;DR

The paper addresses real-time remote patient monitoring by integrating a hybrid CNN-LSTM deep learning model with 5G URLLC to achieve sub-second end-to-end latency and high prediction accuracy. It proposes an edge-optimized architecture leveraging network slicing and URLLC to support real-time vital-sign analytics and prediction across heart rate, blood pressure, and respiratory rate. Evaluations on data from ICU patients over months report an end-to-end latency of ms and an average prediction accuracy of , outperforming three benchmarks with a latency reduction and accuracy gain. The work establishes a practical framework for reliable, real-time vital sign monitoring in digital healthcare and points to future directions in multimodal data fusion, adaptive learning, federated approaches, and privacy-preserving deployment.

Abstract

Remote patient monitoring is crucial in modern healthcare, but current systems struggle with real-time analysis and prediction of vital signs. This paper presents a novel architecture combining deep learning with 5G network capabilities to enable real-time vital sign monitoring and prediction. The proposed system utilizes a hybrid CNN-LSTM model optimized for edge deployment, paired with 5G Ultra-Reliable Low-Latency Communication (URLLC) for efficient data transmission. The architecture achieves end-to-end latency of 14.4ms while maintaining 96.5% prediction accuracy across multiple vital signs. Our system shows significant improvements over existing solutions, reducing latency by 47% and increasing prediction accuracy by 4.2% compared to current state-of-the-art systems. Performance evaluations conducted over three months with data from 1000 patients validate the system's reliability and scalability in clinical settings. The results demonstrate that integrating deep learning with 5G technology can effectively address the challenges of real-time patient monitoring, leading to early detection of deteriorating conditions and improved clinical outcomes. This research establishes a framework for reliable, real-time vital sign monitoring and prediction in digital healthcare.
Paper Structure (28 sections, 22 equations, 7 figures, 5 tables, 2 algorithms)

This paper contains 28 sections, 22 equations, 7 figures, 5 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Deep Learning-Based Vital Sign Analysis Systems
  4. 5G-Enabled Healthcare Monitoring
  5. Hybrid Systems Combining Deep Learning and 5G
  6. Proposed System Architecture
  7. System Overview
  8. Deep Learning Framework
  9. 5G Network Integration
  10. Network Slicing Configuration
  11. Resource Allocation
  12. Latency Optimization
  13. Data Processing Pipeline
  14. Implementation
  15. Experimental Setup
  16. ...and 13 more sections

Figures (7)

  • Figure 1: An Integrated Approach to Modern Healthcare
  • Figure 2: System Integration and Deployment Architecture
  • Figure 3: Deep Learning Model Development for Vital Sign Analysis
  • Figure 4: Performance Timline
  • Figure 5: Latency Analysis
  • ...and 2 more figures