Smart Patient Monitoring Using AI: A Breakthrough Real-Time ECG System for Multiple Patients

- 6월 26, 2025

Smart Patient Monitoring Using AI: A Breakthrough Real-Time ECG System for Multiple Patients

Sung-Jin Ahn, Jong Doo Choi, Hoo-Hyun Kim, GyungChul Kim, Hee-Seok Song and Young-Shin Lee

Keywords: AI ECG monitoring, real-time patient monitoring, wearable health devices, arrhythmia detection, IoT in healthcare, parallel data processing, cardiac care innovation

Introduction

The rise of cardiovascular disease (CVD) as a leading cause of global mortality demands smarter, faster, and more scalable healthcare interventions. As hospitals and clinics seek to manage growing patient loads with limited resources, real-time ECG monitoring powered by artificial intelligence (AI) has emerged as a transformative solution. A recent groundbreaking study titled “Smart Patient Monitoring: AI-Driven Real-Time System for Multiple Patients” presents a next-generation ECG monitoring architecture that enables efficient, low-latency, and high-accuracy cardiac surveillance across multiple hospitalized patients simultaneously.

In this blog post, we explore the system's design, architecture, performance metrics, and clinical validation, with insights into how AI is revolutionizing inpatient cardiac monitoring. This post is optimized for Google SEO and structured for professionals, researchers, and digital health innovators aiming to stay at the forefront of AI-powered patient care.

Why Real-Time ECG Monitoring Matters

Cardiovascular diseases, including arrhythmias like tachycardia, bradycardia, and asystole, require early detection to prevent life-threatening events. According to the American Heart Association, continuous ECG monitoring significantly enhances prognosis by allowing timely intervention during cardiac anomalies [3][4].

Traditional telemetry systems, however, face several challenges:

False alarms, leading to alarm fatigue
Wired setups that restrict patient mobility
Sequential data processing, limiting scalability
Insufficient integration with modern IoT and AI technologies

To address these issues, the AI-based smart monitoring system presented in the study offers wearable sensor integration, adaptive batch processing, deep learning-based detection, and self-scheduling parallelism for real-time operation.

System Overview

The proposed system includes four primary phases:

1. Data Acquisition

Using the mobiCARE-MC200 wearable ECG sensor, the system captures real-time Lead II ECG signals at 256 Hz. These lightweight sensors provide high patient comfort and uninterrupted mobility within hospital environments.

2. Data Transmission

Data is transmitted via Bluetooth Low Energy (BLE) to a gateway device (MGW1000) and forwarded to a centralized server over a secure 1 Gbps Ethernet or dual-band Wi-Fi (2.4/5 GHz).

3. Central Processing Server

This is the heart of the system. The server contains:

A deep learning-powered algorithm module
An in-memory database (IMDB) for real-time access
Self-scheduling submodules that independently handle:
- Segmentation
- Feature Extraction
- Arrhythmia Detection

Figure 1: System Architecture

4. Real-Time Monitoring Interface

Healthcare providers access a web-based dashboard (Figure 2) to:

View heart rate, breathing rate, and alarm status
Control system modules
Monitor device status and patient identity

Figure 2: web-based dashboard

AI-Powered ECG Analysis: Modular Breakdown

Segmentation Module

Employs a Deep Dual-Resolution Network (DDRNet) to semantically label ECG waveforms—P-wave, QRS complex, T-wave, etc.—with optimized parameters for 1D 16-second ECG signals. Cross-entropy loss and Adam optimizer enhance training convergence.

Feature Extraction Module

Uses signal processing to derive key metrics like R-peak amplitude and RR intervals, essential for classifying cardiac rhythms.

Arrhythmia Detection Module

Applies rule-based detection for five arrhythmias:

Ventricular Tachycardia (VT): ≥3 V-beats, HR >100 bpm
Tachycardia: ≥14 beats, HR >140 bpm
Bradycardia: ≥5 beats, HR <40 bpm
Pause: No beats for 2–4 seconds
Asystole: No beats for >4 seconds

All alarms are timestamped and transmitted in real time.

Figure 3: Algorithm Module Architecture

Performance Evaluation

Test Environment

Hardware: Intel Core i7-10700K, 48 GB RAM
Software: Python 3.10 on Windows 10 Pro 64-bit

Two metrics were assessed:

1. Concurrent Processing Performance

Data from up to 250 patients was simulated using 2-minute ECG samples, divided into 3-second segments for streaming. Five metrics were measured:

Completion Time: Time from last data input to final analysis
Wait Time: Delay between data arrival and processing start
Execution Time: Time taken by algorithms to analyze data
Concurrent Patient Count: Number of patients processed per batch
Module Operation Count: Number of times submodules executed

Key Findings:

The AI system handled up to 240 patients in real-time.
Completion time remained below 3 seconds even with 240 patients.
Wait times remained steady at ~0.25s vs. >130s in the traditional system.
Parallelism and batch processing reduced computational load significantly.

Figure 4: Performance Metrics Visualizations

2. Arrhythmia Detection Accuracy

Data from 80 hospitalized patients undergoing RF catheter ablation were compared with specialist interpretations.

Results

Arrhythmia	Sensitivity (%)	PPV (%)	NPV (%)	Accuracy (%)
VT	97.14	75.84	91.43	79.06
TACHY	100	100	100	100
BRADY	100	100	100	100
PAUSE	100	89.88	100	90.84
ASYSTOLE	100	90.48	100	97.94

Average sensitivity: 99.43%

Discussion: What Sets This System Apart

This study’s innovation lies in scalability without sacrificing real-time processing, made possible through:

Self-scheduling parallelism
Adaptive batch sizing
Rule-based AI logic for interpretable decisions
High accuracy without GPU dependence

The system is particularly suitable for resource-limited settings and ICUs, where cost-effective large-scale monitoring is urgently needed.

Quiz Section

Q: Which deep learning architecture is used for ECG segmentation in this system?

A: Deep Dual-Resolution Network (DDRNet)
Explanation: DDRNet was chosen for its balance between speed and segmentation accuracy in real-time scenarios.

Q: What arrhythmia is detected when there is a pause in heartbeats longer than 4 seconds?

A: Asystole
Explanation: The system classifies this as a critical cardiac arrest event and triggers an immediate alert.

Q: What was the maximum number of patients processed in real-time without latency?

A: 240
Explanation: The system achieved under-3s completion time for up to 240 patients, proving real-time viability.

Conclusion

The AI-driven real-time ECG monitoring system presented here marks a paradigm shift in inpatient care. By combining wearable sensors, high-efficiency batch processing, and deep learning, it empowers hospitals to monitor hundreds of patients simultaneously, with unprecedented accuracy and responsiveness.

Such innovations are vital as we move toward smart hospitals, where technology not only supplements human decision-making but also actively prevents critical events through intelligent automation.

References

Satija, U., Ramkumar, B., & Manikandan, M. S., "Real-time signal quality-aware ECG telemetry system for IoT-based health care monitoring," IEEE Internet Things J., vol. 4, no. 3, pp. 815–823, 2017.
Scrugli, M. A., et al., "A runtime-adaptive cognitive IoT node for healthcare monitoring," ACM Computing Frontiers, pp. 350–357, 2019.
Drew, B. J., et al., "Practice standards for electrocardiographic monitoring in hospital settings," Circulation, vol. 110, no. 17, pp. 2721–2746, 2004.
Sandau, K. E., et al., "Update to practice standards for ECG monitoring," Circulation, vol. 136, no. 19, pp. e273-e344, 2017.
Sandroni, C., et al., "In-hospital cardiac arrest: incidence, prognosis and improvement measures," Intensive Care Med., vol. 33, pp. 237–245, 2007.
Kwon, S., et al., "Comparison of novel ECG patch with traditional telemetry," Korean Circ J., vol. 54, no. 3, pp. 140, 2024.
Ahouandjinou, A. S., et al., "Smart ICU IoT systems," IEEE BioSMART, pp. 1–4, 2016.

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