A Combined ECG and PPG Signal Powered Artificial Intelligence-Based Prediction Model for Stroke

Year : 2025 | Volume : 16 | Issue : 02 | Page : 18 26
    By

    Shikha Sahu,

  • Saloni Shrivastava,

  • Sanjeev Kumar Sharma,

  1. Scholar, Department of Computer Science and Engineering, Technocrats Institute of Technology-CSE, Bhopal, Madhya Pradesh, India
  2. Assistant Professor, Department of Computer Science and Engineering, Technocrats Institute of Technology-CSE, Bhopal, Madhya Pradesh, India
  3. Director, Department of Computer Science and Engineering, Technocrats Institute of Technology-CSE, Bhopal, Madhya Pradesh, India

Abstract

Stroke is one of the most common causes of morbidity and mortality around the world, and emphasis on prevention and early detection strategies cannot be overstated. This review aims to integrate techniques of artificial intelligence with electrocardiogram and photoplethysmogram signals to enhance stroke prediction and monitoring of cardiovascular health. All in all, the application of artificial intelligence that incorporates machine learning, deep learning, or hybrid models gives robust tools toward the analysis of very complicated bio-signals and enhances real-time, personalized early detection towards conditions like atrial fibrillation and vascular irregularity. Such developments can easily go hand in hand with these wearable technology and telemedicine applications. Despite the challenges of variability in data, noise, and ethical issues, ECG and PPG-based synergistic use with AI has immense potential to transform stroke prevention and patient outcome. This study highlights recent advancements, challenges, and future directions in this transformative field.

Keywords: Stroke prediction, artificial intelligence, ECG, PPG, machine learning, deep learning, hybrid models, wearable technology, telemedicine, cardiovascular monitoring

[This article belongs to Journal of Control & Instrumentation ]

How to cite this article:
Shikha Sahu, Saloni Shrivastava, Sanjeev Kumar Sharma. A Combined ECG and PPG Signal Powered Artificial Intelligence-Based Prediction Model for Stroke. Journal of Control & Instrumentation. 2025; 16(02):18-26.
How to cite this URL:
Shikha Sahu, Saloni Shrivastava, Sanjeev Kumar Sharma. A Combined ECG and PPG Signal Powered Artificial Intelligence-Based Prediction Model for Stroke. Journal of Control & Instrumentation. 2025; 16(02):18-26. Available from: https://journals.stmjournals.com/joci/article=2025/view=213275


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Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 22/03/2025
Accepted 24/03/2025
Published 16/05/2025
Publication Time 55 Days
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