Vol. 7 No. 1 (2026)

Standard Journal Issues

Deep Learning Based Early Detection of Atherosclerosis for Stroke Prevention using Multi-Sensor Data Integration

Published 2026-02-26

  • Akshaya D. Shetty
    • ,
  • Usha Desai
    • ,
  • Akash Saxena

Akshaya D. Shetty
Department of Electronics and Communication Engineering, S.E.A College of Engineering & Technology, Bengaluru 560049, Karnataka, India , Department of Artificial Intelligence and Machine Learning, Vivekananda College of Engineering & Technology, Puttur 574203, Karnataka, India , Visvesvaraya Technological University, Belagavi 590018, Karnataka, India

Usha Desai
Department of Electronics and Communication Engineering, S.E.A College of Engineering & Technology, Bengaluru 560049, Karnataka, India , Visvesvaraya Technological University, Belagavi 590018, Karnataka, India

Akash Saxena
Department of Electrical Engineering, School of Engineering and Technology, Central University of Haryana 123031, Haryana, India

Abstract

Atherosclerosis is a progressive cardiovascular condition where arteries narrow due to plaque buildup, significantly increasing the risk of heart attacks and strokes. This study presents a non-invasive, Deep Learning-architecture based system for the timely diagnosis of atherosclerosis by means of real-time physiological and clinical data. This system integrates wearable sensors namely, Electrocardiogram (ECG), Photoplethysmography (PPG), Galvanic Skin Response (GSR), and Blood Pressure (BP) to continuously monitor heart-related parameters. Also, incorporates the clinical indicators namely blood glucose and cholesterol levels to enhance predictive accuracy. Data set comprises 226 records of the subjects having signs of atherosclerosis and 180 records of healthy subjects. The feature extraction involves total 8 original and 5 engineered features. Collected data undergoes preprocessing and is analyzed using various Deep Learning architectures including LSTM, BiLSTM, GRU, CNN-LSTM, and Transformer networks. These models are trained and evaluated using stratified K-fold cross-validation, ensuring consistent and generalized performance. The assessment metrics involves accuracy, precision, recall and F1 score. Among these, CNN-LSTM and Transformer models achieved superior accuracy and robustness in classifying individuals as healthy or at risk of atherosclerosis. The best model is chosen as CNN-LSTM with highest weighted score of 0.98 in comparison with other individual models. The final model is deployed in a user-friendly Streamlit interface, which helps users to input physiological data and receive real-time health predictions. The system provides a diagnostic output, confidence score, and highlights of any abnormal parameters. This solution addresses limitations of traditional diagnostics such as high cost, invasiveness, and lack of real-time feedback by offering a portable, affordable, and continuous monitoring tool. It empowers users, especially in remote or underserved areas, to take proactive measures for stroke prevention and cardiovascular health management.

Keywords

Atherosclerosis, Deep Learnin, CNN-LSTM, Transformer, Wearable Sensors

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