Purpose: This study aims to address the critical issue of delayed detection of cardiovascular heart diseases, a leading cause of sudden death and heart failure in younger populations. It seeks to develop an innovative, personalized, and ambulatory system for early detection and timely intervention to prevent lifethreatening outcomes. Design/Methodology/Approach: The research proposes a novel approach utilizing Photoplethysmography (PPG) integrated into smartwatches and wearable devices. An artificially intelligent prognosis system, combining Convolutional Neural Network – Long-Short Term Memory (CNN-LSTM) and random forest algorithms, is employed to detect and quantify cardiovascular abnormalities. Datasets such as MIMIC-IV and PPG-DaLiA are used to train and validate the framework, ensuring continuous monitoring during daily activities. Findings: The CNN-LSTM and random forest-based system effectively detects cardiovascular abnormalities with high accuracy, quantifying the degree of irregularity. The framework demonstrates the potential for real-time, non-invasive monitoring, enabling early identification of cardiovascular issues before they escalate. Research Limitations/Implications: The study relies on existing datasets, which may not fully represent diverse populations or real-world conditions. The accuracy of PPG-based monitoring may be affected by factors such as motion artifacts or device placement. Further research is needed to validate the system across broader demographics and refine its robustness for clinical adoption. Practical Implications: The proposed system offers an affordable, privacyconscious, and accessible solution for continuous cardiovascular monitoring. By integrating with widely used wearable devices, it empowers individuals to proactively manage their heart health without requiring frequent medical consultations or specialized setups. Originality/Value: This research introduces a pioneering approach to cardiovascular monitoring by combining PPG with advanced AI techniques (CNNLSTM and random forest) in wearable technology. Its emphasis on privacy, affordability, and real-time detection during daily activities adds significant value to preventive healthcare, addressing the critical gap in early diagnosis of cardiovascular diseases.
Mohammad Ali Syed Jafri (Wed,) studied this question.