Computational Cardiology and Algorithmic Diagnostics: A Comprehensive Synthesis for Cardiac Arrest Detection

Abstract

The rapid evolution of computational cardiology has necessitated a paradigm shift from reactive clinical diagnostics to proactive, algorithmic risk stratification. This research report serves as a foundational analysis for the development of automated detection systems, synthesizing evidence from recent implementations of classical Machine Learning (ML) and advanced Deep Learning (DL) architectures. The investigation leverages a dual-modality approach. Firstly, it examines the application of ensemble learning techniques, predominantly Random Forest (RF) and Support Vector Machines (SVM), on standardized tabular datasets such as the UCI Cleveland repository. Analysis indicates that Random Forest classifiers, when optimized via rigorous hyperparameter tuning and K-fold cross-validation, achieve superior stability and accuracy, frequently exceeding 90% and reaching up to 99% in select combined dataset studies. Secondly, the report explores the frontier of Deep Learning in processing raw physiological signals (ECG/EEG). Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are identified as the gold standard for morphological feature extraction, capable of identifying subtle arrhythmia patterns invisible to standard rulebased systems. The integration of these disparate data streams clinical attributes and real-time physiological waveforms present the most promising avenue for reducing the global mortality burden of cardiovascular disease.

Introduction

Sudden Cardiac Arrest (SCA) is a major global health emergency, causing over 356,000 out-of-hospital cases annually in the U.S. and nearly 1 million cases in India, with survival depending heavily on instant CPR and defibrillation. Survival drops rapidly—10% per minute when CPR is delayed. SCA commonly results from coronary artery disease and electrolyte imbalance, especially hyperkalemia. It affects men more than women and is most common among adults in their 30s and 40s, with major risk factors including hypertension, arrhythmia, family history, high cholesterol, and smoking. Even survivors face high chances of neurological damage, such as Post-Cardiac Arrest Brain Injury (PCABI). Despite growing research, accurate pre-event prediction of cardiac arrest remains unsolved.

The paper reviews recent advancements in camera-based, non-invasive monitoring for early detection of cardiac distress. Two main areas dominate the literature:

1. Human pose estimation for detecting abnormal movements or collapse, and

2. Remote photoplethysmography (rPPG) for extracting heart-rate signals from video.

Modern tools like MediaPipe Pose, transformer-based models, fall-detection systems, and rPPG techniques (including the lightweight GRGB method) allow real-time monitoring, even on low-power edge devices. Studies show these systems can detect abnormal posture, respiration patterns, and fall sequences while preserving user privacy. Research also supports video-assisted telemedicine and AI-based cardiac arrest prediction in clinical settings.

The proposed system follows a client–server architecture that uses a webcam for continuous monitoring. Video frames are processed with OpenCV and analyzed using MediaPipe to extract over 500 facial and body landmarks. These coordinates are converted into feature vectors representing posture, joint angles, and micro-movements. A Random Forest classifier (100 trees, 10,000 training samples) identifies abnormal patterns that may indicate collapse or cessation of breathing. Alternative models like CNN-LSTM and One-Class SVM are also explored, though Random Forest offers the best balance of accuracy and computation speed.

A temporal decision logic module reduces false alarms by requiring that abnormalities persist for several seconds with high confidence before triggering an alert. Once validated, the system uses Twilio API to automatically send SMS notifications to emergency contacts, ensuring rapid response in unwitnessed cases. A cooldown mechanism prevents repeated alerts.

Testing shows that the system operates in real time (20–30 FPS), accurately detects posture changes and thoracic movement, and maintains strong reliability even with variations in lighting or body orientation. The temporal filtering approach significantly reduces false positives, while on-device processing ensures privacy by avoiding cloud uploads.

Conclusion

In summary, this investigation has successfully demonstrated the potential of machine learning enhancing the accuracy of cardiac arrest (CA) prediction utilizing heart health indicators. A primary finding of this research is the superior performance of the developed hybrid model which strategically leverages the strengths of thealgorithmsconfirming its effectiveness over alternative classification methods. This study affirms the utility of ML algorithms as a vital aid in the initial state diagnosis of cardiac arrest, which can subsequently inform and improve the timely administration of appropriate patient medications.While demonstrating clear utility, the research also identified specific opportunities for refinement in future work. To validate and expand the practical utility of these findings, future research should transition the analysis to real-world clinical datasets, thereby extending the applicability of the model beyond controlled test environments. techniques. By addressing these proposed steps, this research framework holds significant promise for contributing to life-saving diagnostics and ultimately reducing CA-related mortality rates.

References

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Copyright

Copyright © 2025 Dr. Vidyarani H J , Dr. Kavitha Devi CS, Yashaswi R, Monisha S, Abhishek B Hegde, Gowtham D, Achutha P Raju. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.