ECG Signal Analysis with QRS Complex Detection for Accurate Cardiac Abnormality Diagnosis

To improve the ECG signal interpretation helping to diagnose the indicated cardiac pathologies, the research concentrates on the formation and analysis of the QRS complex detection methods only. The QRS complex is widely used for diagnosing most cardiac disorders such as arrhythmias and myocardial infarction among others. Proposing a series of QRS complex classifications that enhance the existing solutions in terms of sensitivity and accuracy, the suggested approaches are built using advanced signal processing methodologies combined with logical focalization governed by the fuzzy formalism. This proposed approach showed better results than the benchmark fuzzy rule-based classifiers: classifications based on classical ICA and PCA algorithms in favor of accuracy, sensitivity, specificity, and the F1 score. Essential strengths of this method were higher accuracy due to the improved detection algorithm highlighted, paving way for real-time ECG to monitor and diagnose cardiac disorders. This research has also pointed out the strategies for the advancement of other non-invasive replacements for cardiac monitoring by utilizing wearable or portable ECG devices and constructed a technique for the efficient and accurate QRS complex differentiation in ECG. The future work also includes the fine tuning of the proposed model, expanding the applicability of the model in the hospitals and improving the diagnostic accuracy by implementing more complex machine learning techniques.