Advancements in Cardiovascular Disease Detection: A Comprehensive Survey of ECG and PCG Recording Datasets

A thorough study of the quickly evolving field of cardiovascular disease (CVD) detection is conducted, with a particular emphasis on ECG and PCG recording datasets. Since CVD is still a global health concern, quick and accurate detection techniques are essential to improving patient survival and reducing the strain on healthcare systems. By combining information from PCG and ECG by means of this survey, a complete evaluation of cardiac performance, important for early diagnosis and treatment, is determined. "Systems", which only rely on singular modalities, have specific and sensitive characteristics and need more advanced methods. Detailed analysis of deep learning algorithms, multimodal approach, and model-based means, machine learning classifiers, feature extraction, feature selection, and deep learning algorithms are offered by the survey by presenting the refined comparison of techniques, algorithms, and performance results published in different research articles. Also, the article reviews publicly available datasets and the unique insights into cardiovascular research they offer. The aim of this survey is to pave the way for more advanced and refined approaches by conducting a comprehensive analysis of the landscape of CVD detection today. This will lead to greater reliability and accuracy with regard to the identification of cardiovascular disease.