In recent years, cardiovascular diseases are on the rise, and they entail enormous health burdens on global economies. Cardiac vibrations yield a wide and rich spectrum of essential information regarding the functioning of the heart, and thus it is necessary to take advantage of this data to better monitor cardiac health by way of prevention in early stages. Specifically, seismocardiography (SCG) is a noninvasive technique that can record cardiac vibrations by using new cutting-edge devices as accelerometers. Therefore, providing new and reliable data regarding advancements in the field of SCG, i.e., new devices and tools, is necessary to outperform the current understanding of the State-of-the-Art (SoTA). This paper reviews the SoTA on SCG and concentrates on three critical aspects of the SCG approach, i.e., on the acquisition, annotation, and its current applications. Moreover, this comprehensive overview also presents a detailed summary of recent advancements in SCG, such as the adoption of new techniques based on the artificial intelligence field, e.g., machine learning, deep learning, artificial neural networks, and fuzzy logic. Finally, a discussion on the open issues and future investigations regarding the topic is included.

This paper proposes ideas to create hybrid optimization algorithms that combines strengths of SFLA or PSO with strengths of GA, DE or BA. While SFLA or PSO can find optimal solutions quickly because of directive searching and exchange of information, GA, DE or BA has higher random that make it easily escape from local optima to find global solutions. Thus, hybrid algorithms are able to find optimal solutions quickly like SFLA or PSO and escape from local optima like GA, DE or BA. A hybrid SFL-Bees algorithm has illustrated for these ideas. Numerical simulations carried out have shown the effectiveness of the proposed algorithm, its ability to achieve good quality solutions and processing time, which outperforms the SFLA and BA. General Terms Algorithms.

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