Deep learning approaches for the cardiovascular disease diagnosis using smartphone

Abstract

One of the most important subjects for societies is human health services, which aims to determine the appropriate, accurate and robust diagnosis of the disorder for patients to get the adequate treatment as quickly as possible. Since this diagnosis is always a challenging process, support from other areas such as statistics and computer science are needed for healthcare. Biomedical signals relevant to several diseases are recorded from the human body and are generally employed to diagnose physiological or pathological conditions. The objective of biomedical signal analysis is exact modeling by using machine learning techniques for the diagnosis of diseases. This chapter explains how deep learning approaches are utilized in disease diagnosis. An automated diagnosis of cardiovascular diseases (CVDs) based on deep learning approaches is also presented as a case study. Atrial fibrillation (AFib) is one of the most common chronic and relapsing heart arrhythmias. Mechanocardiography (MCG) through which translational and rotational precordial chest movements are monitored is an effective approach for the detection of CVDs. MCG information obtained from cardiac patients using a smartphone's multidimensional built-in inertial sensors. The aim is to identify AFib episodes employing a smartphone MCG (or sMCG). Hence, this book chapter deals with applications of deep learning for the diagnosis of human diseases. In addition, this chapter focuses on current methods relevant to the utilization of deep learning techniques employed for cardiac abnormality detection, in order to discover remarkable patterns, make non-trivial assessments and make use of smartphone sensors effective in decision making. Hence, this chapter will assist researchers to explore the applicability of artificial intelligence approaches in their particular specialties for disease diagnosis and treatment.