Significance of Features from Biomedical Signals in Heart Health Monitoring

Abstract

Cardiovascular diseases require extensive diagnostic tests and frequent physician visits. With the advance in signal processing and sensor technology, now it is possible to acquire vital signs from the human body and process the signal to extract features necessary to primarily diagnose symptoms of cardiovascular disease early. This can help prevent deadly health incidents such as heart attack and or stroke, as well as reduce the number of visits to a health care facility. The proper detection of an elevated ST segment of ECG wave at an early stage may save the patient from having a heart attack or ST elevated myocardial infarction later. The use of a variety of complementary biomedical sensors can lead to a better diagnosis than what is possible when a single sensor is used. This paper proposes a MATLAB GUI which can detect elevated ST segments of ECG waves and use information from a variety of biomedical sensors to bring forth a technique to assess heart health to predict potential heart failure conditions. The proposed technique used fusion among multiple biomedical sensors to reduce the false alarm in diagnosis. Data from the online dataset were used to show the effectiveness and promise of the proposed detection of elevated ST segments and diagnosis techniques using the GUI.