Abstract
One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient’s heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.
Highlights
Cardiovascular disease is one of the principal causes of human death in all over the world.Based on the American College of Cardiology, in 2008, over 616,000 persons died of heart disease, which caused almost 25% of deaths in the US, i.e., one in every four deaths [1]
It can be ascertained that current implementation takes the advantage of parallel processing and much faster than the deep learning techniques
The digital stethoscope was designed by modifying an analog stethoscope and adding an analog front end and miniaturized microcontroller with built-in Bluetooth low-energy (BLE) for digitization and transmission
Summary
Cardiovascular disease is one of the principal causes of human death in all over the world. Sensors 2019, 19, 2781 non-invasive, and intuitive method of diagnosing heart-related issues, has its limitation when it comes to detecting structural abnormalities and defects in heart valves due to heart murmurs [5]. Other technologies such as magnetic resonance imaging (MRI), which uses radio waves and magnets, are even capable of capturing moving images of the heart and major blood vessels [6]. S3 can be heard during the rush of blood entry to the ventricle from atrium and is normally a pre-diastolic low-pitched sound.
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