Heart Diseases Diagnose via Mobile Application

Mesut Güven,Funda Tuna,Kanat Özışık,Fırat Hardalaç

doi:10.3390/app11052430

Mesut Güven, Funda Tuna + Show 2 more

Open Access

https://doi.org/10.3390/app11052430

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Journal: Applied sciences	Publication Date: Mar 9, 2021
Citations: 1	License type: CC BY 4.0

Affiliation: Gazi University, Memorial Ankara Hospital

Abstract

One of the oldest and most common methods of diagnosing heart abnormalities is auscultation. Even for experienced medical doctors, it is not an easy task to detect abnormal patterns in the heart sounds. Most digital stethoscopes are now capable of recording and transferring heart sounds. Moreover, it is proven that auscultation records can be classified as healthy or unhealthy via artificial intelligence techniques. In this work, an artificial intelligence-powered mobile application that works in a connectionless fashion is presented. According to the clinical experiments, the mobile application can detect heart abnormalities with approximately 92% accuracy, which is comparable to if not better than humans since only a small number of well-trained cardiologists can analyze auscultation records better than artificial intelligence. Using the diagnostic ability of artificial intelligence in a mobile application would change the classical way of auscultation for heart disease diagnosis.

Highlights

Heart disease is one of the most common causes of death worldwide [1]
Segmentation is necessary for classification since most of the features used for classification are derived from fundamental heart sounds (FHSs) that occur because of the contraction and relaxation movements of the heart [7]
Sensitivity which is very close to validation results in Appendix A (Tables A1–A7). This result shows the robustness of the developed classification algorithm since it can work properly on a mobile device

Summary

Introduction

Heart disease is one of the most common causes of death worldwide [1]. Even in developed countries, healthcare services are expensive, and having a check-up in a clinic is a time-consuming and costly task, but it is a relatively dangerous endeavor under the current conditions of a global pandemic. Automatic diagnoses of heart diseases have been studied for a long time and the proposed algorithms have acceptable success on the largest available dataset, the PhysioNet/CinC Challenge 2016 dataset [5,6]. The general methodology of the automated heart sound classification algorithms consists of three steps. Those are segmentation, feature extraction, and classification steps (Figure 1). Segmentation is necessary for classification since most of the features used for classification are derived from fundamental heart sounds (FHSs) that occur because of the contraction and relaxation movements of the heart [7]. Available online: https://www.mathworks.com/help/supportpkg/android/ref/gettingstarted-with-android-devices.html (accessed on 10 January 2021). Available online: https://www.mathworks.com/help/supportpkg/appleios/ug/gettingstarted-with-apple-ios-devices.html (accessed on 10 January 2021).

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Conclusion