This article presents a systematic map** study dedicated to conduct a literature review on
machine learning and IoT applied in the identification of diseases through heart sounds …

Background This study examines the development of the connected health (CH) research
landscape with a view to providing an overview of the existing CH research. The research …

This paper strives to construct the relevance between smartphone sensor data and personal
health via a deep learning method. Firstly, the data captured by smartphone sensors is …

Identification of pulmonary diseases comprises of accurate auscultation as well as elaborate
and expensive pulmonary function tests. Prior arts have shown that pulmonary diseases …

Abnormal heart sounds may have diverse frequency characteristics depending upon
underlying pathological conditions. Designing a binary classifier for predicting normal and …

The medical device used for auscultation is known as a stethoscope. An ordinary acoustics-
based stethoscope is limited in its ability to provide high-quality sound in noisy …

Usually physicians use acoustic stethoscope to detect abnormalities in the heart sound and
predict abnormal conditions of human heart. As the frequency range and intensity of heart …

Abstract Coronary Artery Disease (CAD) kills more than a million of people every year.
However, there is no significant marker for identifying CAD patients unobtrusively. In this …

Objectives: The demand for cardiopulmonary assessment via real-time live streaming is
prevalent in remote communities of British Columbia, Canada. Digital stethoscopes enable …

Technological advancements have led to significant improvements in healthcare for
prevention, diagnosis, treatments, and care. While resourceful regions can capitalize on …