Classifying obstructive sleep apnea using smartphones

AbstractObstructive sleep apnea (OSA) is a serious sleep disorder which is characterized by frequent obstruction of the upper airway, often resulting in oxygen desaturation. The serious negative impact of OSA on human health makes monitoring and diagnosing it a necessity. Currently, polysomnography is considered the gold standard for diagnosing OSA, which requires an expensive attended overnight stay at a hospital with considerable wiring between the human body and the system. In this paper, we implement a reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests. The design takes advantage of a smatrphone’s built-in sensors, pervasiveness, computational capabilities, and user-friendly interface to screen OSA. We use three main sensors to extract physiological signals from patients which are (1) an oximeter to measure the oxygen level, (2) a microphone to record the respiratory effort, and (3) an accelerometer to detect the body’s movement. Finally, we examine our system’s ability to screen the disease as compared to the gold standard by testing it on 15 samples. The results showed that 100% of patients were correctly identified as having the disease, and 85.7% of patients were correctly identified as not having the disease. These preliminary results demonstrate the effectiveness of the developed system when compared to the gold standard and emphasize the important role of smartphones in healthcare

An open access database for the evaluation of heart sound algorithms

This is an author-created, un-copyedited version of an article published in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/0967-3334/37/12/2181In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.This work was supported by the National Institutes of Health (NIH) grant R01-EB001659 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and R01GM104987 from the National Institute of General Medical Sciences.Liu, C.; Springer, DC.; Li, Q.; Moody, B.; Abad Juan, RC.; Li, Q.; Moody, B.... (2016). An open access database for the evaluation of heart sound algorithms. Physiological Measurement. 37(12):2181-2213. doi:10.1088/0967-3334/37/12/2181S21812213371

Intelligent heartsound diagnostics on a cellphone using a hands-free kit

In resource-constrained environments, supply chains for consumables, repairs and calibration of diagnostic equipment are generally poor. To obviate this issue, we propose the use of widely available hardware with a strong supply chain: a cellphone with a hands-free kit. In particular, we focus on the use of the audio channel to determine heart rate (HR) and heart rate variability (HRV) in order to provide a first level screening system for infection. This article presents preliminary work performed on a gold standard database and a cellphone platform. Results indicate that HR and HRV can be accurately assessed from acoustic recordings of heart sounds using only a cellphone and hands-free kit. Heart sound analysis software, which can run on a standard cellphone in real time, has been developed that detects S1 heart sounds with a sensitivity of 92.1% and a positive predictivity of 88.4%. Evaluation of data recorded from cellphones demonstrates that the low-frequency response (<100 Hz) is key to the success of heart sound analysis on cellphones. Noise rejection is also shown to be important. © 2010, Association for the Advancement of Artificial Intelligence

A framework for automated heart and lung sound analysis using a mobile telemedicine platform

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 246-261).Many resource-poor communities across the globe lack access to quality healthcare,due to shortages in medical expertise and poor availability of medical diagnostic devices. In recent years, mobile phones have become increasingly complex and ubiquitous. These devices present a tremendous opportunity to provide low-cost diagnostics to under-served populations and to connect non-experts with experts. This thesis explores the capture of cardiac and respiratory sounds on a mobile phone for analysis, with the long-term aim of developing intelligent algorithms for the detection of heart and respiratory-related problems. Using standard labeled databases, existing and novel algorithms are developed to analyze cardiac and respiratory audio data. In order to assess the algorithms' performance under field conditions, a low-cost stethoscope attachment is constructed and data is collected using a mobile phone. Finally, a telemedicine infrastructure and work-flow is described, in which these algorithms can be deployed and trained in a large-scale deployment.by Katherine L. Kuan.M.Eng