scispace - formally typeset
Search or ask a question
Book ChapterDOI

Automatic Quality Assessment of a Forced Expiratory Manoeuvre Acquired with the Tablet Microphone

TL;DR: This method provides immediate feedback to the user, by grading the manoeuvre in a visual scale, promoting the repetition of the FEM when needed, and using 498 FEM recordings, both specificity and sensitivity attained were above 90%.
Abstract: Evaluation of lung function is central to the management of chronic obstructive respiratory diseases. It is typically evaluated with a spirometer by a specialized health professional, who ensures the correct execution of a forced expiratory manoeuvre (FEM). Audio recording of a FEM using a smart device embedded microphone can be used to self-monitor lung function between clinical visits. The challenge of microphone spirometry is to ensure the validity and reliability of the FEM, in the absence of a health professional. In particular, the absence of a mouthpiece may allow excessive mouth closure, leading to an incorrect manoeuvre. In this work, a strategy to automatically assess the correct execution of the FEM is proposed and validated. Using 498 FEM recordings, both specificity and sensitivity attained were above 90%. This method provides immediate feedback to the user, by grading the manoeuvre in a visual scale, promoting the repetition of the FEM when needed.
References
More filters
Proceedings ArticleDOI
05 Sep 2012
TL;DR: SpiroSmart, a low-cost mobile phone application that performs spirometry sensing using the built-in microphone, is presented and it is shown that pulmonologists can use SpiroSmart to diagnose varying degrees of obstructive lung ailments.
Abstract: Home spirometry is gaining acceptance in the medical community because of its ability to detect pulmonary exacerbations and improve outcomes of chronic lung ailments. However, cost and usability are significant barriers to its widespread adoption. To this end, we present SpiroSmart, a low-cost mobile phone application that performs spirometry sensing using the built-in microphone. We evaluate SpiroSmart on 52 subjects, showing that the mean error when compared to a clinical spirometer is 5.1% for common measures of lung function. Finally, we show that pulmonologists can use SpiroSmart to diagnose varying degrees of obstructive lung ailments.

221 citations

Proceedings ArticleDOI
29 May 2013
TL;DR: Besides enabling accurate COPD examinations at home, the mCOPD system also offers a video-game based guidance system for breathing exercises, which is a promising tool for remote medical treatment of COPD.
Abstract: COPD (Chronic Obstructive Pulmonary Disease) is a serious lung disease that causes difficulty in breathing. COPD patients require lung function examinations and perform breathing exercises on a regular basis in order to manage and be more aware of their health status. In this paper, we designed and developed a mobile-phone based system for lung function diagnosis, called mCOPD. Besides enabling accurate COPD examinations at home, the mCOPD system also offers a video-game based guidance system for breathing exercises. We evaluated mCOPD in controlled and uncontrolled environments with 40 subjects. The experimental results show that our system is a promising tool for remote medical treatment of COPD.

20 citations

Proceedings ArticleDOI
16 Jun 2013
TL;DR: A system to recognize and classify sounds produced by human subjects blowing air by the mouth using low-complexity algorithms in a low-budget processor and a naive Bayes classifier is presented.
Abstract: This paper presents a system to recognize and classify sounds produced by human subjects blowing air by the mouth. The objective is to implement the system for fast recognition using low-complexity algorithms in a low-budget processor. Recognition is achieved using tailored band energy ratios, modified frequency centroid and a periodicity test based on spectrum autocorrelation. These lightweight feature extraction techniques are adapted to the particular task of recognition of blowing sound types. The classification is achieved by a naive Bayes classifier. The algorithm can be implemented in real-time (latency ≤ 100 ms) and experimental test results show average recognition rates over 94 %.

4 citations

Proceedings ArticleDOI
12 Jan 2015
TL;DR: This paper aims to find the best and most efficient combination of signal processing and machine learning approaches to produce a smartphone application that could accurately classify lung function, using microphone recordings as the only input.
Abstract: Worldwide, over 250 million people are affected by chronic lung conditions such as Asthma and COPD. These can cause breathlessness, a harsh decrease in quality of life and, if not detected and duly managed, even death. In this paper, we aim to find the best and most efficient combination of signal processing and machine learning approaches to produce a smartphone application that could accurately classify lung function, using microphone recordings as the only input. A total of 61 patients performed the forced expiration maneuver providing a dataset of 101 recordings. The signal processing comparison experiments were conducted in a backward selection approach, reducing from 54 to 12 final envelopes, per recording. The classification experiments focused first on differentiating Normal from Abnormal lung function, and second in multiple lung function patterns. The results from this project encourage further development of the system.

2 citations