Real-time Identification of Respiratory Movements through a Microphone

Abstract

KEYWORD ABSTRACT Digital signal processing Pattern recognition Machine learning Respiratory analysis Acoustic featuresThis work presents a software application to identify, in real time, the respiratory movements -inspiration and expiration-through a microphone. The application, which has been developed in Matlab and named ASBSLAB for the GUI version and ASBSLABCONSOLE for the command-line version, is the result of a research and experimentation process. A total of 48 minutes of breathing movements from four subjects was recorded and 18 acoustic features were extracted to generate the data model. A first level of identification, based on the classification of tiny audio segments, was designed using kNN supervised method. The second level of identification implements a state machine that takes the results ordered in the time from kNN as input and identifies the whole respiratory movement, achieving a level of positive identifications above 95%. As computation time is a handicap, the application let the user choose easily the sample rate, the audio segment size and the set of acoustic features to use in the identification process. In addition, based on the number of features selected, this works suggests those that achieve best results.

Applying ubiquitous sensing to estimate perceived exertion based on cardiorespiratory features

“…The aVE was calculated by obtaining the product of the audio volume amplitude and the Rf. Rf can also be obtained from the audio data through signal processing methods [37][38][39][40][41], although this process demands a validation that is beyond the aims of this study. For this reason, only the Rf from the metabolic cart, the gold standard, was used for the following analysis.…”

Development of a Novel Wireless Multi-Channel Stethograph System for Monitoring Cardiovascular and Cardiopulmonary Diseases

“…Respiration rate = × 60 (8) where N is number of the breaths that were extracted and T is the total duration of the trachea sound in seconds. The rising time is longer than decay time in inhalation and shorter in exhalation, and this helps in identifying the inhalation and exhalation patterns in the trachea sound [49]. In addition, a peak detection method was used for determining the location of the breath peak and in calculating the rise and decay times for identifying inhalation and exhalation.…”

Development of a Novel Wireless Multi-Channel Stethograph System for Monitoring Cardiovascular and Cardiopulmonary Diseases

“…Respiration rate = × 60 (7) where N is number of the breaths that were extracted and T is the total duration of the trachea sound in seconds. The rising time is longer than decay time in inhalation and shorter in exhalation, and this helps in identifying the inhalation and exhalation patterns in the trachea sound [49]. In addition, a peak detection method was used for determining the location of the breath peak and in calculating the rise and decay times for identifying inhalation and exhalation.…”