Detection of Respiratory Phases in a Breath Sound and Their Subsequent Utilization in a Diagnosis

Abstract

Detection of lung sounds and their propagation is a powerful tool for analysing the behaviour of the respiratory system. A common approach to detect the respiratory sounds is lung auscultation, however, this method has significant limitations including low sensitivity of human ear or ambient background noise. This article targets the major limitations of lung auscultation and presents a new approach to analyse the respiratory sounds and visualise them together with the respiratory phases. The respiratory sounds from 41 patients were recorded and filtered to eliminate the ambient noise and noise artefacts. The filtered signal is processed to identify the respiratory phases. The article also contains an approach for removing the noise that is very difficult to filter but the removal is crucial for identifying the respiratory phases. Finally, the respiratory phases are overlaid with the frequency spectrum which simplifies the orientation in the recording and additionally offers the information on the inter-individual ratio of the inhalation and exhalation phases. Such interpretation provides a powerful tool for further analysis of lung sounds, simplifythe diagnosis of various types of respiratory tract dysfunctions, and returns data which are comparable among the patients.

Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone

“…This study evaluates the feasibility of estimating from breathing sounds during walking and running activities. While different previous studies have supported the use of microphones for respiratory monitoring by using built-in smartphones [ 25 , 45 ], headphones [ 29 ], or phonendoscopes [ 28 ], the use of such sensors was limited to controlled environments or stationary conditions (e.g., monitoring during sleep) [ 23 , 24 , 26 ]. It is therefore unclear whether the use of a microphone for monitoring can be extended to other everyday scenarios (such as walking or running).…”

“…The attempts made so far to extract f R from the sound signal have mostly been performed at rest and in conditions where ambient noise was experimentally minimized [ 23 , 24 ]. In these conditions, inhalation and exhalation show characteristic features detectable from the sound respiratory signal [ 25 , 26 , 27 , 28 , 29 ]. These characteristics in the signal are attenuated when these devices are used during exercise, where extracting f R from the breathing signal is more challenging due to breathing-unrelated sounds generated by the athlete’s movements and environmental noise, among others.…”

An Acoustic System of Sound Acquisition and Image Generation for Frequent and Reliable Lung Function Assessment

“…As 3M Littmann and Thinklabs One can only provide a single data point, the breathing phase was used to synchronize [23], [37] and form an array of lung sound signals and converts the lung sound signals into acoustic imaging (see Fig. 13), similar to the typical acoustic imaging systems [15], [20], [21].…”

“…The respiratory signal synchronization technique from the literature [37] has demonstrated the detection of respiratory phases with an accuracy up to 0.2s in lung signals recordings containing minimal noise or significant noise interference. A 5 th order FIR bandpass filter [37], consisting of a 1400 Hz low-pass cutoff frequency and a 150 Hz high-pass cutoff frequency is first applied to the digital stethoscope raw sound signals to improves detection of lower airway sounds by further eliminating aliasing, muscle, heart, and other low-frequency sounds. The identification of respiratory phases is based on the observation that louder sounds occur during transitions between phases.…”

“…The identification of respiratory phases is based on the observation that louder sounds occur during transitions between phases. To detect these transitions more easily [37], an envelope of the signal is calculated using the Hilbert transform. The Hilbert transform generates a version of the input signal with a 90-degree phase shift, allowing computation of the signal's envelope while retaining the original time distribution.…”