# Detection of Respiratory Phases in a Breath Sound and Their Subsequent Utilization in a Diagnosis
## 4 Discussion  
The results indicate that the quality of the sound recording is very important for a successful and accurate analysis and the detection of the respiratory phases. When the breath sound is recorded with a minimum of undesirable noise, the algorithm is able to detect the phases with an accuracy up to 0.2 s with a minimum adjustment of the parameters. When there is a greater deviation from the accurate identification of the phases, it is mostly caused by the poor quality of the recording or a very slow frequency of breathing. The lowered quality of the recordings is frequently caused by a strong heart sound and/or the phonendoscope membrane.  
Figure 10: Comparing of two relative respiratory curves—healthy person (Patient No. 1) and patient with asthma (Patient No. 2).  
Figure 9: Very chaotic frequency spectrum—Patient No. 2. Recorded at jugular, age 17 years old, with pronounced bronchial obstruction. The vertical black boarder lines are helpful in this case. In the spectrum the wheezes (areas with sharply increased amplitude—some of them marked by white arrow) are very distinct.  
In comparison with the other studies that have targeted the detection of the respiratory phases, the presented approach of the filters and envelope provides a robust, fast and efficient approach with little computational demands. On the one hand, the approach does not require any special accessories such as additional sensors or holders [6, 10], but on the other hand there is a requirement for an additional piece of software and lower reliability of the algorithm while the recording contains a significant amount of noise. Other authors relied on band-pass filters (150-800 Hz or 500-5000 Hz) or VAD instead of low-pass and high-pass filters or included more parameters in the analysis. Unfortunately, this often makes the algorithm more computationally expensive [6, 9, 11]. This can be a very limiting factor as the examiner requires the results immediately after the examination. Recent studies have also highlighted the necessity of detecting the pauses between the inhalation and exhalation [12]. If such a pause existed in the recording, the presented algorithm is not able to detect such a pause and the indication line in the graph is placed in the middle of such a pause. In addition to that, the algorithm is not able to label which phase is the inhalation one and which phase is the exhalation one. Of course, there are other methods to determine the respiratory phases. For example, the methods based on the respiratory gas flow [16]. These methods can be very accurate but the instrumental and personal demands are considerably high.  
The presented approach of the filters and envelope provides a robust and efficient approach with little computational demands. Although the amplitudes of the individual frequencies are biased by the pressure of phonendoscope on participant's body, and therefore cannot be directly compared with other recordings, the introduced approach of the inhaling/exhaling proportion in the curves provides an objective parameter for comparing data between individuals.