The implementation of Fast Hilbert Transform with low pass filter for reconstructing breathing sound signals in LabView

Abstract

Many studies have stated that the respiration rate is one of the vital signs in individual health. It can be used to identify serious illness. Detecting either the chest movement or the exhausted gases properties using various sensors are the common methods to monitor respiration rate. Apart from both, breathing sound seems also effective for such purpose without requiring any complicated sensing devices. However, attaining respiration rate from the burst-type of breathing sound signals is challenging. Their high variations in frequency, amplitude, and the interval between inhalation and exhalation make the reconstruction of a smoother signal needs extra signals processing effort. This paper proposes a technique to reconstruct breathing sound which is acquired using a built-in headset microphone and implemented using Fast Hilbert Transform with Low Pass Filter in LabView. Through adjusting a proper filter order and cut-off frequency, the result shows that the proposed method is able to derive the representation of breathing sound signals in a smooth curve and convenient form. Compare to the raw ones, this reconstructed signals will make the peak detection easier. It is expected that this sound-based approach will be a cheap and effective solution for further respiration rate monitoring systems.