A proof of concept study of acoustic sensing of lung recruitment during mechanical ventilation. (February 2017)

• Record Type: Journal Article
• Authors: Rodgers, Geoffrey W.; Lau Young, Jade B.; Desaive, Thomas; Shaw, Geoffrey M.; Chase, J. Geoffrey
• Is Part Of: Biomedical signal processing and control. Volume 32(2017)
• Page Start: 130
• Page End: 142
• Publication Date: 2017-02
• Subjects: Biomedical systems -- Acoustic monitoring -- Signal analysis -- Frequency spectrum -- Acoustic emissions -- Medical research -- Medical systems -- Patient testing; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.08.021
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Acoustic recording are made of lung recruitment via a recording stethoscope. Event detection algorithms are developed to detect regions of crackles. The event detection algorithms are benchmarked against each other. The results are evaluated against detection by an experienced clinician. A proof-of-concept is presented to use acoustic indicators of lung recruitment. Abstract: Advancements in health technologies are crucial to support healthcare professionals, improve patient outcomes, and best utilize increasingly scarce and under-demand healthcare resources. This research presents an initial proof-of-concept study of simple, non-invasive monitoring techniques used in Mechanical Ventilation (MV), which is the primary therapy for Acute Respiratory Distress Syndrome (ARDS). The high levels of inter-patient variability seen in patients with ARDS have resulted in much speculation about the ideal method of determining ventilation settings, such as tidal volume (Vt) and Positive End Expiratory Pressure (PEEP). One of the oldest and simplest methods is acoustic sensing of recruitment and lung condition. This project involves using a digital recording stethoscope to monitor the acoustic output of patients in the Intensive Care Unit (ICU) during mechanical lung ventilation. During lung recruitment, 'crackles' can be heard within the chest cavity with a stethoscope. These crackles vary significantly, depending on the status of the patient's respiratory system and are used as an indicator of the level of alveolar recruitment. This preliminary, proof-of-concept study focused on crackle detection and involved gathering sound samples from patients in the Christchurch Hospital ICU with evidence of crackles in the chest cavity. Frequency based analysis showed that crackles can be detected as emissions with higher power levels between 100 and 300 Hz (subject to patient variability). The ability to non-invasively record, detect and quantify the intensity of crackles could provide immediate feedback to clinicians and, in the long term, aid in the optimization of ventilator therapy.