A multi-channel device for respiratory sound data acquisition and transient detection

Area of Science:

• Biomedical Engineering
• Respiratory Medicine
• Signal Processing

Background:

• Accurate detection and localization of adventitious pulmonary sounds are crucial for diagnosing respiratory conditions.
• Existing methods for respiratory sound analysis can be limited in their ability to precisely identify and map abnormal lung sounds.

Purpose of the Study:

• To design and implement a multi-channel analog data acquisition and processing device for respiratory sounds.
• To develop an algorithm for detecting and locating adventitious pulmonary sounds, such as crackles and wheezes.
• To synchronize pulmonary sound data with airflow measurements for improved respiratory cycle analysis.

Main Methods:

• A system was developed comprising fourteen microphones, an airflow measuring unit, a fifteen-channel amplifier/filter unit, and a data acquisition (DAQ) card connected to a PC.
• Respiratory sound data were amplified and band-pass filtered, while airflow data were low-pass filtered.
• A wavelet decomposition algorithm was implemented for the detection of adventitious pulmonary sounds.

Main Results:

• The system successfully recorded fourteen-channel respiratory sound data and measured airflow.
• The developed algorithm demonstrated the capability to detect adventitious pulmonary sounds, specifically crackles and wheezes.
• The system allows for synchronization of pulmonary signals with the respiration cycle.

Conclusions:

• The designed multi-channel system provides a robust platform for respiratory sound acquisition and processing.
• The wavelet decomposition algorithm effectively detects and can aid in locating adventitious pulmonary sounds.
• This technology has the potential to enhance the mapping and diagnosis of pulmonary conditions.