Acoustical signal properties for cardiac/respiratory activity and apneas.
Traditionally, auscultation is applied to the diagnosis of either respiratory disturbances by respiratory sounds or cardiac disturbances by cardiac sounds. In addition, for sleep apnea syndrome diagnosis, snoring sounds are also monitored. The present study was aimed at synchronous detection of all three sound components (cardiac, respiratory, and snoring) from a single spot. The sounds were analyzed with respect to the cardiorespiratory activity, and to the detection and classification of apneas. Sound signals from 30 subjects including 10 apnea patients were detected by means of a microphone connected to a chestpiece which was applied to the heart region. The complex nature of the signal was investigated using time, spectral, and statistical approaches, in connection with self-defined time-based and event-based characteristics. The results show that the obstruction is accompanied by an increase of statistically relevant spectral components in the range of 300 to 2000 Hz, however, not within the range up to 300 Hz. Signal properties are discussed with respect to different breathing types, as well as to the presence and the type of apneas. Principal component analysis of the event-based characteristics shows significant properties of the sound signal with respect to different types of apneas and different patient groups, respectively. The analysis reflects apneas with an obstructive segment and those with a central segment. In addition, aiming for an optimum detection of all three sound components, alternative regions on the thorax and on the neck were investigated on two subjects. The results suggest that the right thorax region in the seventh intercostal space and the neck are optimal regions. It is concluded that for patient assessment, extensive acoustic analysis offers a reduction in the number of required sensor components, especially with respect to compact home monitoring of apneas.
Kaniusas
Eugenijus
E
Institute of Fundamentals and Theory of Electrical Engineering, Bioelectricity and Magnetism Laboratory, University of Technology, Vienna, Austria. kaniusas@tuwien.ac.at
Pfützner
Helmut
H
Saletu
Bernd
B
D000465:Algorithms; D001314:Auscultation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006347:Heart Sounds; D006801:Humans; D007140:Immunoglobulin Fab Fragments; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D017286:Polysomnography / Q000379:methods; D015203:Reproducibility of Results; D012135:Respiratory Sounds; D012680:Sensitivity and Specificity; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology*; D013018:Sound Spectrography / Q000379:methods*
D007140:Immunoglobulin Fab Fragments; C050199:digoxin antibodies Fab fragments