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Analysis of cardiopulmonary dynamics using wavelet analysis methods: removing the influence of respiration and prediction of emphysema
Author(s) -
Bartels Matthew N,
Petrock Anne Marie,
Reisman Stanley S,
Rockland Ronald H,
Rebmann Rebecca A,
De Meersman Ronald E
Publication year - 2008
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.22.1_supplement.756.14
Subject(s) - principal component analysis , wavelet , heart rate variability , heart rate , pattern recognition (psychology) , spectral analysis , mathematics , statistics , cardiology , computer science , artificial intelligence , medicine , blood pressure , physics , quantum mechanics , spectroscopy
Respiratory effects are significant in standard frequency analysis of heart rate variability (HRV). This limits evaluation of sympathovagal balance in exercise and other dynamic states. We developed a method using wavelet statistical analyses combined with cluster analysis to reduce this. Methods: Ten emphysema subjects (ES) and 5 matched controls (CS) were evaluated using wavelet entropy (WE) of heart rate data during rest and exercise. The model included wavelet source separation via a discrete wavelet decomposition of heart rate and respiration signals. Data were analyzed blinded, and prediction for group separation (ES versus CS) was performed using cluster analysis. Results: Using WE, the source separation model was able to predict actual classification of individuals with 93% accuracy at rest (4/5 CS, 10/10 ES) and 100% accuracy at exercise. The most significant factors for principal component analysis were identified for resting and exercise states. Conclusion: Novel utilization of wavelet analysis in combination with principal component analysis and wavelet analysis methods can remove the influences of respiration on the time frequency analysis of HRV. Support: VIDDA Foundation