Frequency and time domain analysis of airflow breath patterns in patients with chronic obstructive airway disease

Shimon Abboud*, Israel Bruderman, Dror Sadeh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Airflow patterns from patients with chronic obstructive airway diseases (COAD) and normal subjects were analyzed using time and frequency domain analysis. Data were recorded during tidal breathing with a pause between the breaths, digitized at 320 samples per second (10-bit resolution), and processed with a CDC 6600 computer. The appearance of high-frequency components (10-20 Hz) in the time domain waveform and the spectral curve in the power spectrum were studied. One complete waveform was taken as a reference signal and all subsequent waves were analyzed using the cross-correlation function which was employed via the cross spectrum and the fast Fourier transform algorithm. The energy content from the averaged spectrum and the root mean square (RMS) value from the filtered waveforms were calculated. Our study indicated that the RMS and the power content estimated from a part of the filtered wave (10-20 Hz) which included the time interval from the peak of the expiratory flow (tE) to the end of the flow curve (tN) were significantly greater in normal subjects (n = 13; 0.86 ± 0.30 × 10-2 l/s; P < 0.00005 for RMS value, and 0.76 ± 0.32 l/s; P < 0.00005 for the power content) than in patients with chronic airways obstruction (n = 19; 0.40 ± 0.13 × 10-2 l/s; for RMS value and 0.35 ± 0.16 l/s; for the power content). It is concluded that the RMS and the power values of the filtered flow curve during tidal breathing over the time interval tE-tN can detect chronic airway obstruction.

Original languageEnglish
Pages (from-to)266-273
Number of pages8
JournalComputers and Biomedical Research
Issue number3
StatePublished - Jun 1986


Dive into the research topics of 'Frequency and time domain analysis of airflow breath patterns in patients with chronic obstructive airway disease'. Together they form a unique fingerprint.

Cite this