Spectral analysis of heart rate variability signal and respiration in diabetic subjects

A. Bianchi, B. Bontempi, S. Cerutti, P. Gianoglio, G. Comi, M. G. Natali Sora

Research output: Contribution to journalArticlepeer-review


The paper deals with methods of processing ECG and respiration signals which aim at detecting parameters whose values may be correlated to normal and diabetic subjects with or without cardiovascular autonomic neuropathy (CAN). Beatto-beat R-R duration values of the ECG and discrete series of respiration are obtained from original signals using a recognition algorithm. Power spectrum analysis (autospectra, cross-spectra and coherence via autoregressive modelling) is carried out on segments of about 200 consecutive cardiac cycles. Spectral parameters of the R-R variability signal are obtained as follows: total power, power of low-frequency (LF) and high-frequency (HF) components, power of the signal which is (or is not) coherent with respiration, in absolute or in percentage values. The experimental protocol considers 40 diabetic patients (21 of whom have diabetic neuropathy) and 14 normals in three different conditions: resting, standing and controlled respiration. The developed spectral parameters seem sensitive enough to differentiate between normal and pathological subjects. These parameters may constitute a quantitative means to be edded to the classical diabetic tests for the diagnosis of cardiovascular autonomic neuropathy.

Original languageEnglish
Pages (from-to)205-211
Number of pages7
JournalMedical and Biological Engineering and Computing
Issue number3
Publication statusPublished - May 1990


  • Biological signal processing
  • Diabetic neuropathy
  • Heart rate variability
  • Respiration
  • Sympathetic and parasympathetic nervous systems

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Biomedical Engineering
  • Computer Science Applications
  • Computational Theory and Mathematics


Dive into the research topics of 'Spectral analysis of heart rate variability signal and respiration in diabetic subjects'. Together they form a unique fingerprint.

Cite this