Monitoring the autonomic nervous system in the ICU through cardiovascular variability signals

Cardiovascular autonomic responses during head-up tilt test in newly diagnosed type 2 diabetes

BACKGROUND

Autonomic dysfunction is commonly observed in patients with long-standing type 2 diabetes. Previous studies have confirmed the value of both subjectively assessed symptoms and objective measurements of autonomic nervous system function in diagnosing cardiovascular autonomic neuropathy. However, the head-up tilt test (HUTT) has been rarely used to investigate cardiovascular autonomic responses in subjects with high risk of newly diagnosed type 2 diabetes (nT2D).

OBJECTIVE

To evaluate autonomic cardiovascular responses through passive orthostatic challenge along the diabetes continuum.

METHODS

The study population was stratified as normoglycemic (n = 16), prediabetes (n = 20), and nT2D (n = 20). The prevalence of orthostatic intolerance and autonomic cardiovascular responses was evaluated with the Task Force Monitor during a 30-min passive HUTT. Spectral indices of heart rate and blood pressure variability and baroreceptor effectiveness index (BEI) were calculated through the HUTT. BEI was obtained by the sequence method.

RESULTS

There were no differences in the prevalence of orthostatic intolerance or in the indices of heart rate and blood pressure variability among the three groups of study. The BEI was attenuated in the nT2D group in supine rest and throughout HUTT compared with normoglycemic and prediabetes groups. The multivariable linear regression analysis showed that BEI was associated with fasting glucose (β =  - 0.52, p  < 0.001) and HbA1c (β =  - 0.57, p  < 0.001) independently of cardiovascular risk factors.

CONCLUSION

Cardiovascular autonomic neuropathy, expressed as blunted BEI, is the only abnormal autonomic nervous test detected in nT2D, and it was independently associated with fasting glucose and HbA1c values.

Quantifying agitation in sedated ICU patients using heart rate and blood pressure

Agitation is a significant problem in the intensive care unit (ICU), affecting 71% of sedated adult patients during 58% of ICU patient-days (Fraser and Riker 2001 NY Health-Syst. Pharm. 20 17-24). Subjective scale-based assessment methods are currently used to assess the level of patient agitation, but are limited in their accuracy and resolution. This research develops an objective agitation measurement method using heart rate variability (HRV), systolic blood pressure (BP) and blood pressure variability (BPV) data, processed by wavelet transforms and autoregressive signal processing. A fuzzy inference system (FIS) is developed to classify changes in these signals that correlate with observed patient agitation, and combine them into a final agitation level. Proof of concept clinical trials on 13 normal subjects and 5 ICU patients has been performed to verify the validity of this approach in comparison with agitation graded by nursing staff using the Riker sedation-agitation scale (SAS). Results show good correlation with medical staff assessment with no false positive results during calm periods. Clinically, this initial agitation measurement method promises the ability to consistently and objectively quantify patient agitation to enable better management of sedation and agitation through optimized drug delivery leading to reduced length of stay.

Quantification of haemodynamic response to auditory stimulus in intensive care

Measuring effects of sensory stimuli on haemodynamics could provide information about the interplay between central and autonomous nervous system (ANS). However, ANS response to sensory stimulus has received little attention. In this paper we present a signal processing scheme to extract the responses of heart rate and systemic arterial pressure on auditory stimulus in intensive care patients (N=5). In short, the effect of mechanical ventilation is rejected by optimal linear modelling. Other disturbances are attenuated by filtering and efficient rejection of outlying sweeps of data. The results show identifiable responses on three out of five cases. The response characteristics may be explained by synchronisation of spontaneous variability in systemic arterial pressure to auditory stimulus.

Automatic ectopic beat elimination in short-term heart rate variability measurement

Our studies deal with fully automatic measurement of heart rate variability (HRV) in short term electrocardiograms. Presently, all existing HRV analysis programs require user intervention for ectopic beat identification, especially of supraventricular ectopic beats (SVE). This makes the HRV measurement in large, e.g. epidemiological studies problematic. In this paper, we present a fully automatic algorithm for the discrimination of the ventricular (VE) and SVE ectopic beats from the normal QRS complexes suited for a reliable HRV analysis. The QRS identification is based on the template matching method. The ectopic beats are identified based on several morphological and timing properties of the electrocardiogram (ECG) signal. The method incorporates several approaches and makes HRV analysis of large numbers of electrocardiograms feasible. It uses the template matching for the basic morphology check of the QRS complex and the P-wave, the timing information to avoid unnecessary ectopic beat checks and to adjust thresholds and it also looks for a special QRS morphology, which is common in VEs. We used a testing set of 69 electrocardiograms selected from a large number of recordings. The selected ECGs contained abnormalities including ectopic beats, right branch bundle block, respiratory arrhythmia, blocked atrial extrasystole, high amplitude and wide T-waves. The evaluation of our method showed a specificity of 0.99, supraventricular ectopic beat sensitivity of 0.99 and ventricular ectopic beat sensitivity of 0.98.