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L T Mainardi

Publications and source records attributed to L T Mainardi.

16 recordsLinked to original sources
PubMed2001

Advanced spectral methods for detecting dynamic behaviour.

The traditional analysis in the frequency domain of cardiovascular variability signals requires stationarity along the considered temporal window, in order to obtain reliable indicators of the sympatho-vagal balance (low frequency (LF) and high frequency (HF) power and frequency, and LF/HF ratio). Through proper advanced algorithms of signal processing, it is possible to implement methods that allow the enhancement of important parameters about the behaviour of the system under investigation in the time and frequency domain. Both non-parametric and parametric time-frequency methods are generally employed at this purpose. Among them, Wigner-Ville Distribution and Time-Variant Autoregressive models are here described. Through such advanced methods of signal processing, it is possible to investigate the dynamic properties of the spectral parameters during transient physiological or pathological episodes, after a proper validation using simulated signals. The methods are used in various applicative areas of interest where the spectral parameters present a significant change in time and where the classical spectral analysis cannot be correctly applied. A few significant cases will be discussed such as tilting manoeuvre, vaso-vagal syncope onset and progression, and acute ischemic episodes. Further, multivariate analysis can be applied in which the focus is on squared coherence function and phase relationships, in order to estimate some possible causal effects in different experimental conditions. It is believed that such advanced methods of time-variant or time-frequency approaches are capable of overcoming the problem of stationarity in classical spectral analysis and to make applicable frequency domain techniques in the study of transient episodes which generally characterise various physiological and clinical conditions.

Autonomic Nervous System↗
PubMed2001

Linear and non-linear analysis of atrial signals and local activation period series during atrial-fibrillation episodes.

Linear and non-linear indexes for the characterisation of the dynamics in atrial signals (AS) and local atrial period (LAP) series are assessed in different atrial fibrillation (AF) episodes as defined by Wells. Parameters include the linear index obtained from the cross-correlation function (CCF) between ASs and the non-linear synchronisation (S) index based on the mutual corrected conditional entropy (MCCE). Regularity (R) was computed on single-lead AS. In addition, the level of predictability (LP) and the regularity of LAP series were computed. It was found that the level of synchronisation between ASs decreased passing from type-I to type-II AF when using linear (CCF: 0.90 +/- 0.10 against 0.44 +/- 0.18; p<0.001) and non-linear (S: 0.22 +/- 0.10 against 0.05 +/- 0.03; p<0.001) indexes. The regularity index (in normal sinus rhythm (NSR): 0.30 +/- 0.08; in AF-I: 0.19 +/- 0.10; in AF-II: 0.09 +/- 0.02; NSR against AF-I p<0.001; AF-I against AF-II p<0.001) and level of predictability (in NSR: 65 +/- 18; in AF-I: 27 +/- 13; in AF-II 7 +/- 6; NSR against AF-I p<0.001; AF-I against AF-II p<0.001) significantly decreased in the LAP series passing from NSR to AF-II. The proposed parameters succeeded in discriminating the different dynamics which characterised AS and LAP series during different kinds of AF episodes.

Aged↗
PubMed2001

Autonomic function and baroreflex sensitivity during angiotensin-converting enzyme inhibition or angiotensin II AT-1 receptor blockade in essential hypertensive patients.

OBJECTIVE: The influence of ACE-inhibition and angiotensin II ATI receptor blockade on the autonomic function and baroreflex sensitivity was investigated in hypertension. METHODS AND RESULTS: Heart rate variability was assessed in a resting condition by power spectrum analysis to evaluate the low frequency (LF) power, high frequency (HF) power and LF/HF ratio in 19 hypertensive patients and 23 normotensive controls. Moreover, the coherence between the tachogram and the systogram was evaluated, and the baroreflex gain (alphaLF-index), describing the transfer function of variability in the systolic pressure signal to variability in the RR interval, was obtained. Then a 24-h ambulatory blood pressure monitoring was performed. The 19 hypertensive patients were randomized to either enalapril or losartan treatment, and after 2 months were re-submitted to the RR variability and baroreflex study and to blood pressure monitoring. The subjects then crossed to the other antihypertensive treatment and were re-evaluated after an additional two months. No significant difference was found either in LF power and HF power and LF/HF ratio between normotensive and hypertensive subjects whereas a slight though significant difference was observed in the alphaLF-index. In hypertensive patients, both the treatments with enalapril and losartan reduced blood pressure and had no effect on heart rate. No significant change was observed in autonomic balance or in baroreflex sensitivity during the two antihypertensive treatments. CONCLUSIONS: In hypertensive patients, the angiotensin system or bradykinins do not seem to have any modulatory effect on the sympathetic/parasympathetic control of blood pressure and baroreflex sensitivity, in a resting condition. Since heart rates were unchanged by the two antihypertensive treatments despite a significant reduction of blood pressure, a resetting of baroreflex function was observed during both ACE-inhibition and angiotensin II ATI receptor blockade.

Adult↗
PubMed2001

Discrimination of atrial rhythms by linear and non-linear methods.

The discrimination among atrial rhythms is obtained through the analysis of linear and non-linear dynamics of atria electrograms and local atrial period (LAP) series. Linear and non-linear degree of coupling between atrial electrograms have been assessed using cross-correlation (CCI) and synchronization indexes. Dynamics of LAP series were investigated using level of predictability (LP) and regularity indexes. Atrial fibrillation (AF) episodes, classified according to Wells' criteria, were investigated. We found that metrics obtained from LAP series provide the best performance in the classification of AF rhythms. The LP index misclassified only 12 AF episodes over 87. Synchronization index was the most performing metric in the discrimination between organized and not-organized fibrillation: detection of organized rhythm; sensitivity (SE) > 95%; positive predictability (+P) > 95%. LP (SE: 89%; +P: 89%) and CCI (SE: 87%; +P: 83%) provide slightly lower performances.

Atrial Fibrillation↗
PubMed2000

Single sweep analysis of event related auditory potentials for the monitoring of sedation in cardiac surgery patients.

Event-related potentials (ERPs) from the auditory system were investigated in 28 post-operative cardiac patients in order to assess their relevance in the monitoring of patient sedation level. Midazolam (17 patients) and propofol (11 patients) were the sedative agents used. The auditory ERP components of N100 (HAB100) and mismatch negativity (MMN) were considered. A single sweep method based on the AutoRegressive with eXogenous input (ARX) model, which is able to enhance the evoked responses to each single stimulus, was used to process each sweep and to compute traditional parameters on a sweep-by-sweep basis. Differences in the measured parameters were related to variations in the patient sedation levels classified through Ramsay score. Significant differences (P<0.05) in both MMN and HAB100 parameters were found between light sedation (LS) and deep sedation (DS) levels.

Aged↗
PubMed2000

Event-related brain potentials: laplacian transformation for multichannel time-frequency analysis.

During a visual-motor task the movement strategies and the learning processes are investigated. A group of 10 normal young volunteers underwent the experiment. The EEG signal was recorded through the 10-20 acquisition system during the execution of a task after a visual input. Each subject repeated the movement several times in three different conditions: i) without knowledge of the performance; ii) with visual feedback; iii) with knowledge of the result. The signal was transformed through Laplacian operator in order to eliminate the spurious coherence and then time-variant coherence was calculated. Different trends of the coherence function have been evidenced in subjects learning and not learning the better movement strategy. In particular, relations have been found between frontal, central and occipital electrodes in medium and high frequency ranges.

Adult↗
PubMed1999

Autonomic function and baroreflex sensitivity during a normal ovulatory cycle in humans.

OBJECTIVE AND DESIGN: Possible variations occurring in the autonomic function during a normal ovulatory cycle have been poorly investigated and it is not known whether the baroreflex sensitivity may change according to the different phases of the cycle. The aim of this study was to evaluate heart rate variability (power spectrum analysis), and baroreceptor sensitivity (alpha-low frequency coefficient) in 13 young women with regular menses during the two phases of the cycle (phase I: 5 +/- 1 days and phase II: 23 +/- 3 days from bleeding). RESULTS: The low frequency/high frequency ratio was significantly higher in the second phase of the cycle (phase I: 2.8 +/- 2.6; phase II: 5.6 +/- 5.8, p < 0.05), in the presence of increased low frequency and reduced high frequency expressed in normalized units-nu-(phase I: 59.8 +/- 16.7 nu and 32.6 +/- 15.6 nu for low frequency and high frequency, respectively; phase II: 67.7 +/- 18 nu and 25.7 +/- 17.5 nu for low frequency and high frequency, respectively; p < 0.05). The alpha-low frequency coefficient, index of baroreflex sensitivity, did not statistically differ in the two phases (phase I: 10.6 +/- 4.5 msec/mm Hg; phase II: 8.9 +/- 4.9 msec/mm Hg; ns). CONCLUSIONS: The power spectrum analysis suggests that the autonomic function may be affected by the ovulatory cycle, sympathetic activation being relatively increased in the second phase. On the contrary, the baroreceptor function seems to be scarcely influenced by the two phases of the menstrual cycle.

Adult↗
PubMed1997

Multivariate time-variant identification of cardiovascular variability signals: a beat-to-beat spectral parameter estimation in vasovagal syncope.

In this paper a bivariate, time-variant model able to continuously measure the mutual interactions between heart rate and systolic blood pressure variability signals is presented. A recursive identification of the model parameters makes it possible to estimate, on a beat-to-beat basis, spectral low-frequency (LF) and high-frequency (HF) power, (LF/HF ratio) and cross-spectral (coherence and phase relationships between spectral peaks) indexes during nonstationary events. These indexes can be helpful in: 1) physiological study of autonomic nervous system mechanisms of cardiovascular control and 2) quantification and clinical evaluation of the neural and mechanical links between the two signals. In addition, an estimate of baroreceptive activation (alpha-gain) is continuously extracted. Before applying the model to cardiovascular signals, the reliability of the estimated parameters was tested on simulated signals. Subsequently, the model was applied to investigating vasovagal syncope episodes, aiming at the assessment of autonomic nervous system status and autonomic role in the dynamic phenomena which lead to syncope. The proposed model, which provides noninvasive beat-to-beat evaluation of the autonomic events, may be useful in the description of the syncopal episodes and in the comprehension of the complex physiological mechanisms of syncope.

Algorithms↗
PubMed1997

The whale forgetting factor in recursive AR spectral analysis of heart rate variability signals.

Spectral parameters extracted from the heart rate variability signal are obtained on a beat-to-beat basis by means of autoregressive recursive identification. In this paper a whale forgetting window is introduced, instead of the classical exponential one, in order to reduce the noise influence on the estimated parameters. After proper simulation it was found that the whale forgetting window markedly reduces the noise in the identification, but maintains a good response to abrupt changes in the signal. The algorithm was thus applied to the analysis of the HRV data recorded during different transient situations in physiological and pathological conditions. The spectral parameters were obtained on a beat-to-beat basis and their trends were smoother and more accurate with respect to the traditional exponential window also in presence of noise or artifacts in the time series (sudden and short time changes, ectopic beats, etc.), without losing the signal variations of physiological interest.

Adult↗
PubMed1996

Clinical evaluation of algorithms for ST measurement during exercise test.

HYPOTHESIS: Computer processing of the exercise electrocardiogram (ECG) has many advantages, but the reliability of the analysis algorithms is not easily evaluable. No standard annotated database, nor recommended practice for testing and reporting performance results is available: thus, performance evaluation of such devices can be accomplished only by using a set of unannotated recordings, obtained in clinical practice. We evaluated the accuracy of an original microcomputer-based exercise test analyzer comparing the ST computer output with the measurements obtained by two experienced cardiologists. METHODS: Six hundred ECG strips were randomly selected from the exercise test recordings of 60 patients. The ST shift (at J + 80 ms) was blindly assessed by two observers (with the aid of a calibrated lens) and compared with computer measurements. Correlation coefficients, linear regression equations, percent of discrepant measurements, and 95% confidence limits of the mean error were calculated for all leads, peripheral leads, precordial leads, and "stress-test" leads (II, III, aVF, V4, V5, V6). RESULTS: The computer did not analyze five samples on a total of 600 (0.83%) ECG strips because of excessive noise or signal loss, while 51 (8.5%) were considered unreadable by both observers and 67 (11.2%) were rejected by at least one observer. Correlation between the measurements taken by computer and observer(s) measurements was statistically significant (p < 0.001 for all lead groups), no systematic measurement bias was found, and the mean difference was lower than human eye resolution. CONCLUSIONS: Our algorithms provide results as good as those provided by trained cardiologists in measuring ST changes occurring during exercise test. However, this study did not evaluate whether computer improvement of the signal-to-noise ratio would allow accurate measurements even on cardiologists' uninterpretable ECG. This potential advantage of computer-assisted analysis could be assessed only by using a dedicated exercise test database, in which different patterns of noise are superimposed on noise-free recordings previously annotated for ST level.

Adult↗
PubMed1996

Assessment of heart rate variability changes during dipyridamole infusion and dipyridamole-induced myocardial ischemia: a time variant spectral approach.

OBJECTIVES: We sought to evaluate changes in RR interval variability during dipyridamole infusion and dipyridamole-induced myocardial ischemia. BACKGROUND: Myocardial ischemia and the autonomic nervous system can be mutually interdependent. Spectral analysis of RR interval variability is a useful tool in assessing autonomic tone. METHODS: We used a time variant autoregressive spectral estimation algorithm that could extract spectral variables even in the presence of nonstationary signals. Two groups were considered: group A (patients with ischemia, n = 15) with effort or mixed angina, angiographically assessed coronary artery disease and positive exercise and dipyridamole echocardiographic test results, and group B (control subjects, n = 10) with normal exercise and dipyridamole echocardiographic test results. We investigated the following variables: RR interval mean and variance, low frequency (LF) and high frequency (HF) power in normalized units, LF ratio (LF/LFbasal power), HF ratio (HF/HFbasal power) and LF/HF ratio. For each test epoch, we calculated for group A and group B the mean value +/- SE of all indexes considered. Differences due to an effect either of group (ischemic vs. control) or of time (including both drug and ischemia effects) were analyzed by using analysis of variance for repeated measurements. RESULTS: Dipyridamole injection was characterized by a reduction of all spectral components in negative test. The LF ratio was the only variable able to discriminate patients with ischemia from control subjects (p < 0.05), whereas a time effect was evident for both mean RR interval and high frequency power in normalized units (p < 0.05). The LF ratio decreased in group B from 1 +/- 0.00 (basal) to 0.31 +/- 0.22 (peak), and increased in group A from 1 +/- 0.00 to 15.41 +/- 6.59, respectively. Results of an unpaired t test comparing the peak values of the two groups were also statistically significant (p < 0.01). CONCLUSIONS: Our data show that time variant analysis of heart rate variability evidences an increase in the low frequency ratio that allows differentiation of positive from negative test results, suggesting that the electrocardiogram may contain ischemia information unrelated to ST-T variations, even if their enhancement requires a more complex data processing procedure.

Aged↗
PubMed1995

Pole-tracking algorithms for the extraction of time-variant heart rate variability spectral parameters.

Various algorithms of autoregressive (AR) recursive identification make it possible to evaluate power spectral distribution in correspondence with each sample of a time series, and time-variant spectral parameters can be calculated through the evaluation of the pole positions in the complex z-plane. In traditional analysis, the poles are obtained by zeroing the denominator of the model transfer function, expressed as a function of the AR coefficients. In this paper, two algorithms for the direct updating and tracking of movements of poles of an AR time-variant model on the basis of the innovation given to the coefficients are presented and investigated. The introduced algorithms are based upon 1) the classical linearization method and 2) a recursive method to compute the roots of a polynomial, respectively. In the present paper, applications in the field of heart rate variability (HRV) signal analysis are presented and efficient tools are proposed for quantitative extraction of spectral parameters (power and frequency of the low-frequency (LF) and high-frequency (HF) components) for the monitoring of the action of the autonomic nervous system in transient patho-physiological events. These computational methods seem to be very attractive for HRV applications, as they inherit the peculiarity of recursive time-variant identification, and provide a more immediate comprehension of the spectral process characteristics when expressed in terms of poles and AR spectral components.

Algorithms↗
PubMed1994

On-line beat-to-beat monitoring of spectral parameters of heart rate variability signal using a pole-tracking algorithm.

Spectral parameters extracted from the heart rate variability (HRV) signal are obtained on a beat-to-beat basis, following a procedure which uses two recursive algorithms. In the first step of the procedure the set of the AR model coefficients is updated each time a new RR value is available. Then from the estimated AR model parameters, the new position of the poles of the model transfer function in the complex z-plane is evaluated and, finally, through a residual calculation, it is possible to calculate the spectral parameters which quantify the control of the autonomic nervous system in assessing the cardiac frequency (i.e., power and frequency of LF and HF components). The whole procedure has first been tested on a simulated time series, in order to evaluate its performance in tracking the dynamic changes during different conditions; next the algorithms were employed in the study of the HRV signal for continuous monitoring of non-stationary conditions.

Algorithms↗