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Biomedical subjects

M Vallverdú

Publications and source records attributed to M Vallverdú.

10 recordsLinked to original sources
PubMed2006

Hidden Markov models based on symbolic dynamics for statistical modeling of cardiovascular control in hypertensive pregnancy disorders.

Discrete hidden Markov models (HMMs) were applied to classify pregnancy disorders. The observation sequence was generated by transforming RR and systolic blood pressure time series using symbolic dynamics. Time series were recorded from 15 women with pregnancy-induced hypertension, 34 with preeclampsia and 41 controls beyond 30th gestational week. HMMs with five to ten hidden states were found to be sufficient to characterize different blood pressure variability, whereas significant classification in RR-based HMMs was found using fifteen hidden states. Pregnancy disorders preeclampsia and pregnancy induced hypertension revealed different patho-physiological autonomous regulation supposing different etiology of both disorders.

Algorithms↗
PubMed2005

Optimized symbolic dynamics approach for the analysis of the respiratory pattern.

Traditional time domain techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this paper, the respiratory pattern variability is analyzed using symbolic dynamics. A group of 20 patients on weaning trials from mechanical ventilation are studied at two different pressure support ventilation levels, in order to obtain respiratory volume signals with different variability. Time series of inspiratory time, expiratory time, breathing duration, fractional inspiratory time, tidal volume and mean inspiratory flow are analyzed. Two different symbol alphabets, with three and four symbols, are considered to characterize the respiratory pattern variability. Assessment of the method is made using the 40 respiratory volume signals classified using clinical criteria into two classes: low variability (LV) or high variability (HV). A discriminant analysis using single indexes from symbolic dynamics has been able to classify the respiratory volume signals with an out-of-sample accuracy of 100%.

Algorithms↗
PubMed2004

Variability analysis of the respiratory volume based on non-linear prediction methods.

This work proposed and studied a method of automatically classifying respiratory volume signals as high or low variability by means of non-linear analysis of the respiratory volume. The analysis used volume signals generated by the respiratory system to construct a model of its dynamics and to estimate the quality of the predictions made with the model. Different methods of prediction evaluation, prediction horizons and embedding dimensions were also analysed. Assessment of the method was made using a database that contained 40 respiratory volume signals classified using clinical criteria into two classes: low or high variability. The results obtained using the method of surrogate data provided evidence of non-linear determinism in the respiratory volume signals. A discriminant analysis carried out using non-linear prediction variables classified the respiratory volume signals with an accuracy of 95%.

Humans↗
PubMed2004

Estimating respiratory pattern variability by symbolic dynamics.

OBJECTIVES: The traditional techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this study the respiratory pattern variability was analyzed using symbolic dynamics. METHODS: A group of 20 patients on weaning trials from mechanical ventilation were studied at two different pressure support ventilation levels. Breath duration (T(TOT)) time series and the relation T(I)/T(TOT), that contains the influence of inspiratory time (T(I)), were considered. Length-3 words and 3 different symbols were proposed. The incidence of the overlapping tau and the parameter alpha were analyzed. RESULTS: From the breath duration time series, the distribution of words with probability of occurrence higher than 6% was concentrated on one word for low respiratory variability, whereas high variability was characterized by 4 words, presenting a statistically significant difference (p </= 0.0005). The probability occurrence of words "110" and "111" was also significantly different (p</= 0.0005) when comparing both variabilities. CONCLUSION: The analysis carried out obtained discriminant functions able to correctly classify all the testing set series. These results permit the consideration of symbolic dynamics as a promising methodology to study the respiratory pattern variability.

Data Interpretation, Statistical↗
PubMed2000

Time-frequency analysis of the RT and RR variability to stratify hypertrophic cardiomyopathy patients.

The RT interval is a measure of the ventricular repolarization and is partially influenced by the sympathovagal balance. The analysis of the variation of the duration of the RT and RR intervals might bring new information about the arrhythmogenic vulnerability and autonomic imbalance. The RR signal and its spectral density (SD) are characterized by two different patterns during the sleep period. On the basis of this information, RT and RR sequences have been automatically classified into two patterns, R and N. In this work, we propose a methodology to define new variables that are able to distinguish patients with hypertrophic cardiomyopathy (HCM) who later developed sudden cardiac death (SCD) from HCM patients without such episode during the follow-up. These variables are based on the instantaneous frequency calculation using time-frequency representation of the RT and RR signals previously classified into R and N patterns. In this study, three spectral bands have been considered: low-frequency band (LF, 0-0.07 Hz), mid-frequency band (MF, 0.07-0.15 Hz), and high-frequency band (HF, 0.15-0.45 Hz). Then a suitable combination of mean energy and mean frequency of the RT and RR signals in the MF and HF bands has allowed HCM patients with SCD to be discriminated from HCM patients without SCD (P < 0.001).

Adult↗
PubMed1999

[Community-acquired pneumonia: impact of the use of a therapeutic strategy based on probability of short-term mortality].

BACKGROUND: To assess the impact of the use of a therapeutic strategy based on classifying patients with community-acquired pneumonia (CAP) according to the probability of short-term mortality. PATIENTS AND METHODS: During one year, all patients admitted to the Emergency Department with diagnosis of CAP were included. Clinicians were invited to treat patients according to a recently published protocol that stratifies patients into five categories (from low to high-risk mortality): patients assigned to class 1 were managed at home; patients included in classes 2 and 3 were assigned to a short-time period at emergency department before managed at home; and patients assigned to classes 4 and 5 were hospitalized. RESULTS: The final population analyzed included 101 patients. The rate of acceptability among clinicians was 96.7%. Patients were classified by the following terms: risk-class 1: 17 (16.8%); risk-classes 2 and 3: 40 (39.7%); risk-classes 4 and 5: 44 (43.6%). During follow-up, of the 57 non-hospitalized patients, 3 (5.2%) were subsequently admitted to hospital and 7 (12.2%) patients initially assigned to a short-time period at emergency department were hospitalized, and 1 (1.7%) of them died. By this program, the reduction of the hospitalization rate was 23.8%. CONCLUSION: A strategy of management of CAP based on a prognostic classification has a good safety and acceptability among clinicians, and reduces the rate of hospitalizations.

Adolescent↗
PubMed1998

Mixed quantitative/qualitative modeling and simulation of the cardiovascular system.

The cardiovascular system is composed of the hemodynamical system and the central nervous system (CNS) control. Whereas the structure and functioning of the hemodynamical system are well known and a number of quantitative models have already been developed that capture the behavior of the hemodynamical system fairly accurately, the CNS control is, at present, still not completely understood and no good deductive models exist that are able to describe the CNS control from physical and physiological principles. The use of qualitative methodologies may offer an interesting alternative to quantitative modeling approaches for inductively capturing the behavior of the CNS control. In this paper, a qualitative model of the CNS control of the cardiovascular system is developed by means of the fuzzy inductive reasoning (FIR) methodology. FIR is a fairly new modeling technique that is based on the general system problem solving (GSPS) methodology developed by G.J. Klir (Architecture of Systems Problem Solving, Plenum Press, New York, 1985). Previous investigations have demonstrated the applicability of this approach to modeling and simulating systems, the structure of which is partially or totally unknown. In this paper, five separate controller models for different control actuations are described that have been identified independently using the FIR methodology. Then the loop between the hemodynamical system, modeled by means of differential equations, and the CNS control, modeled in terms of five FIR models, is closed, in order to study the behavior of the cardiovascular system as a whole. The model described in this paper has been validated for a single patient only.

Central Nervous System↗
PubMedDate not supplied

Spectral analysis of heart period variance (HPV)--a tool to stratify risk following myocardial infarction.

The purpose of this study was to contribute to the improvement of stratification of post-myocardial infarction patients at increased risk of malignant ventricular arrhythmia (MVA). Power spectral analysis of heart period variability (HPV) was used as a non-invasive tool to assess cardiac autonomic control. Three groups were used: (1) post-myocardial infarction patients with MVA; (2) post-myocardial infarction patients without MVA; and (3) a control group without heart disease. Spectral analysis of HPV (AR model) was performed on four minute long RR-interval time series derived from consecutive hours of Holter ECG. Significant decrease of powers of mid-frequency (MF) (70-150 mHz) and high-frequency (HF) (150-450 mHz) spectral components of HPV was obtained in Group 1 as compared to Group 2 (p = 0.001 and p = 0.02, respectively). There were no significant differences between groups concerning the power of low frequency (LF) (10-70 mHz) component HPV, spectra of patients in Group 1 were dominated by a single low frequency spectral peak (with a central frequency of 37 mHz). The relative power was computed as the percentage of power in each of the above (HF, MF, LF) components related to the total spectral power. Highly significant differences (p = 0.04) were obtained between Group 1 and Group 2 concerning relative powers of MF and LF components as well as LF/MF ratio. The above method appeared to be highly sensitive in differentiating patients with increased risk of MVA.

Adult↗