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Biomedical subjects

P Caminal

Publications and source records attributed to P Caminal.

At least 19 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↗
PubMed2002

Influence of estimators of spectral density on the analysis of electromyographic and vibromyographic signals.

Electromyographic (EMG) and vibromyographic (VMG) signals are related to electrical and mechanical muscle activity, respectively. It is known that variations in their frequency components are related to changes in muscle activity and fatigue. The aims of this study were: (1) to analyse the resolution, variance and bias of different estimations of power spectral density function (PSD); and (2) to evaluate the influence of the spectral estimation method on three indices calculated from the PSD of EMG and VMG signals: mean (f(m)) and median (f(c)) frequencies and the ratio of high and low frequency components (H/L ratio) to select the most suitable estimator. Myographic signals were recorded from the sternomastoid muscle, an accessory respiratory muscle, during breathing. For non-parametric methods, Welch periodograms and correlograms were analysed with different windows. Autoregressive (AR) moving average (MA) and ARMA models with different orders were evaluated in the parametric methods. The reproducibility of the results was also studied. Frequency indices, particularly the H/L ratio and f(c), changed considerably when varying the following parameters of the estimators: periodogram with segment durations longer than 150 ms in EMG and with any duration in VMG signals; correlogram with window length shorter than 10% of the total number of samples; and AR models with an order lower than 10, 20 and 40 in f(c), fm and H/L ratio, respectively, in both myographic signals.

Electromyography↗
PubMed2002

Comparison of auditory evoked potentials and the A-line ARX Index for monitoring the hypnotic level during sevoflurane and propofol induction.

BACKGROUND: Extraction of the middle latency auditory evoked potentials (AEP) by an auto regressive model with exogenous input (ARX) enables extraction of the AEP within 1.7 s. In this way, the depth of hypnosis can be monitored at almost real-time. However, the identification and the interpretation of the appropriate signals of the AEP could be difficult to perform during the anesthesia procedure. This problem was addressed by defining an index which reflected the peak amplitudes and latencies of the AEP, developed to improve the clinical interpretation of the AEP. This index was defined as the A-line Arx Index (AAI). METHODS: The AEP and AAI were compared with the Modified Observers Assessment of Alertness and Sedation Scale (MOAAS) in 24 patients scheduled for cardiac surgery, anesthetized with propofol or sevoflurane. RESULTS: When comparing the AEP peak latencies and amplitudes and the AAI, measured at MOAAS level 5 and level 1, significant differences were achieved. (mean(SD) Nb latency: MOAAS 5 51.1 (7.3) ms vs. MOAAS 1: 68.6 (8.1) ms; AAI: MOAAS 5 74.9 (13.3) vs. MOAAS 1 20.7 (4.7)). Among the recorded parameters, the AAI was the best predictor of the awake/anesthetized states. CONCLUSION: We conclude that both the AAI values and the AEP peak latencies and amplitudes correlated well with the MOAAS levels 5 (awake) and 1 (anesthetized).

Anesthesia↗
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↗
PubMed2000

Study of myographic signals from sternomastoid muscle in patients with chronic obstructive pulmonary disease.

Analysis of the respiratory muscle activity is a promising technique for diagnosis of respiratory diseases, such as chronic obstructive pulmonary disease (COPD). The sternomastoid muscle (SMM) was selected to study the activity of respiratory muscles due to its accessibility in order to define a noninvasive analysis. The aims of this work are two: analyze the relationship between the SMM function and pulmonary obstruction, and study the influence of spectral estimator on frequency parameters related with the muscle activity. For the first goal, we propose the analysis of vibromyographic and electromyographic signals from the SMM to study the muscle function during two ventilatory tests. Activity of SMM was found by means of several indexes: root-mean-square (rms) values, mean and median frequencies, and ratio between high and low-frequency components. For the second goal, spectral analysis was performed by means of nonparametric methods: Correlogram and Welch periodogram, and parametric methods: autoregressive (AR), moving average (MA), and ARMA models. It is deduced that these indexes show muscle activity and certain fatigue of the SMM, whose muscle function depends on the level of pulmonary obstruction, and they depend a lot of spectral estimator being the more suitable an AR model with high order.

Aged↗
PubMed2000

[Comparison of an auditory evoked potentials index and a bispectral index versus clinical signs for determining the depth of anesthesia produced by propofol or sevoflurane].

OBJECTIVES: To evaluate an anesthetic depth index (ADI) obtained from auditory evoked potentials and a bispectral EEG index (BIS) in comparison with clinical assessment of anesthetic depth using the modified observer's assessment of awareness/sedation scale (MOAA/SS), for induction of anesthesia with propofol or sevoflurane as the only agent. PATIENTS AND METHODS: The ADI and BIS were recorded simultaneously in this prospective study and compared to the MOAA/SS during the anesthetic induction of 26 adults undergoing elective heart surgery. Assignment of patients to two groups was random. Group A (n = 13) patients were induced with propofol (target dose 5 micrograms.ml-1 in 5 min). Induction in group B (n = 13) was with sevoflurane (8% tidal volume). A scheme of awake-sleeping-awake-sleeping was followed. The means of the two indexes were compared (Mann-Whitney test) one minute before the patient slept (awake) and one minute later (sleeping), and the evolution of the indexes was compared during awake/sleep and sleep/awake phase changes and while the patients were in a stable sleep phase. The sensitivity and specificity of each index was analyzed in function of the MOAA/SS. We also analyzed the time elapsing from the moment the patient fell asleep (MOAA/SS 2) until the two indexes reached published reference values (ADI = 38, BIS = 60). RESULTS: After induction with propofol (group A) the ADI fell to 29.2 +/- 11.7 and the BIS fell to 63.5 +/- 13.4. After induction with sevoflurane (group B) the ADI fell to 33.8 +/- 14.9 and the BIS to 66.8 +/- 15. The ADI value that best discriminated between arousal and sleeping (sensitivity 100%) was 38; the BIS value that best discriminated was 60. The responses to sound in decibels (dB) during "awake/sleeping" and "sleeping/awake" phases were, respectively, -3.8 dB and -4.5 dB for the ADI and -1.5 dB and -0.8 dB for the BIS. With the patient in stable sleep, response to the two indexes was at -0.79 dB. In group A, the ADI detected MOAA/SS 2 significantly earlier (ADI 13.1 +/- 30 s; BIS 56 +/- 36 s; p < 0.05). No patient reported remembering the study period. CONCLUSIONS: Monitoring anesthetic depth with the ADI or BIS was technically easy and effective for detecting whether patients were awake or sleeping. The ADI response was faster and identified awake/sleeping and sleeping/awake phase changes better than did the BIS.

Adult↗
PubMed1999

Identification of causal relations between haemodynamic variables, auditory evoked potentials and isoflurane by means of fuzzy logic.

The aim of this study was to identify a possible relationship between haemodynamic variables, auditory evoked potentials (AEP) and inspired fraction of isoflurane (ISOFl). Two different models (isoflurane and mean arterial pressure) were identified using the fuzzy inductive reasoning (FIR) methodology. A fuzzy model is able to identify non-linear and linear components of a causal relationship by means of optimization of information content of available data. Nine young female patients undergoing hysterectomy under general anaesthesia were included. Mean arterial pressure (MAP), heart rate (HR), end-tidal expired carbon dioxide (CO2ET), AEP and ISOFl were monitored with a sampling time of 10 s. The AEP was extracted using an autoregressive model with exogenous input (ARX model) which decreased the processing time compared with a moving time average. The AEP was mapped into a scalar, termed the depth of anaesthesia index (DAI) normalized to 100 when the patient was awake and descending to an average of 25 during loss of consciousness. The FIR methodology identified those variables among the input variables (MAP, HR, CO2ET, DAI or ISOFl) that had the highest causal relation with the output variables (ISOFl and MAP). The variables with highest causal relation constitute the ISOFl and MAP models. The isoflurane model predicted the given anaesthetic dose with a mean error of 12.1 (SD 10.0)% and the mean arterial pressure model predicted MAP with a mean error of 8.5 (7.8)%.

Anesthetics, Inhalation↗
PubMed1999

Analysis of tracheal sounds during forced exhalation in asthma patients and normal subjects: bronchodilator response effect.

PURPOSE: During the past 10 years, the acoustic analysis of breath sounds has been used as a diagnostic tool in patients suffering from obstructive respiratory diseases. Acoustic analysis might be able to monitor the response to bronchodilator therapy in a clinical setting. So far, few studies have been carried out in asthmatic patients. To assess the responses of a sampling of asthma patients to an inhaled bronchodilator (terbutaline) by means of spectral analysis of the tracheal sound performed during forced expiratory maneuvers. MATERIAL AND METHODS: Seventeen nonsmoking asthma patients (9 were male, 8 were female) who had been suffering from the disease for > or = 15 years were included in the study, as were 15 normal subjects (7 were male, 8 were female). The average age (+/- SD) was 56.5 +/- 15.2 years (FVC, 2.7 +/- 0.9 L [63.4%]; FEV1, 1.5 +/- 0.6 L [53.0%]). The tracheal sounds were collected during three forced expiratory maneuvers with a sampling frequency of 5,000 Hz and were analyzed by applying a 16-parameter autoregressive model. RESULTS: The centroid frequency decreased after the bronchodilator was given at different flow segments between 1.2 and 0.4 L/s, with significant changes between 0.6 and 0.4 L/s. CONCLUSIONS: Patients with asthma showed changes in the spectral acoustic analysis frequencies after the administration of a bronchodilator drug (terbutaline) during forced expiratory maneuvers.

Adolescent↗
PubMed1997

Analysis of abnormal intra-QRS potentials. Improved predictive value for arrhythmic events with the signal-averaged electrocardiogram.

BACKGROUND: Using the signal-averaged ECG (SAECG), this study developed a new electrical index for predicting arrhythmic events: abnormal intra-QRS potentials (AIQP). METHODS AND RESULTS: We studied 173 patients followed after myocardial infarction for a mean duration of 14 +/- 7 months. Sixteen arrhythmic events occurred, defined as sudden cardiac death, documented sustained ventricular tachycardia, or non-fatal cardiac arrest. Noninvasive indices of arrhythmia risk were measured, including AIQP, conventional SAECG, Holter, and left ventricular ejection fraction (LVEF). Abnormal intra-QRS potentials were defined as abnormal signals occurring anywhere within the QRS period. They were estimated with a lead-specific, parametric modeling method that removed the smooth, predictable part of the QRS. AIQPs are characterized by the remaining transient, unpredictable component of the QRS and manifest as low-amplitude notches and slurs. A combined XYZ-lead AIQP index exhibited higher specificity (95%) and predictive value (PV) (+PV, 47%; -PV, 94%) than the conventional SAECG in combination with Holter and LVEF (specificity, 89%; +PV, 25%; -PV, 93%). CONCLUSIONS: AIQP improved specificity and predictive value, compared with conventional tests, for prediction of arrhythmic events. AIQP emerged as the best noninvasive univariate predictor of arrhythmic events after myocardial infarction in this study. A review of several other reports shows that AIQP in the present study outperformed the conventional predictive indices reported in those other data sets.

Arrhythmias, Cardiac↗
PubMed1997

Automatic measurement of corrected QT interval in Holter recordings: comparison of its dynamic behavior in patients after myocardial infarction with and without life-threatening arrhythmias.

This study was designed to determine the value of automatic corrected QT-interval measurement in Holter tapes in patients after myocardial infarction as a marker of life-threatening ventricular arrhythmias. We compared the corrected QT interval, automatically measured in 24-hour Holter recordings, in two groups of patients after myocardial infarction: group I was composed of 14 patients admitted consecutively to our hospital for documented sustained ventricular tachycardia or out-of-hospital cardiac arrest. Group II consisted of 28 patients with previous myocardial infarction with characteristics similar to those of group I, but without malignant ventricular arrhythmias in the follow-up. The global mean 24-hour corrected QT interval was longer in group I (425 +/- 20 msec) than in those patients after myocardial infarction without arrhythmias (group II) (405 +/- 17 msec; p < 0.01). Furthermore, a significant proportion of patients of group I (seven of 14) exhibited more peaks of corrected QT longer than 500 msec compared with patients of group II (two of 28; p < 0.005). A circadian rhythm of corrected QT peaks was observed in group I, having a significantly higher incidence from 11 PM to 11 AM (p < 0.05). We conclude that automatic corrected QT-interval measurement on Holter electrocardiogram is now available and feasible. Our results suggest that this is a marker for risk assessment of life-threatening ventricular arrhythmias. Large-scale trials are needed to confirm these results and to determine the predictive value of this technique for risk stratification.

Algorithms↗
PubMed1997

Analysis of abnormal signals within the QRS complex of the high-resolution electrocardiogram.

This paper presents a new, quantitative approach to measuring abnormal intra-QRS signals, using the high-resolution electrocardiogram (HRECG). These signals are conventionally known as QRS "notches and slurs." They are measured qualitatively and form the basis for the ECG identification of myocardial infarction. The HRECG is used for detection of ventricular late potentials (LP), which are linked with the presence of a reentry substrate for ventricular tachycardia (VT) after a myocardial infarction. LP's are defined as signals from areas of delayed conduction which outlast the normal QRS period. Our objective is to quantify very low-level abnormal signals that may not outlast the normal QRS period. In this work, abnormal intra-QRS potentials (AIQP) were characterized by removing the predictable, smooth part of the QRS from the original waveform. This was represented as the impulse response of an ARX parametric model, with model order selected empirically from a training data set. AIQP were estimated using the residual of the modeling procedure. Critical AIQP parameters to separate VT and non-VT subjects were obtained using discriminant functions. Results suggest that AIQP indexes are a new predictive index of the HRECG for VT. The concept of abnormal intra-QRS potentials permits the characterization of pathophysiological signals contained wholly within the normal QRS period, but related to arrhythmogenesis. The new method may have other applications, such as detection of myocardial ischemia and improved ECG identification of the site of myocardial infarction, particularly in the absence of Q waves.

Electrocardiography↗
PubMed1996

Adaptive estimation of QRS complex wave features of ECG signal by the Hermite model.

The most characteristic wave set in ECG signals is the QRS complex. Automatic procedures to classify the QRS are very useful in the diagnosis of cardiac dysfunctions. Early detection and classification of QRS changes are important in real-time monitoring. ECG data compression is also important for storage and data transmission. An Adaptive Hermite Model Estimation System (AHMES) is presented for on-line beat-to-beat estimation of the features that describe the QRS complex with the Hermite model. The AHMES is based on the multiple-input adaptive linear combiner, using as inputs the succession of the QRS complexes and the Hermite functions, where a procedure has been incorporated to adaptively estimate a width related parameter b. The system allows an efficient real-time parameter extraction for classification and data compression. The performance of the AHMES is compared with that of direct feature estimation, studying the improvement in signal-to-noise ratio. In addition, the effect of misalignment at the QRS mark is shown to become a neglecting low-pass effect. The results allow the conditions in which the AHMES improves the direct estimate to be established. The application is shown, for subsequent classification, of the AHMES in extracting the QRS features of an ECG signal with the bigeminy phenomena. Another application is highlighted that helps wide ectopic beats detection using the width parameter b.

Electrocardiography↗
PubMed1996

Acoustic analysis of snoring sound in patients with simple snoring and obstructive sleep apnoea.

Snoring, a symptom which may indicate the presence of the obstructive sleep apnoea syndrome (OSA), is also common in the general population. Recent studies have suggested that the acoustic characteristics of snoring sound may differ between simple snorers and OSA patients. We have studied a small number of patients with simple snoring and OSA, analysing the acoustic characteristics of the snoring sound. Seventeen male patients, 10 with OSA (apnoea/hypopnoea index (AHI) 26.2 events x h(-1)) and seven simple snorers (AHI 3.8 events x h(-1)), were studied. Full night polysomnography was performed and the snoring sound power spectrum was analysed. Spectral analysis of snoring sound showed the existence of two different patterns. The first pattern was characterized by the presence of a fundamental frequency and several harmonics. The second pattern was characterized by a low frequency peak with the sound energy scattered on a narrower band of frequencies, but without clearly identified harmonics. The seven simple snorers and two of the 10 patients with OSA (AIH 13 and 14 events x h(-1), respectively) showed the first pattern. The rest of the OSA patients showed the second pattern. The peak frequency of snoring was significantly lower in OSA patients, with all but one OSA patient and only one simple snorer showing a peak frequency below 150 Hz. A significant negative correlation was found between AHI and peak and mean frequencies of the snoring power spectrum (p<0.0016 and p<0.0089, respectively). In conclusion, this study demonstrates significant differences in the sound power spectrum of snoring sound between subjects with simple snoring and obstructive sleep apnoea patients.

Acoustics↗
PubMed1995

Karhunen-Loève transform as a tool to analyze the ST-segment. Comparison with QT interval.

The spatial and temporal courses of ventricular repolarization are quite sensitive to the biochemical and biophysiologic environment of the myocardial cells, and are therefore often an early marker of heart disease, particularly of ischemia. The detailed morphology of the surface electrocardiogram contains considerable information about the repolarization process. The ST-segment changes with ischemia, injury, and drugs. The QT interval is affected by drugs, heart rate, and autonomic tone, and in some situations may identify individuals at high risk for arrhythmias and sudden death. Variability in the shape, including duration, of the ST-T waves reflects autonomic nervous system activity and may identify high-risk patients. Automated methods for quantitatively characterizing ST-T complexes are important in studying long-term electrocardiographic records. Two computer-based measurement procedures for characterizing the repolarization period were comparatively analyzed: Karhunen-Loève (KL) transform representation of the ST-T shape and measurement of beat-to beat durations of repolarization (QT intervals). The results of KL transform representation and time-domain QT measurement algorithms for studying the repolarization period of the electrocardiogram on the European ST-T database are presented. It was found that about 20% of the records present a quasiperiodic KL pattern of ischemic ST-T activity and another 20% exhibit repetitive but not clearly periodic patterns of ischemic ST-T changes. From these ischemic records, 50% showed QT variations in at least one lead associated with the ischemic episodes.

Algorithms↗