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Biomedical subjects

R Jané

Publications and source records attributed to R Jané.

11 recordsLinked to original sources
PubMed2005

Analysis of forced wheezes in asthma patients.

BACKGROUND: Spirometric parameters can be normal in many stable asthma patients, making a diagnosis difficult at certain times in the course of disease. OBJECTIVES: The present study aims to find differences and similarities in the acoustic characteristics of forced wheezes among asthma patients with and without normal spirometric values. METHODS: Eleven chronic asthma patients (8 men/3 women) with moderate-to-severe airway obstruction (FEV1 48.4%), 9 stable asthma patients (6 males/3 females) with normal spirometry (FEV1 84.0%) and a positive methacholine test and 14 healthy subjects (8/6) were enrolled in the study. A contact sensor was placed on the trachea, and wheezes were detected by a modified Shabtai-Musih algorithm in a time-frequency representation. RESULTS: More wheezes were recorded in obstructive asthma patients than in stable asthma and control subjects: nonstable asthma 13.6 (13.3), stable asthma 3.5 (3.0) and control subjects 2.5 (2.1). The mean frequency of all wheezes detected was higher in control subjects than in either stable or non-stable asthma patients. The change in the total number of wheezes after terbutaline inhalation was more pronounced in nonstable asthma patients than in stable asthmatics and control subjects. CONCLUSIONS: This study confirms that wheeze recording during forced expiratory maneuvers can be a complementary measure to spirometry to identify asthma patients.

Acoustics↗
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↗
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↗
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↗
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↗
PubMed1994

Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database.

This paper presents an algorithm for automatically locating the waveform boundaries (the onsets and ends of P, QRS, and T waves) in multilead ECG signals (the 12 standard leads and the orthogonal XYZ leads). Given these locations, features of clinical importance (such as the RR interval, the PQ interval, the QRS duration, the ST segment, and the QT interval) may be measured readily. First, a multilead QRS detector locates each beat, using a differentiated and low-pass filtered ECG signal as input. Next, the waveform boundaries are located in each lead. The leads in which the detected electrical activity is of longest duration are used for the final determination of the waveform boundaries. The performance of our algorithm has been evaluated using the CSE multilead measurement database. In comparison with other algorithms tested by the CSE, our algorithm achieves better agreement with manual measurements of the T-wave end and of interval values, while its measurements of other waveform boundaries are within the range of the algorithm and manual measurements obtained by the CSE.

Algorithms↗
PubMed1992

Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques.

Many bioelectric signals result from the electrical response of physiological systems to an impulse that can be internal (ECG signals) or external (evoked potentials). In this paper an adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potentials. The filter needs two inputs: the signal (primary input) and an impulse correlated with the deterministic component (reference input). We use the LMS algorithm to adjust the weights in the adaptive process. First, we show that the AICF is equivalent to exponentially weighted averaging (EWA) when using the LMS algorithm. A quantitative analysis of the signal-to-noise ratio improvement, convergence, and misadjustment error is presented. A comparison of the AICF with ensemble averaging (EA) and moving window averaging (MWA) techniques is also presented. The adaptive filter is applied to real high-resolution ECG signals and time-varying somatosensory evoked potentials.

Algorithms↗
PubMed1991

Alignment methods for averaging of high-resolution cardiac signals: a comparative study of performance.

Accurate signal estimation by means of coherent averaging techniques needs temporal alignment methods. A known low-pass filtering effect is yielded when alignment errors are present. This is very critical in the estimation of low-level high-frequency potentials in high-resolution ECG analysis. A comparative study of the performance of three alignment methods (the double-level method, a new time-delay estimation method based on normalized integrals, and matched filtering) is presented in this paper. A real signal and additive random noise for several signal-to-noise ratios (SNR's) are selected to make an ensemble of computer-simulated beats. The relation between the standard deviation of temporal misalignment versus SNR is discussed. A second study with real ECG signals is also presented. Several morphologies of QRS and P waves are tested. The results are in agreement with the computer simulation study. Nevertheless, the power spectrum of the noise process can affect the results. Matched filter estimation has been tested in the presence of power line interference (50 Hz), with poor results. An application of the three alignment methods as a function of the SNR is proposed. The new time-delay estimation method has been observed to be robust, even in the presence of nonwhite noise.

Computer Simulation↗
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↗