Identification and characterization of near-fatal asthma phenotypes by cluster analysis.

BACKGROUND: Near-fatal asthma (NFA) is a heterogeneous clinical entity and several profiles of patients have been described according to different clinical, pathophysiological and histological features. However, there are no previous studies that identify in a unbiased way--using statistical methods such as clusters analysis--different phenotypes of NFA. Therefore, the aim of the present study was to identify and to characterize phenotypes of near fatal asthma using a cluster analysis. METHODS: Over a period of 2 years, 33 Spanish hospitals enrolled 179 asthmatics admitted for an episode of NFA. A cluster analysis using two-steps algorithm was performed from data of 84 of these cases. RESULTS: The analysis defined three clusters of patients with NFA: cluster 1, the largest, including older patients with clinical and therapeutic criteria of severe asthma; cluster 2, with an high proportion of respiratory arrest (68%), impaired consciousness level (82%) and mechanical ventilation (93%); and cluster 3, which included younger patients, characterized by an insufficient anti-inflammatory treatment and frequent sensitization to Alternaria alternata and soybean. CONCLUSIONS: These results identify specific asthma phenotypes involved in NFA, confirming in part previous findings observed in studies with a clinical approach. The identification of patients with a specific NFA phenotype could suggest interventions to prevent future severe asthma exacerbations.

Cardiac interference reduction in diaphragmatic MMG signals during a Maintained Inspiratory Pressure Test.

A recursive least square (RLS) adaptive filtering algorithm for reduction of cardiac interference in diaphragmatic mecanomyographic (MMGdi) signals is addressed in this paper. MMGdi signals were acquired with a capacitive accelerometer placed between 7th and 8th intercostal spaces, on the right anterior axillary line, during a maintained inspiratory pressure test. Subjects were asked to maintain a constant inspiratory pressure with a mouthpiece connected to a closed tube (without breathing). This maneuver was repeated at five different contraction efforts: apnea (no effort), 20 cmH2O, 40 cmH2O, 60 cmH2O and maximum voluntary contraction. An adaptive noise canceller (ANC) using the RLS algorithm was applied on the MMGdi signals. To evaluate the behavior of the ANC, the MMGdi signals were analyzed in two segments: with and without cardiac interference (WCI and NCI, respectively). In both segments it was analyzed the power spectral density (PSD), and the ARV and RMS amplitude parameters for each contraction effort. With the proposed ANC algorithm the amplitude parameters of the WCI segments were reduced to a level similar to the one of the NCI segments. The obtained results showed that ANC using the RLS algorithm allows to significantly reduce the cardiac interference in MMGdi signals.

All night analysis of time interval between snores in subjects with sleep apnea hypopnea syndrome.

Sleep apnea-hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7-109.9 h(-1)) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h(-1)) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h(-1)) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h(-1), respectively. The features proved to be reliable predictors of the subjects' SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS.

Respiratory and spontaneous arousals in patients with Sleep Apnea Hypopnea Syndrome.

Sleep in patients with Sleep Apnea-Hypopnea Syndrome (SAHS) is frequently interrupted with arousals. Increased amounts of arousals result in shortening total sleep time and repeated sleep-arousal change can result in sleep fragmentation. According to the American Sleep Disorders Association (ASDA) an arousal is a marker of sleep disruption representing a detrimental and harmful feature for sleep. The nature of arousals and its role on the regulation of the sleep process raises controversy and has sparked the debate in the last years. In this work, we analyzed and compared the EEG spectral content of respiratory and spontaneous arousals on a database of 45 SAHS subjects. A total of 3980 arousals (1996 respiratory and 1984 spontaneous) were analyzed. The results showed no differences between the spectral content of the two kinds of arousals. Our findings raise doubt as to whether these two kinds of arousals are truly triggered by different organic mechanisms. Furthermore, they may also challenge the current beliefs regarding the underestimation of the importance of spontaneous arousals and their contribution to sleep fragmentation in patients suffering from SAHS.

Normal non-regular snores as a tool for screening SAHS severity.

Snoring is one of the earliest and most consistent sign of upper airway obstruction leading to Sleep Apnea-Hypopnea Syndrome (SAHS). Several studies on post-apneic snores, snores that are emitted immediately after an apnea, have already proven that this type of snoring is most distinct from that of normal snoring. However, post-apneic snores are more unlikely and sometimes even inexistent in simple snorers and mild SAHS subjects. In this work we address that issue by proposing the study of normal non-regular snores. They correspond to successive snores that are separated by normal breathing cycles. The results obtained establish the feasibility of acoustic parameters of normal non-regular snores as a promising tool for a prompt screening of SAHS severity.

Regular and non regular snore features as markers of SAHS.

Sleep Apnea-Hypopnea Syndrome (SAHS) diagnosis is still done with an overnight multi-channel polysomnography. Several efforts are being made to study profoundly the snore mechanism and discover how it can provide an opportunity to diagnose the disease. This work introduces the concept of regular snores, defined as the ones produced in consecutive respiratory cycles, since they are produced in a regular way, without interruptions. We applied 2 thresholds (TH(adaptive) and TH(median)) to the time interval between successive snores of 34 subjects in order to select regular snores from the whole all-night snore sequence. Afterwards, we studied the effectiveness that parameters, such as time interval between successive snores and the mean intensity of snores, have on distinguishing between different levels of SAHS severity (AHI (Apnea-Hypopnea Index) < 5h(-1), AHI <10 h(-1), AHI < 15 h(-1), AHI < 30 h(-1)). Results showed that TH(adaptive) outperformed TH(median) on selecting regular snores. Moreover, the outcome achieved with non-regular snores intensity features suggests that these carry key information on SAHS severity.

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.

Monitoring of wheeze duration during spontaneous respiration in asthmatic patients.

Respiratory sound analysis can offer important information related to pulmonary diseases. Wheezes have been reported as adventitious respiratory sounds in asthmatic or obstructive patients, during forced exhalation maneuvers. In this work, we propose a method for monitoring and analysis of respiratory sounds in frequency domain, during spontaneous ventilation. The database analyzed was acquired during spontaneous ventilation for 120 seconds (DBsv), of 26 asthmatics patients. Using an autoregressive model (AR, order 16), the Power Spectral Density (PSD) was calculated for every phase of expiration and inspiration and the maximum frequency (fp) was estimated. From this parameter we study the time duration of the wheezes. The effect of bronchodilator inhalation in asthmatic patients was studied analyzing the duration of the wheezes in the bandwidth 600-2000 Hz (HFband). The wheeze duration is evaluated as the number of consecutive segments, with fp is inside of HFband, (for 3 or more segments in a cycle). The difference of the wheeze duration inside the respiratory cycles (Dwheez), before and after bronchodilator inhalation (POST) was evaluated. It was found a good correlation between Dwheez and FEV 1% improvement (FEV 1%_imp), for FEV1%_imp greater than 8%, whereas values FEV1%_imp less than 8% did not show any change of Dwheez. This last result suggests no difference in the wheeze duration between the baseline and POST records. This method could predict the FEV1%_imp by means of estimation of Dwheez during spontaneous ventilation.

A wavelet multiscale based method to separate the high and low frequency components of mechanomyographic signals.

The study of mechanomyographic (MMG) signals during dynamic contraction requires a criterion to select the cut-off frequency of the filter utilized to separate the low frequency (L.F) component (basically due to gross movement of the muscle or of the body) and the high frequency (HF) component (related with the vibration of the muscle fibers during contraction). To date, there is not an established criterion to carry out this selection. In this study, we propose a wavelet multiscale based method to aid to select a suitable cut-off frequency to separate correctly the LF and HF components. This method has been tested in an animal model, with the signal acquired during spontaneous ventilations with a capacitive accelerometer applied on the costal wall. This signal, as the MMG signals during dynamic contractions, has a LF component that is related with the movement of the thoracic cage, and a HF component that could be related with the vibration of diaphragm muscle fibers during contraction. The results obtained in the two respiratory tests analyzed indicate that cut-off frequencies around 10 and 3 Hz, respectively, must be employed to eliminate the LF component. The proposed wavelet multiscale method appears to be suitable to carry out a preliminary study of the MMG frequency content in dynamic contraction protocols.

Variability of snore parameters in time and frequency domains in snoring subjects with and without Obstructive Sleep Apnea.

Several studies have shown differences in spectral parameters between simple snorers and patients with Obstructive Sleep Apnea Syndrome (OSAS). Most of them analyzed a reduced number of snores and/or only post-apneic snores were selected in OSAS patients. Previous findings suggest that snore parameters have a greater variability as the severity of OSAS increases. In this work we propose to analyze the snoring variability through the magnitude of the first difference of snore parameters. The technique is applied to long time sound recordings from 9 simple snorers (15795 snores) and 15 OSAS patients (19263 snores). The snores are characterized by their sound intensity an by a set of spectral parameters The variability of snore parameters is well correlated to OSAS severity (r>0.7) and is significantly greater in OSAS patients than in simple snorers (p<0.005). The results are similar when post-apneic snores are excluded from the analysis. Snoring subjects are classified with a logistic regression model, which is validated with the live-one-out method. The model is adjusted to correctly classify 100% of OSAS patients for screening purposes previously to enroll for a whole polysomnographic study.

Analysis of wheezes in asthmatic patients during spontaneous respiration.

Respiratory sound analysis can offer important information related to pulmonary diseases. Wheezes have been reported as adventitious respiratory sounds in asthmatic or obstructive patients, during forced exhalation maneuvers. In this work, we propose a method for analysis of respiratory sounds in frequency domain, during spontaneous ventilation. Two databases were analyzed: signals acquired during spirometry (DBspir), composed by 23 subjects (N=15 asthmatics, N=8 control); and signals acquired during spontaneous ventilation for 120 seconds (DBsv), composed by 26 asthmatics. Using an autoregressive model (AR, order 16), it was calculated the Power Spectral Density (PSD) for each expiration and the peak frequency (fp) was estimated. Higher values of fp were found in asthmatic patients with severe obstruction in relation to light obstruction or control subjects. The effect of bronchodilator inhalation in asthmatic patients was studied in the database DBsv, analyzing contribution of wheezes in the bandwidth 600-2000 Hz (HFband)., Differences of number of respiratory cycles with wheezes (Dwheez index), before and after bronchodilator inhalation were evaluated. It was found a good correlation between Dwheez and FEV1% improvement (FEV1>%_imp), for FEV1%_imp > 10%. This method could predict the FEV1%_imp by means of estimation of Dwheez index during spontaneous ventilation.

Assessment of respiratory muscle effort studying diaphragm movement registered with surface sensors. Animal model (dogs).

The diaphragm movement (DM) signal during spontaneous ventilations is analyzed in this work. The DM signal is acquired by means two surface sensors (a piezoelectric contact sensor -PCS- and a piezoelectric accelerometer -ACP) applied on the costal wall. The main objective is to develop a new non invasive technique to assess respiratory muscle effort. Experiments were performed in an animal model: four pentobarbital-anesthetized and two awake mongrel dogs, carrying out spontaneous ventilations against an inspiratory load. DM signal has been decomposed in two components: a low frequency component (lower than 5 Hz) due to the overall lateral movement of the muscle (MOV component), and a high frequency component (higher than 5 Hz) due to the lateral vibration of active muscle fibers (VIB component). It has been seen that the PCS acquires only MOV components of MD signal, while ACP acquires both components. Positive high correlation coefficients have been found between amplitude parameters of VIB components of DM signal, acquired by means the ACP, and the respiratory muscle effort during ventilation, measured with inspiratory pressures.

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.

Exploración funcional de los músculos respiratorios / Functional assessment of respiratory muscles

No disponible

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.

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.

Quality of life in severe chronic obstructive pulmonary disease: correlation with lung and muscle function.

UNLABELLED: Chronic obstructive pulmonary disease (COPD) patients suffer from significant impairment in quality of life (QL), but the variables related to this impairment are not well known. The aim of this study has been to identify physiological parameters related to QL in severe COPD patients undergoing long-term oxygen therapy. MATERIALS AND METHODS: We studied 47 COPD patients using long-term oxygen therapy (43 men/four women, 65.17 SD 8.21 years, 3.17 SD 2.61 years on oxygen). The Nottingham Health Profile (NHP) and activities of daily living (ADL) questionnaire were used to measure QL. Subjective assessment of dyspnoea was performed using a visual analogue scale. The physiological parameters determined were lung function (spirometry, arterial blood gases, lung volumes and carbon monoxide diffusing capacity), muscle function (maximum inspiratory and expiratory pressures, deltoid muscle and handgrip strength), and nutrition status (tricipital skin fold and mid-arm muscle circumference). RESULTS: High ADL (8.32 SD 6.97) and NHP scores (energy 63.3 SD 40.43, pain 35.11 SD 31.56, emotional reactions 43.03 SD 25.13, sleep 51.91 SD 32.75, social isolation 30.64 SD 26.98, physical mobility 49.73 SD 24.93) demonstrated clinically significant QL impairment in the severe COPD patients studied. Stepwise multiple regression analysis found a correlation between lung function and QL. Low FEV1% was associated with impairment in energy, physical mobility and social isolation NHP scores and ADL score (r = -0.3, P < 0.05). RV/TLC also correlated with ADL and social isolation scores (r = 0.3, P < 0.05). Lung function explained 39-45% of the variation in these QL dimensions. QL did not correlate with other lung function parameters, muscle function or nutrition status. CONCLUSION: COPD patients using long-term oxygen suffer from severe QL impairment affecting not only energy and mobility but also emotional reactions, social isolation and sleep. Lung function is related to energy, mobility and social isolation dimensions, but muscle function is unrelated to QL in these patients.