José Antonio Fiz

MEASUREMENT OF GENERAL HEALTH STATUS OF NON-OXYGEN-DEPENDENT CHRONIC OBSTRUCTIVE PULMONARY DISEASE PATIENTS

Chronic obstructive pulmonary disease is a prevalent condition causing a high level of disability, and it is one of the leading causes of death. To assess the general health status of moderate to severe Chronic obstructive pulmonary disease patients, we studied 76 male patients attending an outpatient hospital clinic who were not dependent on oxygen and who did not present bronchial obstruction reversibility. We assessed clinical status (dyspnea, six-minute walking distance) and functional respiratory impairment (spirometry, and blood gas analysis) of the patients and also asked them to respond to the Spanish version of the Nottingham Health Profile, a multi-dimensional generic health status measure. Patients scored especially higher than the general population (denoting more level of distress) in energy, physical mobility and sleep Nottingham Health Profile dimensions. The former two dimensions scores had a high correlation with dyspnea (respectively Spearman Rs = 0.60, and Rs = 0.64; P less than 0.01). High levels of sleep disturbances were found for patients reporting low or very low dyspnea level. Health status measurement (Nottingham Health Profile dimension scores) and functional respiratory impairment were not correlated. Results underscore the importance of measuring symptoms carefully when assessing these patients, whose health status is substantially affected by the Chronic obstructive pulmonary disease. They also suggest that it is relevant to assess sleep disturbances in these patients.

Time-frequency detection and analysis of wheezes during forced exhalation

The objective of the present work was to detect and analyze wheezes by means of a highly sensitive time-frequency algorithm. Automatic measurements were compared with clinical auscultation for forced exhalation segments from 1.2 to 0 liters/second (l/s). Sensitivities between 100% and 71%, as a function of flow level related to wheezing segments detection, were achieved. Time-frequency wheeze parameters were measured for the flow range from 1.2 to 0.2 l/s. Wheezes were detected in both analyzed groups; asthmatics (N=16) and control subjects (N=15). Significant differences between groups were found for the mean number of wheezes detected at basal condition (p=0.0003). Frequency parameter differences were also significant (0.0112<p<0.0307). All these parameters were also studied after applying a bronchodilator drug (Terbutaline). Significant differences between patient groups were found when studying the changes in the number of wheezes for each patient (p=0.0195). Finally, limited bandwidth parameters, which measure the bronchodilator response, were also studied.

Continuous analysis and monitoring of snores and their relationship to the apnea-hypopnea index.

We used a new automatic snoring detection and analysis system to monitor snoring during full‐night polysomnography to assess whether the acoustic characteristics of snores differ in relation to the apnea‐hypopnea index (AHI) and to classify subjects according to their AHI.

Automatic detection of snoring signals: validation with simple snorers and OSAS patients

Relationship between snoring and Obstructive Sleep Apnea Syndrome (OSAS) has been reported in the literature. Recently, studies of snoring sound intensity, but also estimation of spectral features for each snoring episode, have been published. Usually, patients that are suspected of OSAS pathology are studied by polysomnography during all the night. To analyze the snoring signal, it is very useful to automatically detect each episode, in order to calculate several features that describe the signal. In this work an automatic detection algorithm of acoustic snoring signals has been designed, to work with long duration respiratory sound recordings. Two blocs compose the detector. The former is a segmentation subsystem that detects changes of variance on the signal. The latter is a 2-layer Feedforward Multilayer Neural Network with backpropagation learning algorithm. The network was trained with 625-selected events, including snores with different shapes and characteristics, from normal snorers and OSAS patients, and other sounds. In this way, the detector was designed to select snoring episodes from simple snorers and OSAS patients, and to reject cough, voice and other artifacts. The detector has been applied to real snoring signals recorded during polysomnographic studies. In order to validate the detector, more than 500 snores were analyzed from 10 excerpts, taken at random from a database of 30 snorer subjects with different apnea/hipoanea index (AHI). Results were compared with manual annotations done by a medical doctor. The detector showed a good performance and achieved a Sensitivity of 82% and a Positive Predictive Value of 90%.

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

Automatic classification of subjects with and without Sleep Apnea through snoring analysis

A new method for indirect identification of Sleep Apnea patients through snoring characteristics is proposed. The method uses a logistic regression model which is fed with several time and frequency parameters from snores and their variability. The information is contained in all the snores automatically detected in nocturnal sound recordings. In the validation of the model, subjects are classified with a sensitivity higher than 93% and a specificity between 73% and 88% when all detected snores are used. The model can also be adjusted to obtain 100% specificity with a corresponding sensitivity between 70% and 87%. This results are better than previous reported methods based on snoring analysis, but with a single channel, and are comparable to the classification scores of several portable apnea monitors when evaluated on a similar number of patients. This technique is a promising tool for the screening of snorers, allowing snorers with a low apnea-hypopnea index (AHI< 10) to avoid a full-night polysomnographic study at the hospital.

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/3females) 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.

Indices of Respiratory Muscle Endurance in Healthy Subjects

Background: The evaluation of respiratory muscle performance can be described in terms of strength and endurance, the latter usually being measured by means of resistive or threshold inspiratory loads, using devices that are also used for respiratory muscle training. Few authors, however, have published endurance reference values for healthy subjects. To that end, we studied two indices of respiratory muscle endurance in a population of 99 healthy volunteers (50 men, 49 women) divided into five age groups (20–70 years old) applying a modification of the methods of Martyn et al. and Nickerson and Keens. Inspiratory muscle endurance (Tlim) was defined as the time the subject was able to sustain breathing against an inspiratory pressure load equivalent to 80% of the maximum tolerated load (Cmax). Cmax was calculated using a 2-min incremental threshold load. Results: We found that the heaviest inspiratory threshold load tolerated for 2 min and the time a load equivalent to 80% of Cmax (Tlim) could be sustained were not significantly different for male and female subjects. Tlim correlated with Cmax, age, height, and maximum respiratory pressures.

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 on spectral estimator being the more suitable an AR model with high order.

Application of the Empirical Mode Decomposition method to the Analysis of Respiratory Mechanomyographic Signals

The study of the mechanomyographic (MMG) signals during dynamic contractions requires a criterion to separate the low frequency (LF) 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). In this study, we propose to use the Empirical Mode Decomposition method in order to analyze the Intrinsic Mode Functions of MMG signals of the diaphragm muscle, acquired by means of 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 method was tested on an animal model, with two incremental respiratory protocols performed by two non anesthetized mongrel dogs. The results show that the proposed EMD based method provides very good results for the cancellation of low frequency component of MMG signals. The obtained correlation coefficients between respiratory and MMG parameters were higher than the ones obtained with a Wavelet multiresolution decomposition method utilized in a previous work.