José Morera

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 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.

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.

Multiclass classification of subjects with sleep apnoea–hypopnoea syndrome through snoring analysis

The gold standard for diagnosing sleep apnoea-hypopnoea syndrome (SAHS) is polysomnography (PSG), an expensive, labour-intensive and time-consuming procedure. Accordingly, it would be very useful to have a screening method to allow early assessment of the severity of a subject, prior to his/her referral for PSG. Several differences have been reported between simple snorers and SAHS patients in the acoustic characteristics of snoring and its variability. In this paper, snores are fully characterised in the time domain, by their sound intensity and pitch, and in the frequency domain, by their formant frequencies and several shape and energy ratio measurements. We show that accurate multiclass classification of snoring subjects, with three levels of SAHS, can be achieved on the basis of acoustic analysis of snoring alone, without any requiring information on the duration or the number of apnoeas. Several classification methods are examined. The best of the approaches assessed is a Bayes model using a kernel density estimation method, although good results can also be obtained by a suitable combination of two binary logistic regression models. Multiclass snore-based classification allows early stratification of subjects according to their severity. This could be the basis of a single channel, snore-based screening procedure for SAHS.

Formant frequencies of normal breath sounds of snorers may indicate the risk of Obstructive Sleep Apnea Syndrome

Several differences between the airway of normal subjects and those with OSAS are well known. The characteristics of the upper airway may be indirectly studied through the formant frequencies of breathing sounds. In this work we analyze the formants of inspiration and exhalation sounds in snoring subjects with and without OSAS. Formant frequencies of inspiration and exhalation appear in the same bands as snores. Formant F1 is significantly lower in inspiration episodes of OSAS patients (p=0.008) with a decreasing tendency as the AHI increases (r=−0.705). In addition, this formant has a significantly higher variability SF1 in pathological subjects, for both inspiration (p=0.022) and exhalation (p=0.038) episodes, as was previously found in snores. A higher variability of formant frequencies seems to be an indicator of the presence of OSAS. The proposed technique could allow the identification of OSAS patients from normal breathing alone.

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 (fm) and median (fc) 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 fc, 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 fc, fm and H/L ratio, respectively, in both myographic signals.

Assessment of diaphragmatic contraction using a non-invasive piezoelectric contact sensor

The use of a contact piezoelectric sensor (accelerometer) to study the mechanical properties of the canine diaphragm is presented. This study determined whether it is possible to monitor the dynamic diaphragmatic function via a non-invasive sensor. For this, the contact sensor (CS) signal on the rib cage is compared with direct measurements of the diaphragmatic muscle length, made by sonomicrometry. Experiments were performed in three pentotarbital-anesthetized mongrel dogs. Length changes were measured using a pair of piezoelectric crystals implanted into the costal diaphragm. The movement of the chest wall, due to the diaphragm contraction, was measured with a piezoelectric contact sensor positioned on the costal wall. In previous works, it have been shown that beginning of inspiratory and expiratory stages were indicated with inflexion points in the contact sensor (CS) signal. Two specific algorithms have been developed to determinate the contraction and relaxation periods in the two signals. The diaphragm length (DL) algorithm has been validated, using a simple replication reliability study (SRRS), by means of manually measures done in the DL signal by a specialist. Finally, a SRRS has been applied between DL and CS signals algorithms measures. Results suggest that it would be possible to evaluate the diaphragmatic function by a non-invasive piezoelectric contact sensor.

Bayes classification of snoring subjects with and without Sleep Apnea Hypopnea Syndrome, using a Kernel method

The gold standard for diagnosing Sleep Apnea Hypopnea Syndrome (SAHS) is the Polysomnography (PSG), an expensive, labor-intensive and time-consuming procedure. It would be helpful to have a simple screening method that allowed to early determining the severity of a subject prior to his/her enrolment for a PSG. Several differences have been reported in the acoustic snoring characteristics between simple snorers and SAHS patients. Previous studies usually classify snoring subjects into two groups given a threshold of Apnea-Hypoapnea Index (AHI). Recently, Bayes multi-group classification with Gaussian Probability Density Function (PDF) has been proposed, using snore features in combination with apnea-related information. In this work we show that the Bayes classifier with Kernel PDF estimation outperforms the Gaussian approach and allows the classification of SAHS subjects according to their severity, using only the information obtained from snores. This could be the base of a single channel, snore-based, screening procedure for 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.