Raimon Jané

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.

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

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). The LMS algorithm is used to adjust the weights in the adaptive process. It is shown that the AICF is equivalent to exponentially weighted averaging (FWA) 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.<<ETX>>

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

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. A real signal and additive random noise for several signal-to-noise ratios (SNRs) 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 interferences (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.<<ETX>>

Evaluation of an automatic threshold based detector of waveform limits in Holter ECG with the QT database

The authors evaluate a single-lead threshold based ECG wave boundaries detector with a QT database developed for validation purposes. They also identify its different sources of error distinguishing those that come from precision errors in boundary location from those that come from morphology misclassification. They obtain 71 % of records with correct morphology identification of T wave and variance in boundary location within manual referees variance. The remaining records analyzed correspond to signals with poor SNR at the T wave, or morphology discrepancies between algorithm and experts.

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 realtime 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.

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.

Adaptive baseline wander removal in the ECG: Comparative analysis with cubic spline technique

Baseline wandering is a classical problem in electrocardiogram (ECG) records that generally produces artifactual data when measuring ECG parameters. The authors present a cascade adaptive filter for removing the baseline wander and preserving the low-frequency components of the ECG. This cascade adaptive filter works in two stages. The first stage is an adaptive notch filter at zero frequency. The second stage is an adaptive impulse correlated filter that, using a QRS detector, estimates the ECG signal correlated with the QRS occurrence. In this way, all the signal components correlated with the QRS complex are preserved. The authors analyze the frequency response of the filter, showing that the filter can be seen as a comb filter without the DC lobe. The method was applied to ECG signals from the MIT-BIH database and its performance was compared with the cubic spline approach. The method can remove baseline wander in real time without needing to calculate the isoelectric levels, while preserving the low-frequency ECG clinical information.<<ETX>>

Improved alignment method for noisy high-resolution ECG and Holter records using multiscale cross-correlation

The coherent signal averaging process requires accurate estimation of a fiducial point in all beats to be averaged. The temporal cross-correlation between each detected beat and a template beat is the typical alignment method used with high-resolution electrocardiogram (HRECG) records. However, this technique does not produce a precise fiducial mark in records with high noise levels, like those found in Holter HRECG systems. In this study, we propose a new alignment method based on the multiscale cross-correlation between the template and each detected beat. We report the results of tests comparing multiscale and temporal methods for 3000 beats of simulated HRECG records corrupted separately with white noise, electromyographic noise and power line interference (50 Hz) of different root mean square levels. A second study with simulated records constructed from real Holter HRECG records is also presented. The results indicate that the multiscale alignment method produces a lower trigger jitter than the temporal method in all tests. We conclude that the proposed alignment method can be used in HRECG records with high noise levels.

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.

Adaptive filtering of ECG baseline wander

Baseline wander removal is a classical problem in ECG signal processing. We present a cascade adaptive filter to remove the baseline wander in the ECG preserving the overlapped deterministic low frequency components of the ECG, such as ST segment components. This cascade adaptive filter works in two stages. The first stage is an adaptive notch filter at zero frequency. The second stage is an adaptive impulse correlated filter that estimates the ECG signal correlated with the QRS occurrence. In both stages the LMS algorithm is used with different gain constants μ1 and μ2. We analyse the frequency response of the filter as a function of the μ1 and μ2 parameters, selecting those more appropriated for baseline removal. Finally, the performance of the filter is studied on an actual ECG affected by baseline drift.