Peter Hult

Feature Extraction for Systolic Heart Murmur Classification

Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an “intelligent stethoscope” with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil’s sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.

Electrical characteristics of conductive yarns and textile electrodes for medical applications

Clothing with conductive textiles for health care applications has in the last decade been of an upcoming research interest. An advantage with the technique is its suitability in distributed and home health care. The present study investigates the electrical properties of conductive yarns and textile electrodes in contact with human skin, thus representing a real ECG-registration situation. The yarn measurements showed a pure resistive characteristic proportional to the length. The electrodes made of pure stainless steel (electrode A) and 20% stainless steel/80% polyester (electrode B) showed acceptable stability of electrode potentials, the stability of A was better than that of B. The electrode made of silver plated copper (electrode C) was less stable. The electrode impedance was lower for electrodes A and B than that for electrode C. From an electrical properties point of view we recommend to use electrodes of type A to be used in intelligent textile medical applications.

A bioacoustic method for timing of the different phases of the breathing cycle and monitoring of breathing frequency

It is well known that the flow of air through the trachea during respiration causes vibrations in the tissue near the trachea, which propagate to the surface of the body and can be picked up by a microphone placed on the throat over the trachea. Since the vibrations are a direct result of the airflow, accurate timing of inspiration and expiration is possible. This paper presents a signal analysis solution for automated monitoring of breathing and calculation of the breathing frequency. The signal analysis approach uses tracheal sound variables in the time and frequency domains, as well as the characteristics of the disturbances that can be used to discriminate tracheal sound from noise. One problem associated with the bioacoustic method is its sensitivity for acoustic disturbances, because the microphone tends to pick up all vibrations, independent of their origin. A signal processing method was developed that makes the bioacoustic method clinically useful in a broad variety of situations, for example in intensive care and during certain heart examinations, where information about both the precise timing and the phases of breathing is crucial.

Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction

Heart sounds (HS) obscure the interpretation of lung sounds (LS). This letter presents a new method to detect and remove this undesired disturbance. The HS detection algorithm is based on a recurrence time statistic that is sensitive to changes in a reconstructed state space. Signal segments that are found to contain HS are removed, and the arising missing parts are replaced with predicted LS using a nonlinear prediction scheme. The prediction operates in the reconstructed state space and uses an iterated integrated nearest trajectory algorithm. The HS detection algorithm detects HS with an error rate of 4% false positives and 8% false negatives. The spectral difference between the reconstructed LS signal and an LS signal with removed HS was 0.34/spl plusmn/0.25, 0.50/spl plusmn/0.33, 0.46/spl plusmn/0.35, and 0.94/spl plusmn/0.64 dB/Hz in the frequency bands 20-40, 40-70, 70-150, and 150-300 Hz, respectively. The cross-correlation index was found to be 99.7%, indicating excellent similarity between actual LS and predicted LS. Listening tests performed by a skilled physician showed high-quality auditory results.

Use of signal analysis of heart sounds and murmurs to assess severity of mitral valve regurgitation attributable to myxomatous mitral valve disease in dogs.

OBJECTIVE To investigate use of signal analysis of heart sounds and murmurs in assessing severity of mitral valve regurgitation (mitral regurgitation [MR]) in dogs with myxomatous mitral valve disease (MMVD). ANIMALS 77 client-owned dogs. PROCEDURES Cardiac sounds were recorded from dogs evaluated by use of auscultatory and echocardiographic classification systems. Signal analysis techniques were developed to extract 7 sound variables (first frequency peak, murmur energy ratio, murmur duration > 200 Hz, sample entropy and first minimum of the auto mutual information function of the murmurs, and energy ratios of the first heart sound [S1] and second heart sound [S2]). RESULTS Significant associations were detected between severity of MR and all sound variables, except the energy ratio of S1. An increase in severity of MR resulted in greater contribution of higher frequencies, increased signal irregularity, and decreased energy ratio of S2. The optimal combination of variables for distinguishing dogs with high-intensity murmurs from other dogs was energy ratio of S2 and murmur duration > 200 Hz (sensitivity, 79%; specificity, 71%) by use of the auscultatory classification. By use of the echocardiographic classification, corresponding variables were auto mutual information, first frequency peak, and energy ratio of S2 (sensitivity, 88%; specificity, 82%). CONCLUSIONS AND CLINICAL RELEVANCE Most of the investigated sound variables were significantly associated with severity of MR, which indicated a powerful diagnostic potential for monitoring MMVD. Signal analysis techniques could be valuable for clinicians when performing risk assessment or determining whether special care and more extensive examinations are required.

Detection of the third heart sound using a tailored wavelet approach

The third heart sound is normally heard during auscultation of younger individuals but disappears with increasing age. However, this sound can appear in patients with heart failure and is thus of potential diagnostic use in these patients. Auscultation of the heart involves a high degree of subjectivity. Furthermore, the third heart sound has low amplitude and a low-frequency content compared with the first and second heart sounds, which makes it difficult for the human ear to detect this sound. It is our belief that it would be of great help to the physician to receive computer-based support through an intelligent stethoscope, to determine whether a third heart sound is present or not. A precise, accurate and low-cost instrument of this kind would potentially provide objective means for the detection of early heart failure, and could even be used in primary health care. In the first step, phonocardiograms from ten children, all known to have a third heart sound, were analysed, to provide knowledge about the sound features without interference from pathological sounds. Using this knowledge, a tailored wavelet analysis procedure was developed to identify the third heart sound automatically, a technique that was shown to be superior to Fourier transform techniques. In the second step, the method was applied to phonocardiograms from heart patients known to have heart failure. The features of the third heart sound in children and of that in patients were shown to be similar. This resulted in a method for the automatic detection of third heart sounds. The method was able to detect third heart sounds effectively (90%), with a low false detection rate (3.7%), which supports its clinical use. The detection rate was almost equal in both the children and patient groups. The method is therefore capable of detecting, not only distinct and clearly visible/audible third heart sounds found in children, but also third heart sounds in phonocardiograms from patients suffering from heart failure.

Electrical Properties of Textile Electrodes

In this study we aim to explain the behavior of textile electrodes due to their construction techniques. Three textile electrodes were tested for electrode impedance and polarization potentials. The multifilament yarn (A) is favorable for its low thread resistance. Although, when knitted into electrodes, the staple fiber yarn (B) showed a comparable and satisfiable electrode impedance. The multifilament yarn had however a lower polarization potential drift then the other specimens. The monofilament yarn (C) had high electrode impedance and varying mean polarization potentials due to its conductive material and small contact area with the skin.

Assessing Aortic Stenosis Using Sample Entropy of the Phonocardiographic Signal in Dogs

In aortic valve stenosis (AS), heart murmurs arise as an effect of turbulent blood flow distal to the obstructed valves. With increasing AS severity, the flow becomes more unstable, and the ensuing murmur becomes more complex. We hypothesize that these hemodynamic flow changes can be quantified based on the complexity of the phonocardiographic (PCG) signal. In this study, sample entropy (SampEn) was investigated as a measure of complexity using a dog model. Twenty-seven boxer dogs with various degrees of AS were examined with Doppler echocardiography, and the peak aortic flow velocity (Vmax) was used as a reference of AS severity. SampEn correlated to Vmax with R = 0.70 using logarithmic regression. In a separate analysis, significant differences were found between physiologic murmurs and murmurs caused by AS (p < 0.05), and the area under a receiver operating characteristic curve was calculated to 0.96. Comparison with previously presented PCG measures for AS assessment showed improved performance when using SampEn, especially for differentiation between physiological murmurs and murmurs caused by mild AS. Studies in patients will be needed to properly assess the technique in humans.

Detection of the third heart sound using a tailored wavelet approach: Method verification

Heart sounds can be considered as mechanical fingerprints of myocardial function. The third heart sound normally occurs in children but disappears with maturation. The sound can also appear in patients with heart failure. The sound is characterised by its low-amplitude and low-frequency content, which makes it difficult to identify by the traditional use of the stethoscope. A wavelet-based method has recently been developed for detection of the third heart sound. This study investigated if the third heart sound could be identified in patients with heart failure using this detection method. The method was also compared with auscultation using conventional phonocardiography and with characterisation of the patients with echocardiography. In the first study, 87% of the third heart sounds were detected using the wavelet method, 12% were missed, and 6% were false positive. In study 2, the waveletdetection method identified 87% of the patients using the third heart sound, and regular phonocardiography identified two (25%) of the subjects.

Validation and characterization of the computerized laryngeal analyzer (CLA) technique

Abstract. The aim of this study was to investigate the response characteristics of the Computerized Laryngeal Analyzer (CLA) and the validity of the noninvasive CLA method to detect swallowing-induced laryngeal elevation correctly. Two healthy adults and two experimental models were used in the study. The CLA technique identified all swallowing events but was unable to discriminate between swallowing and other movements of the tongue or the neck. The computer program produced a derivated response to a square wave signal. Stepwise bending increments of the sensor displayed a linear amplitude response. The degree of laryngeal elevation could not be estimated with the CLA technique, and it was not possible to draw any reliable conclusions from the recordings as to whether the larynx was moving upward or downward.