Computerized Heart Sounds Analysis

Debbal, S. M.

doi:10.5772/23700

Cited by 14 publications

(17 citation statements)

References 4 publications

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“…2 ; arrows) may have useful information for clinical diagnoses [Sato et al, unpublished data] in addition to heart murmurs and accentuated second sounds [14][15][16] . Visualized heart and lung sound signals may contribute to increasing the detection rate of cardiac function anomalies, excluding innocent heart murmurs [15][16][17][18][19] . It may also be possible to predict an adverse neurologic outcome at 1 year of age by analyzing abnormal general movements [20][21][22][23] , which can be detected by the PZT sensor.…”

Section: Discussionmentioning

confidence: 99%

Assessment of a New Piezoelectric Transducer Sensor for Noninvasive Cardiorespiratory Monitoring of Newborn Infants in the NICU

Sato¹,

Ishida-Nakajima

Ishida

et al. 2010

Neonatology

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Background: Electrocardiogram (ECG) and impedance pneumography (IPG), the most widely used techniques for cardiorespiratory monitoring in the neonatal intensive care unit (NICU), have the disadvantage of causing skin damage when used for very premature newborn infants. To prevent skin damage, we designed a new piezoelectric transducer (PZT) sensor. Objective: To assess the potential of the PZT sensor for cardiorespiratory monitoring in the NICU. Methods: The PZT sensor was placed under a folded towel under a neonate to detect an acoustic cardiorespiratory signal, from which heart rate (HR) and breathing rate (BR) were calculated, together with simultaneous ECG/IPG recording for 1–9 days for long and brief (1-min) assessment. Results: The brief assessment showed average correlation coefficients of 0.92 ± 0.12 and 0.95 ± 0.02 between instantaneous HRs/BRs detected by the PZT sensor and ECG/IPG in 27 and 11 neonates examined. During the long assessment, the HR detection rate by the PZT sensor was ∼10% lower than that by ECG (82.6 ± 12.9 vs. 91.8 ± 4.1%; p = 0.001, n = 27), although comparable (90.3 ± 4.1 vs. 92.5 ± 3.4%, p = 0.081) in ∼70% (18/27) of neonates examined; BR detection rate was comparable between the PZT sensor and IPG during relatively stable signal conditions (95.9 ± 4.0 vs. 95.3 ± 3.5%; p = 0.38, n = 11). The PZT sensor caused neither skin damage nor body movement increase in all neonates examined. Conclusion: The PZT sensor is noninvasive and does not cause skin irritation, and we believe it does provide a reliable, accurate cardiorespiratory monitoring tool for use in the NICU, although the issue of mechanical-ventilation noise remains to be solved.

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Section: Discussionmentioning

confidence: 99%

Assessment of a New Piezoelectric Transducer Sensor for Noninvasive Cardiorespiratory Monitoring of Newborn Infants in the NICU

Sato¹,

Ishida-Nakajima

Ishida

et al. 2010

Neonatology

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“…Even though the higher number of components is expected in S1, the interpretation of each of the primary heart sound waveforms for the identification purpose is not an easy task [7]. The structure of the S1 and S2 heart sounds produced by cardiac contractions and valve closures can be considered to be similar across different scales.…”

Section: Multiscale Heart Sound Identificationmentioning

confidence: 99%

“…This is a challenging task since these sounds are found as components of relatively high energy in the same low frequency range having similar morphology [4]. Thus, characterizing the typical heart sounds, such as S1 and S2, has acquired great popularity over the years [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context

et al. 2017

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There has been a sustained effort in the research community over the recent years to develop algorithms that automatically analyze heart sounds. One of the major challenges is identifying primary heart sounds, S1 and S2, as they represent reference events for the analysis. The study presented in this paper analyzes the possibility of improving the structure characterization based on shape context and structure assessment using a small number of descriptors. Particularly, for the primary sound characterization, an adaptive waveform filtering is applied based on blanket fractal dimension for each preprocessed sound candidate belonging to pediatric subjects. This is followed by applying the shape based methods selected for the structure assessment of primary heart sounds. Different methods, such as the fractal ones, are used for the comparison. The analysis of heart sound patterns is performed using support vector machine classifier showing promising results (above 95% accuracy). The obtained results suggest that it is possible to improve the identification process using the shape related methods which are rarely applied. This can be helpful for applications involving automatic heart sound analysis.

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“…11,12 Applications of different methods for signal analysis were tested to provide the best features and characteristics of the PCG signals. [13][14][15] Application of novel signal processing and feature extraction has demonstrated good linear correlation between the spectral energy and frequency bandwidth of the acoustic signal of S1 and systolic functionality (peak systolic velocity and strain rate) during the pharmacological stress test in human subjects. 16,17 In particular, methods that rely on morphological clustering of the heart sounds provide a good handle, enabling the association of different physiological conditions with heartbeats.…”

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confidence: 99%

The Use of Pneumoperitoneum During Laparoscopic Surgery as a Model to Study Pathophysiologic Phenomena: The Correlation of Cardiac Functionality with Computerized Acoustic Indices—Preliminary Data

Bickel

Eitan

Мельник

et al. 2012

Journal of Laparoendoscopic & Advanced Surgical Techniques

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Background: Induction of pneumoperitoneum during laparoscopic surgery leads to diverse cardiovascular changes that can be used as a model to study pathophysiologic phenomena. Application of novel signal processing and figure extraction enabled searching for correlation between various signals and pathophysiologic setting. Our aim was to quantitatively correlate cardiac functionality (as expressed by cardiac output) with the spectral energy of the first heart sound (S1) obtained from the phonocardiogram, during laparoscopic surgery. Patients and Methods: Patients who were scheduled for elective laparoscopic operations were enrolled in the study. Cardiac output was maximally changed during anesthesia and abdominal insufflation and was obtained from the arterial pressure wave (FloTracÔ sensor and VigileoÔ monitor [Edwards Lifesciences Ltd.]). Heart signals were recorded during surgery from each subject by a computerized digital data acquisition system. The automatic analysis of the heart sounds included segmentation that was based on the energy envelope of the heart sounds together with analysis of the electrocardiogram signal. We analyzed the morphology of the sounds using hierarchial cluster analysis to remove those sounds that were not reliably recorded. The magnitude of the amplitude of heart sounds was obtained by using the Hilbert transform for each heartbeat. Statistical analysis was based on linear regression. Results: Following exclusion of 3 patients (mainly because of technical reasons), we were left with 7 patients who demonstrated statistically significant positive correlation between cardiac index and the amplitude of S1 (regression coefficient between 0.4 and 0.9, P < .05). Linear regression analysis was done on the normalized values of all 7 patients and was found to be highly significant. Conclusions:In this study we have demonstrated significant linear correlation between the acoustic amplitude (spectral energy) of S1 and cardiac functionality, through sophisticated computerized analysis, using the pneumoperitoneum model for changing the cardiac output.

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Computerized Heart Sounds Analysis

Cited by 14 publications

References 4 publications

Assessment of a New Piezoelectric Transducer Sensor for Noninvasive Cardiorespiratory Monitoring of Newborn Infants in the NICU

Assessment of a New Piezoelectric Transducer Sensor for Noninvasive Cardiorespiratory Monitoring of Newborn Infants in the NICU

Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context

The Use of Pneumoperitoneum During Laparoscopic Surgery as a Model to Study Pathophysiologic Phenomena: The Correlation of Cardiac Functionality with Computerized Acoustic Indices—Preliminary Data

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