Respiratory modulation of heart sound morphology

Amit, Guy; Shukha, Khuloud; Gavriely, Noam; Intrator, Nathan

doi:10.1152/ajpheart.00806.2008

Cited by 30 publications

(31 citation statements)

References 25 publications

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“…With the opposite changes of heart hemodynamics in inspiration and expiration, the characteristics of respiratory variation in splitting is illustrated in Figure 2a. The morphology changes of heart sounds due to respiration have been investigated in recent years [12][13][14][15]. One real example of these variations of a healthy subject is shown in Figure 2b, where the S2s are sorted by respiratory phase.…”

Section: Physiology Of Respiratory Split In S2mentioning

confidence: 99%

Discrimination of Aortic and Pulmonary Components from the Second Heart Sound Using Respiratory Modulation and Measurement of Respiratory Split

Tang

Chen

2017

Applied Sciences

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Featured Application: This work proposes a method to measure the respiratory split in a quantitative way. Based on the assumption model, the aortic component can be extracted by average and the pulmonary component can be estimated by subtracting. The calculation of the split is performed by timing difference of the two components. The method can track the respiratory split and has applications in monitoring heart response to respiration. Abstract:The second heart sound consists of aortic and pulmonary components. Analysis on the changes of the second heart sound waveform in respiration shows that the aortic component has little variation and the delay of the pulmonary component is modulated by respiration. This paper proposes a novel model to discriminate the aortic and pulmonary components using respiratory modulation. It is found that the aortic component could be simply extracted by averaging the second heart sounds over respiratory phase, and the pulmonary component could be extracted by subtraction. Hence, the split is measured by the timing difference of the two components. To validate the measurement, the method is applied to simulated second heart sounds with known varying splits. The simulation results show that the aortic and pulmonary components can be successfully extracted and the measured splits are close to the predefined splits. The method is further evaluated by data collected from 12 healthy subjects. Experimental results show that the respiratory split can be accurately measured. The minimum split generally occurs at the end of expiration and the split value is about 20 ms. Meanwhile, the maximum split is about 50 ms at the end of inspiration. Both the trend of split varying with respect to respiratory phase and the numerical range of split varying are comparable to the results disclosed by previous physiologists. The proposed method is compared to the two previous well known methods. The most attractive advantage of the proposed method is much less complexity. This method has potential applications in monitoring heart hemodynamic response to respiration.

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Section: Physiology Of Respiratory Split In S2mentioning

confidence: 99%

Discrimination of Aortic and Pulmonary Components from the Second Heart Sound Using Respiratory Modulation and Measurement of Respiratory Split

Tang

Chen

2017

Applied Sciences

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“…This study complements our previous findings indicating amplitude and sound morphology changes due to intrathoracic pressure changes. 23 Positive pressure PP that was established during surgery functioned as a good physiological model, enabling sufficient hemodynamic fluctuations that were appropriate to delineate such correlation. There was no need for vasomotor drugs with potential harmful side effects, such as those used previously in trials involving animals or human volunteers.…”

Section: Discussionmentioning

confidence: 99%

“…This was based on tools that we have recently developed for fine segmentation and morphological analysis. 18,23 Following the segmentation of S1 and the second heart sound, we analyzed the morphology of the sounds using hierarchical cluster analysis to remove from the statistical analysis those sounds that were not reliably recorded. A few heartbeat sounds were excluded as a result of the patient's movement or too high background noise in the operating room.…”

Section: Methodsmentioning

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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“…Amit ve ark. (14), çalışmalarında kalp seslerinin morfolojisinin solunuma bağlı olarak değiştiğini, soluk tutulması durumunda kalp seslerinin genliğinde azalma olduğunu gözlemlemişlerdir. Kablosuz olarak da üretilebilen elektronik bir steteskop ile kaydedilebilecek kalp seslerindeki değişimlerin uyku apnesi tespitinde kullanılabileceği gösterilebilirse, bu durum evde kullanıma uygun, hastaya en az rahatsızlığı verecek, maliyeti düşük bir karar destek sisteminin yolunu açabilecektir.…”

Section: Introductionunclassified

Can Sleep Apnea be Detected by Heart Sounds?

Yıldız¹,

Tabak²,

Yetkin³

2017

jtsm

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Objective: It has previously been shown that there are morphological changes in hearth sounds during respiration and holding breath. In this study, for the first time in the literature, it was investigated whether sleep apnea could be detected automatically from heart sounds by teaching various classifiers of time and frequency plane parameters which are thought to be able to characterize the morphological changes seen in heart sounds during apnea. Materials and Methods: For this purpose, heart sounds were recorded simultaneously with full polysomnography records from 17 people. Classification studies were performed by assigning feature vectors obtained from heart sounds to K nearest neighbors and support vector machines. Results: The best result with K nearest neighbor classifier was 48% accuracy, 100% selectivity level. With support vector machines classifier, 82% accuracy and 42% selectivity values were reached. Conclusion: According to these values, it is concluded that the parameters of the heart sound used in this study do not make it possible to diagnose the sleep apnea from the heart sounds. Keywords: Sleep apnea, heart sounds, polysomnography, classifiers Amaç: Nefes alıp verme ve nefes tutma sırasında kalp seslerinde morfolojik değişimler olduğu daha önce gösterilmiştir. Bu çalışmada, literatürde ilk defa olmak üzere; apne sırasında kalp seslerinde görülen morfolojik değişimleri karakterize edebileceği düşünülen zaman ve frekans düzlemi parametrelerinin çeşitli sınıflandırıcılara öğretilerek kalp seslerinden otomatik olarak uyku apnesi tespiti yapılıp yapılamayacağı araştırılmıştır. Gereç ve Yöntem: Bu amaçla 17 kişiden tam polisomnografi kayıtları ile eş zamanlı olarak kalp sesleri kaydedilmiştir. K en yakın komşu ve destek vektör makineleri sınıflandırıcılarına, kalp seslerinden elde edilen özellik vektörleri verilerek sınıflandırma çalışmaları yapılmıştır. Bulgular: K en yakın komşu sınıflandırıcısıyla alınan en iyi sonuç, %48 doğruluk, %100 seçicilik seviyesinde olmuştur. Destek vektör makineleri sınıflandırıcısıyla ise %82 doğruluk, %42 seçicilik değerlerine ulaşılmıştır. Sonuç: Ulaşılan bu değerlere göre, çalışmada kullanılan kalp sesine ait parametreler ile kalp seslerinden uyku apnesi teşhisi koymanın mümkün olmadığı sonucuna varılmıştır. Anahtar Kelimeler: Uyku apnesi, kalp sesleri, polisomnografi, sınıflandırıcılar Abstract ÖzÖzgün Makale / Original Article DO I: 10.4274/jtsm.07108 2017;4:16-21 Can Sleep Apnea be Detected by Heart Sounds? Journal of Turkish Sleep MedicineMetin Yıldız, Zeynep Tabak*, Sinan Yetkin** GirişUyku ile ilgili rahatsızlıkların önde gelenlerinden biri, uykuda solunum bozukluklarıdır. Bunlar içerisinde en sık rastlanan ise uyku apne sendromudur. Her yaş grubundaki bireylerde görülebilmekle beraber, ilerleyen yaşlarda görülme sıklığı artmaktadır. Dar kapsamlı birkaç çalışmada, Türkiye'de görülme sıklığını %0,9-1,9 olarak tespit edilmiştir (1,2). Farklı toplumlarda yapılan çalışmalarda görülme sıklıkları erkeklerde %3,1-%7,5, kadınlarda %2,1-%4,5 olarak tespit edilmişt...

show abstract

Respiratory modulation of heart sound morphology

Cited by 30 publications

References 25 publications

Discrimination of Aortic and Pulmonary Components from the Second Heart Sound Using Respiratory Modulation and Measurement of Respiratory Split

Discrimination of Aortic and Pulmonary Components from the Second Heart Sound Using Respiratory Modulation and Measurement of Respiratory Split

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

Can Sleep Apnea be Detected by Heart Sounds?

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