Journal articles: 'Heart Sounds'

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Pechetty, Ramya, and Lalita Nemani. "Additional Heart Sounds—Part 1 (Third and Fourth Heart Sounds)." Indian Journal of Cardiovascular Disease in Women WINCARS 5, no. 02 (June 2020): 155–64. http://dx.doi.org/10.1055/s-0040-1713828.

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AbstractS3 is a low-pitched sound (25–50Hz) which is heard in early diastole, following the second heart sound. The following synonyms are used for it: ventricular gallop, early diastolic gallop, protodiastolic gallop, and ventricular early filling sound. The term “gallop” was first used in 1847 by Jean Baptiste Bouillaud to describe the cadence of the three heart sounds occurring in rapid succession. The best description of a third heart sound was provided by Pierre Carl Potain who described an added sound which, in addition to the two normal sounds, is heard like a bruit completing the triple rhythm of the heart (bruit de gallop). The following synonyms are used for the fourth heart sound (S4): atrial gallop and presystolic gallop. S4 is a low-pitched sound (20–30 Hz) heard in presystole, i.e., shortly before the first heart sound. This produces a rhythm classically compared with the cadence of the word “Tennessee.” One can also use the phrase “A-stiff-wall” to help with the cadence (a S4, stiff S1, wall S2) of the S4 sound.

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Mamorita, Noritaka, Naoya Arisaka, Risa Isonaka, Tadashi Kawakami, and Akihiro Takeuchi. "Development of a Smartphone App for Visualizing Heart Sounds and Murmurs." Cardiology 137, no. 3 (2017): 193–200. http://dx.doi.org/10.1159/000466683.

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Background: Auscultation is one of the basic techniques for the diagnosis of heart disease. However, the interpretation of heart sounds and murmurs is a highly subjective and difficult skill. Objectives: To assist the auscultation skill at the bedside, a handy phonocardiogram was developed using a smartphone (Samsung Galaxy J, Android OS 4.4.2) and an external microphone attached to a stethoscope. Methods and Results: The Android app used Java classes, “AudioRecord,” “AudioTrack,” and “View,” that recorded sounds, replayed sounds, and plotted sound waves, respectively. Sound waves were visualized in real-time, simultaneously replayed on the smartphone, and saved to WAV files. To confirm the availability of the app, 26 kinds of heart sounds and murmurs sounded on a human patient simulator were recorded using three different methods: a bell-type stethoscope, a diaphragm-type stethoscope, and a direct external microphone without a stethoscope. The recorded waveforms were subjectively confirmed and were found to be similar to the reference waveforms. Conclusions: The real-time visualization of the sound waves on the smartphone may help novices to readily recognize and learn to distinguish the various heart sounds and murmurs in real-time.

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DEBBAL, S. M., and F. BEREKSI-REGUIG. "COMPARISON BETWEEN DISCRETE AND PACKET WAVELET TRANSFORM ANALYSES IN THE STUDY OF HEARTBEAT CARDIAC SOUNDS." Journal of Mechanics in Medicine and Biology 07, no. 02 (June 2007): 199–214. http://dx.doi.org/10.1142/s021951940700225x.

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This work investigates the study of heartbeat cardiac sounds through time–frequency analysis by using the wavelet transform method. Heart sounds can be utilized more efficiently by medical doctors when they are displayed visually rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and nonstationary that they are very difficult to analyze in the time or frequency domain. We have studied the extraction of features from heart sounds in the time–frequency (TF) domain for the recognition of heart sounds through TF analysis. The application of wavelet transform (WT) for heart sounds is thus described. The performances of discrete wavelet transform (DWT) and wavelet packet transform (WP) are discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify the clinical usefulness of our extraction methods for the recognition of heart sounds.

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Huffman, Lisa M. "Heart sounds." Nursing Made Incredibly Easy! 10, no. 2 (2012): 51–54. http://dx.doi.org/10.1097/01.nme.0000411098.98692.72.

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Treadway, Katharine. "Heart Sounds." New England Journal of Medicine 354, no. 11 (March 16, 2006): 1112–13. http://dx.doi.org/10.1056/nejmp058202.

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Cheng, Tsung O. "Heart sounds." International Journal of Cardiology 135, no. 3 (July 2009): 405. http://dx.doi.org/10.1016/j.ijcard.2008.02.018.

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Martin, Lee. "Heart Sounds." River Teeth: A Journal of Nonfiction Narrative 16, no. 1 (2014): 31–46. http://dx.doi.org/10.1353/rvt.2014.0022.

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HAMZA CHERIF, L., S. M. DEBBAL, and F. BEREKSI-REGUIG. "SEGMENTATION OF HEART SOUNDS AND HEART MURMURS." Journal of Mechanics in Medicine and Biology 08, no. 04 (December 2008): 549–59. http://dx.doi.org/10.1142/s0219519408002759.

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Heart murmurs are often the first signs of pathological changes of the heart valves, and are usually found during auscultation in primary health care. Many pathological conditions of the cardiovascular system cause murmurs and aberrations in heart sounds. Phonocardiography provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography, combined with modern digital processing techniques, has strongly renewed researchers' interest in studying heart sounds and murmurs. This paper presents an algorithm for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs. The segmentation algorithm, which separates the heart signal (or the phonocardiogram (PCG) signal), is based on the normalized average Shannon energy of the PCG signal. This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs to give an assessment of their average duration.

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Zhang, Lu. "Design of Heart Sound Analyzer." Advanced Materials Research 1042 (October 2014): 131–34. http://dx.doi.org/10.4028/www.scientific.net/amr.1042.131.

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There is important physiological and pathological information in heart sound, so the patients’ information can be obtained by detection of their heart sounds. In the hardware of the system, the heart sound sensor HKY06B is used to acquire the heart sound signal, and the DSP chip TMS320VC5416 is used to process the heart sound. De-noising based on wavelet and HHT and other technical are used in the process of heart sound. There are five steps in the system: acquisition, de-noising, segmentation, feature extraction, and finally, heart sounds are classified

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Tao, Ye Wei, Xie Feng Cheng, Shu Yang He, Yan Ping Ge, and Yan Hong Huang. "Heart Sound Signal Generator Based on LabVIEW." Applied Mechanics and Materials 121-126 (October 2011): 872–76. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.872.

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A heart sounds signal generator in the heart sound analysis instrument based on the LabVIEW is devised. The instrument is developed in PC. Heart sounds signal generator can according to need to produce a synthetic heart sounds signal for users to learn and use. The parameters setting are also discussed to find out the best for the each part. All the parameters can be set by user and the best ones are default values so that the instrument can fit other environment. The running test of this instrument proves it can generate and play heart sound precisely，and can be used as an assistance to show, play, and analyze heart sound

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Lee, Soomin, Qun Wei, Heejoon Park, Yuri Na, Donghwa Jeong, and Hongjoon Lim. "Development of a Finger-Ring-Shaped Hybrid Smart Stethoscope for Automatic S1 and S2 Heart Sound Identification." Sensors 21, no. 18 (September 20, 2021): 6294. http://dx.doi.org/10.3390/s21186294.

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Cardiac auscultation is one of the most popular diagnosis approaches to determine cardiovascular status based on listening to heart sounds with a stethoscope. However, heart sounds can be masked by visceral sounds such as organ movement and breathing, and a doctor’s level of experience can more seriously affect the accuracy of auscultation results. To improve the accuracy of auscultation, and to allow nonmedical staff to conduct cardiac auscultation anywhere and anytime, a hybrid-type personal smart stethoscope with an automatic heart sound analysis function is presented in this paper. The device was designed with a folding finger-ring shape that can be worn on the finger and placed on the chest to measure photoplethysmogram (PPG) signals and acquire the heart sound simultaneously. The measured heart sounds are detected as phonocardiogram (PCG) signals, and the boundaries of the heart sound variation and the peaks of the PPG signal are detected in preprocessing by an advanced Shannon entropy envelope. According to the relationship between PCG and PPG signals, an automatic heart sound analysis algorithm based on calculating the time interval between the first and second heart sounds (S1, S2) and the peak of the PPG was developed and implemented via the manufactured prototype device. The prototype device underwent accuracy and usability testing with 20 young adults, and the experimental results showed that the proposed smart stethoscope could satisfactorily collect the heart sounds and PPG signals. In addition, within the developed algorithm, the device was as accurate in start-points of heart sound detection as professional physiological signal-acquisition systems. Furthermore, the experimental results demonstrated that the device was able to identify S1 and S2 heart sounds automatically with high accuracy.

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Cheng, Xiefeng, Pengfei Wang, and Chenjun She. "Biometric Identification Method for Heart Sound Based on Multimodal Multiscale Dispersion Entropy." Entropy 22, no. 2 (February 20, 2020): 238. http://dx.doi.org/10.3390/e22020238.

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In this paper, a new method of biometric characterization of heart sounds based on multimodal multiscale dispersion entropy is proposed. Firstly, the heart sound is periodically segmented, and then each single-cycle heart sound is decomposed into a group of intrinsic mode functions (IMFs) by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). These IMFs are then segmented to a series of frames, which is used to calculate the refine composite multiscale dispersion entropy (RCMDE) as the characteristic representation of heart sound. In the simulation experiments I, carried out on the open heart sounds database Michigan, Washington and Littman, the feature representation method was combined with the heart sound segmentation method based on logistic regression (LR) and hidden semi-Markov models (HSMM), and feature selection was performed through the Fisher ratio (FR). Finally, the Euclidean distance (ED) and the close principle are used for matching and identification, and the recognition accuracy rate was 96.08%. To improve the practical application value of this method, the proposed method was applied to 80 heart sounds database constructed by 40 volunteer heart sounds to discuss the effect of single-cycle heart sounds with different starting positions on performance in experiment II. The experimental results show that the single-cycle heart sound with the starting position of the start of the first heart sound (S1) has the highest recognition rate of 97.5%. In summary, the proposed method is effective for heart sound biometric recognition.

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Liu, Wu Chang, Hai Bin Wang, Jin Qun Liu, Yu Fang, and Zhu Qin Li. "A Heart Sound Acquisition and Analysis System." Advanced Materials Research 341-342 (September 2011): 504–8. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.504.

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This paper focuses on a heart sound acquisition and analysis system for in-home use of heart condition monitoring. The acquisition board is comprised of a transducer and the processing module, which is used to detect and send heart sound signal to PC via USB interface for heart sound analysis. The analysis software identifies normal and abnormal heart sounds, and provides an easy understanding graphical representation. Both normal and abnormal heart sounds are used to verify the validity of this system, and the result shows the system can discriminate normal and abnormal heart sounds. It is envisaged that the system can eventually be used for in-home use of heart condition monitoring that even inexperienced users are also able to monitor heart condition easily.

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Nazeran, H. "Wavelet-based Segmentation and Feature Extraction of Heart Sounds for Intelligent PDA-based Phonocardiography." Methods of Information in Medicine 46, no. 02 (2007): 135–41. http://dx.doi.org/10.1055/s-0038-1625394.

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Summary Objectives : Many pathological conditions of the cardiovascular system cause murmurs and aberrations in heart sounds. Phonocardiography provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography combined with modern digital signal processing techniques has strongly renewed researchers' interest in studying heart sounds and murmurs.The aim of this work is to investigate the applicability of different spectral analysis methods to heart sound signals and explore their suitability for PDA-based implementation. Methods : Fourier transform (FT), short-time Fourier transform (STFT) and wavelet transform (WT) are used to perform spectral analysis on heart sounds. A segmentation algorithm based on Shannon energy is used to differentiate between first and second heartsounds. Then wavelet transform is deployed again to extract 64 features of heart sounds. Results : The FT provides valuable frequency information but the timing information is lost during the transformation process. The STFT or spectrogram provides valuable time-frequency information but there is a trade-off between time and frequency resolution. Waveletanalysis, however, does not suffer from limitations of the STFT and provides adequate time and frequency resolution to accurately characterize the normal and pathological heartsounds. Conclusions : The results show that the wavelet-based segmentation algorithm is quite effective in localizing the important components of both normal and abnormal heart sounds. They also demonstrate that wavelet-based feature extraction provides suitable feature vectors which are clearly differentiable and useful for automatic classification of heart sounds.

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Safara, Fatemeh, Shyamala Doraisamy, Azreen Azman, Azrul Jantan, and Sri Ranga. "Wavelet Packet Entropy for Heart Murmurs Classification." Advances in Bioinformatics 2012 (November 25, 2012): 1–6. http://dx.doi.org/10.1155/2012/327269.

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Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.

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Chen, Lingguang, Sean F. Wu, Yong Xu, William D. Lyman, and Gaurav Kapur. "Blind Separation of Heart Sounds." Journal of Theoretical and Computational Acoustics 26, no. 01 (March 2018): 1750035. http://dx.doi.org/10.1142/s2591728517500359.

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This paper presents a theoretical foundation for the newly developed methodology that enables the prediction of blood pressures based on the heart sounds measured directly on the chest of a patient. The key to this methodology is the separation of heart sounds into first heart sound and second heart sound, from which components attributable to four heart valves, i.e.: mitral; tricuspid; aortic; and pulmonary valve-closure sounds are separated. Since human physiology and anatomy can vary among people and are unknown a priori, such separation is called blind source separation. Moreover, the sources locations, their surroundings and boundary conditions are unspecified. Consequently, it is not possible to obtain an exact separation of signals. To circumvent this difficulty, we extend the point source separation method in this paper to an inhomogeneous fluid medium, and further combine it with iteration schemes to search for approximate source locations and signal propagation speed. Once these are accomplished, the signals emitted from individual sources are separated by deconvoluting mixed signals with respect to the identified sources. Both numerical simulation example and experiment have demonstrated that this approach can provide satisfactory source separation results.

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Golpaygani, Ali Tavakoli, Nahid Abolpour, Kamran Hassani, Kourosh Bajelani, and D. John Doyle. "Detection and identification of S1 and S2 heart sounds using wavelet decomposition method." International Journal of Biomathematics 08, no. 06 (October 15, 2015): 1550078. http://dx.doi.org/10.1142/s1793524515500783.

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Phonocardiogram (PCG), the digital recording of heart sounds is becoming increasingly popular as a primary detection system for diagnosing heart disorders and it is relatively inexpensive. Electrocardiogram (ECG) is used during the PCG in order to identify the systolic and diastolic parts manually. In this study a heart sound segmentation algorithm has been developed which separates the heart sound signal into these parts automatically. This study was carried out on 100 patients with normal and abnormal heart sounds. The algorithm uses discrete wavelet decomposition and reconstruction to produce PCG intensity envelopes and separates that into four parts: the first heart sound, the systolic period, the second heart sound and the diastolic period. The performance of the algorithm has been evaluated using 14,000 cardiac periods from 100 digital PCG recordings, including normal and abnormal heart sounds. In tests, the algorithm was over 93% correct in detecting the first and second heart sounds. The presented automatic segmentation algorithm using wavelet decomposition and reconstruction to select suitable frequency band for envelope calculations has been found to be effective to segment PCG signals into four parts without using an ECG.

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TAYLOR, DOLORES LAKE. "ASSESSING HEART SOUNDS." Nursing 15, no. 1 (January 1985): 51–53. http://dx.doi.org/10.1097/00152193-198501000-00011.

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Shahmohammadi, Mehrdad, Hongxing Luo, Philip Westphal, Richard N. Cornelussen, Frits W. Prinzen, and Tammo Delhaas. "Hemodynamics-driven mathematical model of first and second heart sound generation." PLOS Computational Biology 17, no. 9 (September 22, 2021): e1009361. http://dx.doi.org/10.1371/journal.pcbi.1009361.

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We propose a novel, two-degree of freedom mathematical model of mechanical vibrations of the heart that generates heart sounds in CircAdapt, a complete real-time model of the cardiovascular system. Heart sounds during rest, exercise, biventricular (BiVHF), left ventricular (LVHF) and right ventricular heart failure (RVHF) were simulated to examine model functionality in various conditions. Simulated and experimental heart sound components showed both qualitative and quantitative agreements in terms of heart sound morphology, frequency, and timing. Rate of left ventricular pressure (LV dp/dtmax) and first heart sound (S1) amplitude were proportional with exercise level. The relation of the second heart sound (S2) amplitude with exercise level was less significant. BiVHF resulted in amplitude reduction of S1. LVHF resulted in reverse splitting of S2 and an amplitude reduction of only the left-sided heart sound components, whereas RVHF resulted in a prolonged splitting of S2 and only a mild amplitude reduction of the right-sided heart sound components. In conclusion, our hemodynamics-driven mathematical model provides fast and realistic simulations of heart sounds under various conditions and may be helpful to find new indicators for diagnosis and prognosis of cardiac diseases. New & noteworthy To the best of our knowledge, this is the first hemodynamic-based heart sound generation model embedded in a complete real-time computational model of the cardiovascular system. Simulated heart sounds are similar to experimental and clinical measurements, both quantitatively and qualitatively. Our model can be used to investigate the relationships between heart sound acoustic features and hemodynamic factors/anatomical parameters.

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Narváez, Pedro, and Winston S. Percybrooks. "Synthesis of Normal Heart Sounds Using Generative Adversarial Networks and Empirical Wavelet Transform." Applied Sciences 10, no. 19 (October 8, 2020): 7003. http://dx.doi.org/10.3390/app10197003.

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Currently, there are many works in the literature focused on the analysis of heart sounds, specifically on the development of intelligent systems for the classification of normal and abnormal heart sounds. However, the available heart sound databases are not yet large enough to train generalized machine learning models. Therefore, there is interest in the development of algorithms capable of generating heart sounds that could augment current databases. In this article, we propose a model based on generative adversary networks (GANs) to generate normal synthetic heart sounds. Additionally, a denoising algorithm is implemented using the empirical wavelet transform (EWT), allowing a decrease in the number of epochs and the computational cost that the GAN model requires. A distortion metric (mel–cepstral distortion) was used to objectively assess the quality of synthetic heart sounds. The proposed method was favorably compared with a mathematical model that is based on the morphology of the phonocardiography (PCG) signal published as the state of the art. Additionally, different heart sound classification models proposed as state-of-the-art were also used to test the performance of such models when the GAN-generated synthetic signals were used as test dataset. In this experiment, good accuracy results were obtained with most of the implemented models, suggesting that the GAN-generated sounds correctly capture the characteristics of natural heart sounds.

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Debbal, Sid. "Heart cardiac’s sounds signals segmentation by using the discrete wavelet transform (DWT)." Biomedical Research and Clinical Reviews 4, no. 3 (July 23, 2021): 01–15. http://dx.doi.org/10.31579/2692-9406/052.

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The presence of abnormal sounds in one cardiac cycle, provide valuable information on various diseases.Early detection of various diseases is necessary; it is done by a simple technique known as: phonocardiography. The phonocardiography, based on registration of vibrations or oscillations of different frequencies, audible or not, that correspond to normal and abnormal heart sounds. It provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography, combined with signal processing techniques, has strongly renewed researchers’ interest in studying heart sounds and murmurs. This paper presents an algorithm based on the denoising by wavelet transform (DWT) and the Shannon energy of the PCG signal, for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs. This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs to give an assessment of their average duration.

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Kurniadi, Dedi, Surfa Yondri, Albar, Roza Susanti, David Eka Putra, and Gwo-Jia Jong. "Optimization Audicor for Normal and Abnormal Heart Sounds Characteristic." International Journal on Data Science 1, no. 2 (May 26, 2020): 99–106. http://dx.doi.org/10.18517/ijods.1.2.99-106.2020.

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Heart Sounds are important things in the human body that can deliver information related to the heart condition. However, a recorded signal such as PCG and ECG that getting through Audicor still contain unexpected components or noise while the recording process happens it makes the result data from Audicor cannot directly use to recognize the condition of the heart. This research presents signal processing and data analysis to suppress the noise of the heart sounds that getting while the process of recording data happens. The cleaned heart sound will be processed in feature extraction by using FFT and PCA that capable to produce the feature both of the normal and abnormal heart sounds. For the normal case, we get the data from some healthy volunteers recorded by using Audicor. While the abnormal heart sound we focus to observe the data that contain Ventricular Septal Defect (VSD) that getting from a partner hospital. As a result, feature both normal and abnormal heart sounds can be separated.

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She, Chen-Jun, and Xie-Feng Cheng. "Design framework of hybrid ensemble identification network and its application in heart sound analysis." AIP Advances 12, no. 4 (April 1, 2022): 045117. http://dx.doi.org/10.1063/5.0083764.

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Mixed heart sounds include heart sounds in a state of resting and motion. The analysis of heart sound signals in a state of motion is a difficult problem. (1) First, the mixed heart sound signal was collected by using the shoulder-strap-type heart sound acquisition device designed and made by our research group. The acquisition scheme and data preprocessing method were given, and the characteristics of heart sound signals in a state of motion were analyzed. (2) The design framework of the Hybrid Ensemble Identification Network (HEINet) is proposed, and the design requirements, architecture principles, and detailed design steps are discussed. The design process is simple, fast, and convenient. (3) In this paper, according to the design framework of HEINet, HEINet of the mixed heart sound signal is designed, and the recognition rate of the mixed heart sound signal in biometric authentication has reached 99.1%. Based on this design framework, HEINet of the heart sound signal for the Heart Sounds Catania 2011 heart sound database and HEINet of the electrocardiogram signal for Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database were designed, and the recognition rates both met the expected requirements. It shows that the design framework of HEINet has obvious universality.

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She, Chen-Jun, Xie-Feng Cheng, and Kai Wang. "Analysis of Heart-Sound Characteristics during Motion Based on a Graphic Representation." Sensors 22, no. 1 (December 28, 2021): 181. http://dx.doi.org/10.3390/s22010181.

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In this paper, the graphic representation method is used to study the multiple characteristics of heart sounds from a resting state to a state of motion based on single- and four-channel heart-sound signals. Based on the concept of integration, we explore the representation method of heart sound and blood pressure during motion. To develop a single- and four-channel heart-sound collector, we propose new concepts such as a sound-direction vector of heart sound, a motion–response curve of heart sound, the difference value, and a state-change-trend diagram. Based on the acoustic principle, the reasons for the differences between multiple-channel heart-sound signals are analyzed. Through a comparative analysis of four-channel motion and resting-heart sounds, from a resting state to a state of motion, the maximum and minimum similarity distances in the corresponding state-change-trend graphs were found to be 0.0038 and 0.0006, respectively. In addition, we provide several characteristic parameters that are both sensitive (such as heart sound amplitude, blood pressure, systolic duration, and diastolic duration) and insensitive (such as sound-direction vector, state-change-trend diagram, and difference value) to motion, thus providing a new technique for the diverse analysis of heart sounds in motion.

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Tang, Hong, Ting Li, Tianshuang Qiu, and Yongwan Park. "Fetal Heart Rate Monitoring from Phonocardiograph Signal Using Repetition Frequency of Heart Sounds." Journal of Electrical and Computer Engineering 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/2404267.

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As a passive, harmless, and low-cost diagnosis tool, fetal heart rate (FHR) monitoring based on fetal phonocardiography (fPCG) signal is alternative to ultrasonographic cardiotocography. Previous fPCG-based methods commonly relied on the time difference of detected heart sound bursts. However, the performance is unavoidable to degrade due to missed heart sounds in very low signal-to-noise ratio environments. This paper proposes a FHR monitoring method using repetition frequency of heart sounds. The proposed method can track time-varying heart rate without both heart sound burst identification and denoising. The average accuracy rate comparison to benchmark is 88.3% as the SNR ranges from −4.4 dB to −26.7 dB.

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Lenz, Isabella, Yu Rong, and Daniel Bliss. "Contactless Stethoscope Enabled by Radar Technology." Bioengineering 10, no. 2 (January 28, 2023): 169. http://dx.doi.org/10.3390/bioengineering10020169.

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Contactless vital sign measurement technologies have the potential to greatly improve patient experiences and practitioner safety while creating the opportunity for comfortable continuous monitoring. We introduce a contactless alternative for measuring human heart sounds. We leverage millimeter wave frequency-modulated continuous wave radar and multi-input multi-output beamforming techniques to capture fine skin vibrations that result from the cardiac movements that cause heart sounds. We discuss contact-based heart sound measurement techniques and directly compare the radar heart sound technique with these contact-based approaches. We present experimental cases to test the strengths and limitations of both the contact-based measurement techniques and the contactless radar measurement. We demonstrate that the radar measurement technique is a viable and potentially superior method for capturing human heart sounds in many practical settings.

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Chowdhury, Muhammad E. H., Amith Khandakar, Khawla Alzoubi, Samar Mansoor, Anas M. Tahir, Mamun Bin Ibne Reaz, and Nasser Al-Emadi. "Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring." Sensors 19, no. 12 (June 20, 2019): 2781. http://dx.doi.org/10.3390/s19122781.

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One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient’s heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.

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Guo, Binbin, Hong Tang, Shufeng Xia, Miao Wang, Yating Hu, and Zehang Zhao. "Development of a Multi-Channel Wearable Heart Sound Visualization System." Journal of Personalized Medicine 12, no. 12 (December 4, 2022): 2011. http://dx.doi.org/10.3390/jpm12122011.

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A multi-channel wearable heart sound visualization system based on novel heart sound sensors for imaging cardiac acoustic maps was developed and designed. The cardiac acoustic map could be used to detect cardiac vibration and heart sound propagation. The visualization system acquired 72 heart sound signals and one ECG signal simultaneously using 72 heart sound sensors placed on the chest surface and one ECG analog front end. The novel heart sound sensors had the advantages of high signal quality, small size, and high sensitivity. Butterworth filtering and wavelet transform were used to reduce noise in the signals. The cardiac acoustic map was obtained based on the cubic spline interpolation of the heart sound signals. The results showed the heart sound signals on the chest surface could be detected and visualized by this system. The variations of heart sounds were clearly displayed. This study provided a way to select optimal position for auscultation of heart sounds. The visualization system could provide a technology for investigating the propagation of heart sound in the thoracic cavity.

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Li, Suyi, Feng Li, Shijie Tang, and Wenji Xiong. "A Review of Computer-Aided Heart Sound Detection Techniques." BioMed Research International 2020 (January 10, 2020): 1–10. http://dx.doi.org/10.1155/2020/5846191.

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Cardiovascular diseases have become one of the most prevalent threats to human health throughout the world. As a noninvasive assistant diagnostic tool, the heart sound detection techniques play an important role in the prediction of cardiovascular diseases. In this paper, the latest development of the computer-aided heart sound detection techniques over the last five years has been reviewed. There are mainly the following aspects: the theories of heart sounds and the relationship between heart sounds and cardiovascular diseases; the key technologies used in the processing and analysis of heart sound signals, including denoising, segmentation, feature extraction and classification; with emphasis, the applications of deep learning algorithm in heart sound processing. In the end, some areas for future research in computer-aided heart sound detection techniques are explored, hoping to provide reference to the prediction of cardiovascular diseases.

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Gavrovska, Ana, Goran Zajić, Vesna Bogdanović, Irini Reljin, and Branimir Reljin. "Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context." Complexity 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1580414.

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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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Meikle, Ashley, Amal Fayruz Adan, Findlay Smith, Marla Bragagnolo, Neal Uren, Cian Murphy, and Anitha Varghese. "Heart sounds in motion." Clinical Medicine 19, Suppl 2 (March 2019): s148. http://dx.doi.org/10.7861/clinmedicine.19-2-s148.

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CORDERO, SUSAN BRODHEIM. "Assessing fetal heart sounds." Nursing 33, no. 10 (October 2003): 54–55. http://dx.doi.org/10.1097/00152193-200310000-00054.

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Stevens, Cynthia. "Understanding Pediatric Heart Sounds." Journal of Cardiovascular Nursing 7, no. 1 (October 1992): 78–79. http://dx.doi.org/10.1097/00005082-199210000-00012.

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Aroney, Con. "Heart Sounds Made Easy." Heart, Lung and Circulation 13, no. 2 (June 2004): 199–200. http://dx.doi.org/10.1016/j.hlc.2004.02.011.

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Gibson, Lorna M. "Heart sounds and murmurs." BMJ 336, Suppl S6 (June 1, 2008): 0806255a. http://dx.doi.org/10.1136/sbmj.0806255a.

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Debbal, S. M., and F. Bereksi-Reguig. "Computerized heart sounds analysis." Computers in Biology and Medicine 38, no. 2 (February 2008): 263–80. http://dx.doi.org/10.1016/j.compbiomed.2007.09.006.

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Lewkowicz, M., and M. Gitterman. "Theory of heart sounds." Journal of Sound and Vibration 117, no. 2 (September 1987): 263–75. http://dx.doi.org/10.1016/0022-460x(87)90538-4.

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Diehl, Antoni M. "Understanding Pediatric Heart Sounds." JAMA: The Journal of the American Medical Association 268, no. 23 (December 16, 1992): 3380. http://dx.doi.org/10.1001/jama.1992.03490230110045.

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Iyer, V. K., P. A. Ramamoorthy, and Y. Ploysongsang. "Quantification of heart sounds interference with lung sounds." Journal of Biomedical Engineering 11, no. 2 (March 1989): 164–65. http://dx.doi.org/10.1016/0141-5425(89)90129-5.

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Giordano, Noemi, and Marco Knaflitz. "A Novel Method for Measuring the Timing of Heart Sound Components through Digital Phonocardiography." Sensors 19, no. 8 (April 19, 2019): 1868. http://dx.doi.org/10.3390/s19081868.

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The auscultation of heart sounds has been for decades a fundamental diagnostic tool in clinical practice. Higher effectiveness can be achieved by recording the corresponding biomedical signal, namely the phonocardiographic signal, and processing it by means of traditional signal processing techniques. An unavoidable processing step is the heart sound segmentation, which is still a challenging task from a technical viewpoint—a limitation of state-of-the-art approaches is the unavailability of trustworthy techniques for the detection of heart sound components. The aim of this work is to design a reliable algorithm for the identification and the classification of heart sounds’ main components. The proposed methodology was tested on a sample population of 24 healthy subjects over 10-min-long simultaneous electrocardiographic and phonocardiographic recordings and it was found capable of correctly detecting and classifying an average of 99.2% of the heart sounds along with their components. Moreover, the delay of each component with respect to the corresponding R-wave peak and the delay among the components of the same heart sound were computed: the resulting experimental values are coherent with what is expected from the literature and what was obtained by other studies.

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Hall, Angela. "Heart sounds: auscultation for valvular heart disease." British Journal of Cardiac Nursing 13, no. 1 (January 2, 2018): 12–18. http://dx.doi.org/10.12968/bjca.2018.13.1.12.

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Andrisevic, Nicholas, Khaled Ejaz, Fernando Rios-Gutierrez, Rocio Alba-Flores, Glenn Nordehn, and Stanley Burns. "Detection of Heart Murmurs Using Wavelet Analysis and Artificial Neural Networks." Journal of Biomechanical Engineering 127, no. 6 (July 8, 2005): 899–904. http://dx.doi.org/10.1115/1.2049327.

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This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%.

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Mohamed, Nourelhuda, Hyun-Seok Kim, Kyu-Min Kang, Manal Mohamed, Sung-Hoon Kim, and Jae Gwan Kim. "Heart and Lung Sound Measurement Using an Esophageal Stethoscope with Adaptive Noise Cancellation." Sensors 21, no. 20 (October 12, 2021): 6757. http://dx.doi.org/10.3390/s21206757.

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In surgeries where general anesthesia is required, the auscultation of heart and lung sounds is essential to provide information on the patient’s cardiorespiratory system. Heart and lung sounds can be recorded using an esophageal stethoscope; however, there is huge background noise when this device is used in an operating room. In this study, a digital esophageal stethoscope system was designed. A 3D-printed case filled with Polydimethylsiloxane material was designed to hold two electret-type microphones. One of the microphones was placed inside the printed case to collect the heart and lung sound signals coming out from the patient through the esophageal catheter, the other was mounted on the surface of the case to collect the operating room sounds. A developed adaptive noise canceling algorithm was implemented to remove the operating room noise corrupted with the main heart and lung sound signals and the output signal was displayed on software application developed especially for this study. Using the designed case, the noise level of the signal was reduced to some extent, and by adding the adaptive filter, further noise reduction was achieved. The designed system is lightweight and can provide noise-free heart and lung sound signals.

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DJEBBARI, ABDELGHANI, and F. BEREKSI-REGUIG. "SMOOTHED-PSEUDO WIGNER–VILLE DISTRIBUTION OF NORMAL AND AORTIC STENOSIS HEART SOUNDS." Journal of Mechanics in Medicine and Biology 05, no. 03 (September 2005): 415–28. http://dx.doi.org/10.1142/s0219519405001552.

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In this paper, we are interested in the acquisition and the time-frequency analysis of the Phonocardiogram (PCG) signal. The interactive software "PCG Recorder" we implemented in MATLAB, drives the sound card of a personal computer for acquisition purposes. Normal and abnormal heart sounds were acquired with 16 bits resolution and at high sampling frequencies; the value 2 kHz was selected as sampling rate to avoid spectral aliasing. For each patient, additional information like the age, the gender, the weight as well as the auscultation area can be introduced within the saved data file. The aortic, the tricuspid, the mitral and the pulmonic areas are considered for the acquisition task. The Smoothed-Pseudo Wigner–Ville Distribution (SPWVD) yield adequate Time-Frequency Representations (TFRs) of such non-stationary signal as heart sounds. Moreover, by taking into account the corresponding auscultation area for each obtained TFR, we adopt exclusion reasoning to attribute each burst to its origins within the myocardium. Furthermore, the alternating functioning of heart valves and cavities in systole and diastole was characterized in the time and frequency domains. Aortic stenosis heart sounds were involved in our study in a view to confirm their pathological nature towards the normal heart sounds findings. Indeed, the weakened S1 and S2 heart sounds and the strong systolic ejection murmur which dominates the overall systole, confirm our hypotheses. Thus, modulations laws relating to the systolic ejection of blood through the stenosed orifice were characterized by means of the reliable SPWVD approach. A third heart sound (S3) which is an indicator of the presence of systolic dysfunction and the elevated filling pressure for aortic stenosis lesion was also characterized.

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Nemani, Lalita, and Ramya Pechetty. "Additional Heart Sounds–Part 2 (Clicks, Opening Snap and More)." Indian Journal of Cardiovascular Disease in Women - WINCARS 5, no. 04 (December 2020): 351–63. http://dx.doi.org/10.1055/s-0040-1722385.

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AbstractSystolic clicks are high-pitched sharp sounds. They are classified as ejection and nonejection clicks. Ejections clicks commonly occur at the aortic and pulmonary valve, while nonejection clicks occur at the mitral and tricuspid valve.Opening snap is an additional sound heard in the diastole. It is described as an early diastolic, high-pitched sound, which is associated with opening of the mitral and/or tricuspid valve.Pericardial knock is a high-pitched early diastolic sound, which is characteristic of constrictive pericarditis.The opening and closing of prosthetic valves produce sounds which may vary in intensity and timing according to the type and design of the valve, patient’s rhythm, and hemodynamic status.

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He, Yi, Wuyou Li, Wangqi Zhang, Sheng Zhang, Xitian Pi, and Hongying Liu. "Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning." Applied Sciences 11, no. 2 (January 11, 2021): 651. http://dx.doi.org/10.3390/app11020651.

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The heart sound signal is one of the signals that reflect the health of the heart. Research on the heart sound signal contributes to the early diagnosis and prevention of cardiovascular diseases. As a commonly used deep learning network, convolutional neural network (CNN) has been widely used in images. In this paper, the method of analyzing heart sound through using CNN has been studied. Firstly, the original data set was preprocessed, and then the heart sounds were segmented on U-net, based on the deep CNN. Finally, the classification of heart sounds was completed through CNN. The data from 2016 PhysioNet/CinC Challenge was utilized for algorithm validation, and the following results were obtained. When the heart sound segmented, the overall accuracy rate was 0.991, the accuracy of the first heart sound was 0.991, the accuracy of the systolic period was 0.996, the accuracy of the second heart sound was 0.996, and the accuracy of the diastolic period was 0.997, and the average accuracy rate was 0.995; While in classification, the accuracy was 0.964, the sensitivity was 0.781, and the specificity was 0.873. These results show that deep learning based on CNN shows good performance in the segmentation and classification of the heart sound signal.

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He, Yi, Wuyou Li, Wangqi Zhang, Sheng Zhang, Xitian Pi, and Hongying Liu. "Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning." Applied Sciences 11, no. 2 (January 11, 2021): 651. http://dx.doi.org/10.3390/app11020651.

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The heart sound signal is one of the signals that reflect the health of the heart. Research on the heart sound signal contributes to the early diagnosis and prevention of cardiovascular diseases. As a commonly used deep learning network, convolutional neural network (CNN) has been widely used in images. In this paper, the method of analyzing heart sound through using CNN has been studied. Firstly, the original data set was preprocessed, and then the heart sounds were segmented on U-net, based on the deep CNN. Finally, the classification of heart sounds was completed through CNN. The data from 2016 PhysioNet/CinC Challenge was utilized for algorithm validation, and the following results were obtained. When the heart sound segmented, the overall accuracy rate was 0.991, the accuracy of the first heart sound was 0.991, the accuracy of the systolic period was 0.996, the accuracy of the second heart sound was 0.996, and the accuracy of the diastolic period was 0.997, and the average accuracy rate was 0.995; While in classification, the accuracy was 0.964, the sensitivity was 0.781, and the specificity was 0.873. These results show that deep learning based on CNN shows good performance in the segmentation and classification of the heart sound signal.

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Cheng Xie-Feng and Li Wei. "Research on heart-sound graphical processing methods based on heart-sounds window function." Acta Physica Sinica 64, no. 5 (2015): 058703. http://dx.doi.org/10.7498/aps.64.058703.

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Abdollahpur, Mostafa, Ali Ghaffari, Shadi Ghiasi, and M. Javad Mollakazemi. "Detection of pathological heart sounds." Physiological Measurement 38, no. 8 (July 31, 2017): 1616–30. http://dx.doi.org/10.1088/1361-6579/aa7840.

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Chen, Lingguang, Sean F. Wu, Yong Xu, William D. Lyman, and Gaurav Kapur. "Blind separation of heart sounds." Journal of the Acoustical Society of America 137, no. 4 (April 2015): 2388. http://dx.doi.org/10.1121/1.4920693.

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Journal articles on the topic 'Heart Sounds'

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