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Relevant bibliographies by topics / Heart Sounds

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Academic literature on the topic 'Heart Sounds'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 February 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Heart Sounds"

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Leung, Terence Sze-tat. "Time-frequency characterisation of paediatric heart sounds." Thesis, University of Southampton, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287001.

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Andersson, Gustav. "Classification of Heart Sounds with Deep Learning." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149699.

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Health care is becoming more and more digitalized and examinations of patients from a distance are closer to reality than fiction. One of these examinations would be to automatically classify a patient-recorded audiosegment of its heartbeats as healthy or pathological. This thesis examines how it can be achieved by examining different kinds of neural networks; convolutional neural networks (CNN) and long short-term memory networks (LSTM). The theory of artificial neural networks is explained. With this foundation, the feed forward CNN and the recurrent LSTM-network have their methods described. Before these methods can be used, the required pre-processing has to be completed, which is different for the two types of networks. Using this theory, the process of how to implement the networks in Matlab is explained. Different CNN:s are compared to each other, then the best performing CNN is compared to the LSTM-network. When comparing the two different networks to each other, cross validation is used to achieve the most correct result possible. The networks are compared by accuracy, least amount of training time and least amount of training data. A final resulti s presented, to show which type of network has the best performance, together with a discussion to explain the results. The CNN performed better than the LSTM-network in all aspects. A reflection on what could have been done differently to achieve a better result is posted.

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Thiyagaraja, Shanti. "Detection and Classification of Heart Sounds Using a Heart-Mobile Interface." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc1159216/.

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An early detection of heart disease can save lives, caution individuals and also help to determine the type of treatment to be given to the patients. The first test of diagnosing a heart disease is through auscultation - listening to the heart sounds. The interpretation of heart sounds is subjective and requires a professional skill to identify the abnormalities in these sounds. A medical practitioner uses a stethoscope to perform an initial screening by listening for irregular sounds from the patient's chest. Later, echocardiography and electrocardiography tests are taken for further diagnosis. However, these tests are expensive and require specialized technicians to operate. A simple and economical way is vital for monitoring in homecare or rural hospitals and urban clinics. This dissertation is focused on developing a patient-centered device for initial screening of the heart sounds that is both low cost and can be used by the users on themselves, and later share the readings with the healthcare providers. An innovative mobile health service platform is created for analyzing and classifying heart sounds. Certain properties of heart sounds have to be evaluated to identify the irregularities such as the number of heart beats and gallops, intensity, frequency, and duration. Since heart sounds are generated in low frequencies, human ears tend to miss certain sounds as the high frequency sounds mask the lower ones. Therefore, this dissertation provides a solution to process the heart sounds using several signal processing techniques, identifies the features in the heart sounds and finally classifies them. This dissertation enables remote patient monitoring through the integration of advanced wireless communications and a customized low-cost stethoscope. It also permits remote management of patients' cardiac status while maximizing patient mobility. The smartphone application facilities recording, processing, visualizing, listening, and classifying heart sounds. The application also generates an electronic medical record, which is encrypted using the efficient elliptic curve cryptography and sent to the cloud, facilitating access to physicians for further analysis. Thus, this dissertation results in a patient-centered device that is essential for initial screening of the heart sounds, and could be shared for further diagnosis with the medical care practitioners.

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Corona, Blanca Tovar. "Analysis and representation of heart sounds and murmurs." Thesis, University of Sussex, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299958.

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Baranek, Humberto Leon. "Automatic detection and identification of cardiac sounds and murmurs." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63754.

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Marcus, Diveena Seshetta. "Sounds from the heart: Native American language and song." Thesis, Montana State University, 2011. http://etd.lib.montana.edu/etd/2011/marcus/MarcusD0511.pdf.

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Our world is witnessing the rapid extinction of indigenous cultures through colonization. This thesis is presented not to amplify decolonization but to honor the value and meaning of the oral society and its indigenous peoples through their culture's traditional and necessary components of language and song. The basis of this thesis pertains to the author's tribal relatives, the Coast Miwok original people of California known as Tamal Michchawmu which literally translates as the People of the West Coast. The author chooses to use this work as an advocacy for the worldview of indigenous peoples, particularly to matriarchal societies in which the Tamal Michchawmu are included. In this thesis, stories and interviews with scholars and with Native Americans studying their language and singing their songs as well as the author's personal experiences are included as support to the theory that language and song are formed from the foundation of a philosophy that is grounded within a peoples relationship with the land. My thesis question is: If this worldview is resurrected, how can it contribute to its indigenous people in a modern society?

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Tahmasbi, Mohammad Saeed. "VLSI implementation of heart sounds maximum entropy spectral estimation /." Title page, contents and summary only, 1994. http://web4.library.adelaide.edu.au/theses/09ENS/09enst128.pdf.

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Ewing, Gary John. "A new approacch to the analysis of the third heart sound." Title page, contents and summary only, 1988. http://web4.library.adelaide.edu.au/theses/09SM/09sme95.pdf.

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Feng, Shuo. "Designing for Stress Reduction by Connecting Heart Rate to Sounds." Thesis, KTH, Medieteknik och interaktionsdesign, MID, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191454.

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With the progress of society, an increasing number of people pay close attention to their health. In this study, we designed a Heart Rate based interactive sound to investigate how biodata-based interactive sounds can affect the well-being of people. We used a variety of approaches to analyzing the data and feedback from users. Firstly, the Trier Social Stress Test protocol was employed as a basis for the test. Also, a package of cultural probes such as photos, diaries and cards were used to collect data from users' everyday life. Recruited by using a snowball sampling technique, five users took and completed the test. From the analysis, we identified problems with our design and realized how to potentially improve the device in the future. The main conclusion that could be drawn was that Heart Rate-based interactive sounds can help users to reduce stress, but that most individuals were more willing to listen to steady sounds in order to relax.
I takt med att samhället utvecklas, utvecklas också ett hälsomedvetet tänkande bland samhällets individer. I denna studie designade vi ett ljud baserad på hjärtfrekvens för att undersöka hur ljud baserade på biodata kan påverka välmåendet hos individer. Vi använde oss av en mängd tillvägagångssätt för att analysera data och återkopplingen från användarna. Det så kallade ”Trier Social Stress Test” protokollet utgjorde testets grund. Därtill användes en samling kulturella stimulis som exempelvis foton, dagböcker och kort, vilka användes för att samla data från användarnas vardagliga liv. Fem användare rekryterades genom snöbollsmetoden, och genomförde sedan testet. Utifrån analysen fann vi problem med vår design och insåg hur vi eventuellt kunde förbättra apparaten i framtiden. Den huvudsakliga slutsatsen som kan dras var att ljud baserad på hjärtfrekvens kan hjälpa användare att minska stress, fastän de flesta individer hellre ville lyssna på mer konstanta ljud för att slappna av.

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Gretzinger, David Theodor Kerr. "Analysis of heart sounds and murmurs by digital signal manipulation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ45444.pdf.

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Books on the topic "Heart Sounds"

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Heart sounds. Aotearoa, N.Z: Steele Roberts, 2005.

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Johns, Michele. Heart sounds. New York: Harper Paperbacks, 1993.

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HarperPaperbacks and Copyright Paperback Collection (Library of Congress), eds. Heart sounds. New York: HarperPaperbacks, 1993.

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service), SpringerLink (Online, ed. Pediatric Heart Sounds. London: Springer-Verlag London, 2008.

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McConnell, Michael E., and Alan Branigan. Pediatric Heart Sounds. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-684-1.

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E, McConnell Michael, and Concept Media inc, eds. Pediatric heart sounds. Irvine, CA: Concept Media, 2002.

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M, Brown E., ed. Heart sounds made easy. Edinburgh: Churchill Livingstone, 2002.

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1962-, Brown E. M., ed. Heart sounds made easy. 2nd ed. Edinburgh: Churchill Livingstone, 2008.

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1962-, Brown E. M., ed. Heart sounds made easy. 2nd ed. Edinburgh: Churchill Livingstone, 2008.

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Understanding pediatric heart sounds. 2nd ed. Philadelphia: Saunders, 2003.

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Book chapters on the topic "Heart Sounds"

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Ranganathan, Narasimhan, Vahe Sivaciyan, and Franklin B. Saksena. "Heart Sounds." In Contemporary Cardiology, 141–210. Totowa, NJ: Humana Press, 2006. http://dx.doi.org/10.1007/978-1-59745-023-2_6.

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McConnell, Michael E., and Alan Branigan. "Normal Heart Sounds." In Pediatric Heart Sounds, 1–11. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-684-1_1.

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McConnell, Michael E., and Alan Branigan. "Innocent Heart Murmurs." In Pediatric Heart Sounds, 13–25. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-684-1_2.

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McConnell, Michael E., and Alan Branigan. "Atrial Septal Defects." In Pediatric Heart Sounds, 27–37. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-684-1_3.

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McConnell, Michael E., and Alan Branigan. "Ventricular Septal Defects." In Pediatric Heart Sounds, 39–55. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-684-1_4.

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McConnell, Michael E., and Alan Branigan. "Patent Arterial Duct." In Pediatric Heart Sounds, 57–63. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-684-1_5.

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McConnell, Michael E., and Alan Branigan. "Aortic Stenosis." In Pediatric Heart Sounds, 65–72. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-684-1_6.

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McConnell, Michael E., and Alan Branigan. "Pulmonary Stenosis." In Pediatric Heart Sounds, 73–84. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-684-1_7.

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McConnell, Michael E., and Alan Branigan. "Mitral Valve Insufficiency." In Pediatric Heart Sounds, 85–93. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-684-1_8.

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McConnell, Michael E., and Alan Branigan. "Tetralogy of Fallot." In Pediatric Heart Sounds, 95–104. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-684-1_9.

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Conference papers on the topic "Heart Sounds"

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Chakir, Fatima, Abdelilah Jilbab, Chafik Nacir, and Ahmed Hammouch. "Phonocardiogram signals classification into normal heart sounds and heart murmur sounds." In 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA). IEEE, 2016. http://dx.doi.org/10.1109/sita.2016.7772311.

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Charleston-Villalobos, S., L. F. Dominguez-Robert, R. Gonzalez-Camarena, and A. T. Aljama-Corrales. "Heart Sounds Interference Cancellation in Lung Sounds." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.259357.

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Charleston-Villalobos, S., L. F. Dominguez-Robert, R. Gonzalez-Camarena, and A. T. Aljama-Corrales. "Heart Sounds Interference Cancellation in Lung Sounds." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.4397747.

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Abubakar, Mohammed Mansur, and Taner Tuncer. "Heart Sounds Classification Using Hybrid CNN Architecture." In International Students Science Congress. Izmir International Guest Student Association, 2021. http://dx.doi.org/10.52460/issc.2021.023.

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In this paper, we propose a hybrid model for diagnosing heart conditions by analyzing heart sounds and signals. The Hybrid CNN (Convolutional Neural Network) model is trained to classify distinguishable pathological heart sounds into three classes; normal, murmur, and extrasystole. Scalogram images of heart sounds were obtained by applying wavelet transform to heart sound signals. Images are inputs for Resnet50 and Resnet101 CNN models. The feature vectors of these architectures in the fc1000 layer are combined. Relief feature selection algorithm was applied to the obtained feature vector, and then the classification was performed with the support vector machine algorithm. Training the proposed model resulted in accuracy of 92.75%, thus, making it the best performing model in comparison to other models in this paper.

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Cheng, Xie-feng, Ye-wei Tao, Yong-hua Ma, Zhong Zhang, and Reng-yi Yan. "Heart Sounds in Identity Recognition." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5514809.

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Yazdani, Sasan, Silas Schlatter, Seyyed Abbas Atyabi, and Jean-Marc Vesin. "Identification of Abnormal Heart Sounds." In 2016 Computing in Cardiology Conference. Computing in Cardiology, 2016. http://dx.doi.org/10.22489/cinc.2016.330-341.

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Pourazad, M. T., Z. Moussavi, F. Farahmand, and R. K. Ward. "Heart Sounds Separation From Lung Sounds Using Independent Component Analysis." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1617037.

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Yildiz, Metin, and Zeynep Turkoglu. "Feasibility of heart rate variability analysis with heart sounds." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830387.

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Syeda-Mahmood, Tanveer, and Fei Wang. "Shape-based matching of heart sounds." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761691.

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Mayorga, P., C. Druzgalski, D. Calderas, and V. Zeljkovic. "Multimodal classification of heart sounds attributes." In 2014 Pan American Health Care Exchanges (PAHCE). IEEE, 2014. http://dx.doi.org/10.1109/pahce.2014.6849615.

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Reports on the topic "Heart Sounds"

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Axelrod, M. C., G. A. Clark, and D. Scott. Classification of heart valve sounds from experiments in an anechoic water tank. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/10788.

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Axelrod, M. C., G. A. Clark, and D. Scott. Classification of heart valve sounds from experiments in an anechoic water tank. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/10789.

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Heremans, Joseph. Magnetic Fields Can Control Heat and Sound. Fort Belvoir, VA: Defense Technical Information Center, March 2015. http://dx.doi.org/10.21236/ada614068.

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Murrill, Steven R., and Michael V. Scanlon. Design of a Heart Sound Extraction Algorithm for an Acoustic-Based Health Monitoring System. Fort Belvoir, VA: Defense Technical Information Center, October 2002. http://dx.doi.org/10.21236/ada409127.

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BONNEAU, François, Guillaume CAUMON, Judith SAUSSE, and Philippe RENARD. Genetic-like modeling of fracture network honoring connectivity data in the geothermal heat exchanger at Soultz-sous-Forêt (France). Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0094.

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