Relevant bibliographies by topics / Diagnostics of heart sounds

Academic literature on the topic 'Diagnostics of heart sounds'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Diagnostics of heart sounds"

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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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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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Adiban, Mohammad, Bagher BabaAli, and Saeedreza Shehnepoor. "Statistical feature embedding for heart sound classification." Journal of Electrical Engineering 70, no. 4 (August 1, 2019): 259–72. http://dx.doi.org/10.2478/jee-2019-0056.

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Abstract Cardiovascular Disease (CVD) is considered as one of the principal causes of death in the world. Over recent years, this field of study has attracted researchers’ attention to investigate heart sounds’ patterns for disease diagnostics. In this study, an approach is proposed for normal/abnormal heart sound classification on the Physionet challenge 2016 dataset. For the first time, a fixed length feature vector; called i-vector; is extracted from each heart sound using Mel Frequency Cepstral Coefficient (MFCC) features. Afterwards, Principal Component Analysis (PCA) transform and Variational Autoencoder (VAE) are applied on the i-vector to achieve dimension reduction. Eventually, the reduced size vector is fed to Gaussian Mixture Models (GMMs) and Support Vector Machine (SVM) for classification purpose. Experimental results demonstrate the proposed method could achieve a performance improvement of 16% based on Modified Accuracy (MAcc) compared with the baseline system on the Physionet2016 dataset.
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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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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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Boulares, Mehrez, Reem Alotaibi, Amal AlMansour, and Ahmed Barnawi. "Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process." International Journal of Environmental Research and Public Health 18, no. 20 (October 18, 2021): 10952. http://dx.doi.org/10.3390/ijerph182010952.

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Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.
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Omari, Tahar, and Fethi Bereksi-Reguig. "An automatic wavelet denoising scheme for heart sounds." International Journal of Wavelets, Multiresolution and Information Processing 13, no. 03 (May 2015): 1550016. http://dx.doi.org/10.1142/s0219691315500162.

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Phonocardiograms (PCGs), recording of heart sounds, have many advantages over traditional auscultation in that they may be replayed and analyzed for spectral and frequency information. PCG is not a widely used diagnostic tool as it could be. One of the major problems with PCG is noise corruption. Many sources of noise may pollute a PCG signal including lung and breath sounds, environmental noise and blood flow noises which are known as murmurs. These murmurs contain many information on heart hemodynamic which can be used particularly in detecting of heart valve diseases. An automated system for heart murmurs processing can be an important tool in diagnostic of heart diseases using a simple electronic stethoscope. However, the first step before developing any automated system is the segmentation of the PCG signals from which the murmurs can be separated. A robust segmentation algorithm must have a robust denoising technique, where, wavelet transform (WT) is among the ones which exhibits very high satisfactory results in such situations. However, the selection of level of decomposition and the mother wavelet are the major challenges. This paper proposes a novel approach for an automatic selection of mother wavelet and level of decomposition that can be used in heart sounds denoising. The obtained results on both simulative and real PCG signals showed that the proposed approach can successfully select the best level of decomposition with the best mother wavelet that can be used in extraction operation of main PCG sound components (S1 and S2) from various types of murmurs.
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Bourouhou, Abdelhamid, Abdelilah Jilbab, Chafik Nacir, and Ahmed Hammouch. "Heart Sounds Classification for a Medical Diagnostic Assistance." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 11 (July 16, 2019): 88. http://dx.doi.org/10.3991/ijoe.v15i11.10804.

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<span lang="EN-US">In order to develop the assessment of phonocardiogram “PCG” signal for discrimination between two of people classes – individuals with heart disease and healthy one- we have adopted the database provided by "The PhysioNet/Computing in Cardilogy Challenge 2016", which contains records of heart sounds 'PCG '. This database is chosen in order to compare and validate our results with those already published. We subsequently extracted 20 features from each provided record. For classification, we used the Generalized Linear Model (GLM), and the Support Vector Machines (SVMs) with its different types of kernels (i.e.; Linear, polynomial and MLP). The best classification accuracy obtained was 88.25%, using the SVM classifier with an MLP kernel.</span>
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Larsen, Bjarke Skogstad, Simon Winther, Louise Nissen, Axel Diederichsen, Morten Bøttcher, Johannes Jan Struijk, Mads Græsbøll Christensen, and Samuel Emil Schmidt. "Spectral analysis of heart sounds associated with coronary artery disease." Physiological Measurement 42, no. 10 (October 1, 2021): 105013. http://dx.doi.org/10.1088/1361-6579/ac2fb7.

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Abstract Objective. The aim of this study was to find spectral differences of diagnostic interest in heart sound recordings of patients with coronary artery disease (CAD) and healthy subjects. Approach. Heart sound recordings from three studies were pooled, and patients with clear diagnostic outcomes (positive: CAD and negative: Non-CAD) were selected for further analysis. Recordings from 1146 patients (191 CAD and 955 Non-CAD) were analyzed for spectral differences between the two groups using Welch’s spectral density estimate. Frequency spectra were estimated for systole and diastole segments, and time-frequency spectra were estimated for first (S1) and second (S2) heart sound segments. An ANCOVA model with terms for diagnosis, age, gender, and body mass index was used to evaluate statistical significance of the diagnosis term for each time-frequency component. Main results. Diastole and systole segments of CAD patients showed increased energy at frequencies 20–120 Hz; furthermore, this difference was statistically significant for the diastole. CAD patients showed decreased energy for the mid-S1 and mid-S2 segments and conversely increased energy before and after the valve sounds. Both S1 and S2 segments showed regions of statistically significant difference in the time-frequency spectra. Significance. Results from analysis of the diastole support findings of increased low-frequency energy from previous studies. Time-frequency components of S1 and S2 sounds showed that these two segments likely contain heretofore untapped information for risk assessment of CAD using phonocardiography; this should be considered in future works. Further development of features that build on these findings could lead to improved acoustic detection of CAD.
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Ms. Mohini Dadhe, Ms. Sneha Miskin. "Optimized Wireless Stethoscope Using Butterworth Filter." International Journal of New Practices in Management and Engineering 4, no. 03 (September 30, 2015): 01–05. http://dx.doi.org/10.17762/ijnpme.v4i03.37.

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Acoustic stethoscope is a primary diagnostic tool used by doctors for effective analysis of various diseases. But, it suffers defects such as low level heart sounds and presence of murmurs. This project aims at developing ‘Noise removal in stethoscope using Butterworth filter’. In this, the low level heart sounds were amplified with high gain pre-amplifier circuit. A review of basic filters like low pass RC filter, Butterworth filter, Chebyshev filter and Bessel filter are done for elimination of murmurs and best suited filter is suggested. Finally, amplified noise free heart sounds are simulated using Proteus 7.4. The results prove that efficient filter for designing is ‘Butterworth filter’.
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Dissertations / Theses on the topic "Diagnostics of heart sounds"

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Жемчужкіна, Т. В., and Т. В. Носова. "Сonstruction of phase portraits of PCG signals." Thesis, НТУ «ХПІ», 2021. https://openarchive.nure.ua/handle/document/17554.

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For PCG signals we constructed phase portraits (PP) in 2-dimensional state space. To demonstrate PP of heart sounds and murmurs we extracted main sounds from PCG signals. Different signals have different forms of PP and samples of main sounds can be separated from rest samples using PP. Showed applications of PP analysis. So, PP of PCG signals probably can be used for diagnostics of heart sounds and for segmentation of PCG signal without simultaneous ECG-recording.
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Romero-Vivas, Eduardo. "Hidden Markovian models applied to the analysis of heart sounds for diagnostic purposes." Thesis, University of Southampton, 2006. https://eprints.soton.ac.uk/425886/.

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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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Books on the topic "Diagnostics of heart sounds"

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Erickson, Barbara. Heart sounds and murmurs: A practical guide. St. Louis: Mosby, 1987.

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Erickson, Barbara. Heart sounds and murmurs: A practical guide. 3rd ed. St. Louis: Mosby, 1997.

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G, Szilagyi Peter, ed. Bates' guide to physical examination and history taking. 8th ed. Philadelphia: Lippincott Williams & Wilkins, 2003.

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Bickley, Lynn S. Bates' guide to physical examination and history taking. 7th ed. Philadelphia: Lippincott, 1999.

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G, Szilagyi Peter, and Bates Barbara 1928-, eds. Bates' Guide to Physical Examination and History Taking. Philadelphia: Lippincott-Raven Publishers, 2009.

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G, Szilagyi Peter, and Bates Barbara 1928-, eds. Bates' guide to physical examination and history taking. 9th ed. Philadelphia: Lippincott Williams & Wilkins, 2007.

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G, Szilagyi Peter, and Bates Barbara 1928-, eds. Bates' guide to physical examination and history taking: Lynn S. Bickley, Peter G. Szilagyi. 9th ed. Philadelphia, PA: Lippincott Williams & Wilkins, 2007.

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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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Book chapters on the topic "Diagnostics of 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 "Diagnostics of heart sounds"

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Baala Sundaram, Divaakar Siva, Anoushka Kapoor, Jackie Xie, Natalie C. Xu, Prissha Krishna Moorthy, Rogith Balasubramani, Suganti Shivaram, Anjani Muthyala, and Shivaram Poigai Arunachalam. "Comparison of Multiscale Frequency Characteristics of Normal Phonocardiogram With Diseased Heart States." In 2020 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dmd2020-9090.

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Abstract Phonocardiogram (PCG) signals are electrical recording of heart sounds containing vital information of diagnostic importance. Several signal processing methods exist to characterize PCG, however suffers in terms of sensitivity and specificity in accurately discriminating normal and abnormal heart sounds. Recently, a multiscale frequency (MSF) analysis of normal PCG was reported to characterize subtle frequency content changes in PCG which can aid in differentiating normal and abnormal heart sounds. In this work, it was hypothesized that MSF can discriminate normal PCG signal compared to an artifact, PCG with extra systolic heart sounds and murmur based on their varying frequency content. Various samples of PCG with normal and abnormal heart sounds were obtained from Peter Bentley Heart Sounds Database sampled at 44.1 kHz for analysis. The signal was filtered using a 4th order Butterworth lowpass filter with cutoff frequency at 200 Hz to remove higher frequency noise and MSF estimation was performed on the filtered dataset using custom MATLAB software. Mann-Whitney test was performed for statistical significance at p &lt; 0.05. Results indicate that MSF successfully discriminated normal and abnormal heart sounds, which can aid in PCG classification with more sophisticated analysis. Validation of this technique with larger dataset is required.
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Siva Baala Sundaram, Divaakar, Anjani Muthyala, Rogith Balasubramani, Suganti Shivaram, Susan Karki, and Shivaram Poigai Arunachalam. "Profiling Multiscale Frequency State of Normal Phonocardiogram: Feasibility Study." In 2019 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dmd2019-3301.

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Phonocardiogram (PCG) signals contain very important information regarding the heart condition. Recently, several automatic detection algorithms have been explored to profile the characteristics of heart sounds to aid in disease diagnosis. However, many of these methods has been demonstrated only on clean signals with limited test data and variety of PCG signals that can accurately provide information of diagnostic importance with higher sensitivity and specificity. In this work, we propose to characterize the multiscale frequency state of the normal PCG signals that can aid in accurate profiling of PCG to discriminate from pathological conditions.
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Cooper, Daniel, Pavlos Vlachos, and Michael Roan. "2D Analysis of Acoustic Transfer Through the Chest Cavity of Sounds Associated With Coronary Artery Disease." In ASME 2009 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2009. http://dx.doi.org/10.1115/sbc2009-206698.

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Cardiovascular disease represents the leading cause of death in the United States, accounting for about 35% of all deaths, the majority of which are due to Coronary Artery Disease (CAD), which alone accounts for 20% of all deaths [1]. This is primarily due to the difficulty in early diagnosis of CAD; only about 20% of all cases are diagnosed prior to the patient’s first heart attack [2]. This is due to the limitations of current diagnostic techniques, which are either invasive or prohibitively expensive for use as a screening test for CAD. Phonoangiography is a technique that promises inexpensive and noninvasive diagnosis of CAD by detecting the flow noise created by turbulent flow within a stenosed coronary artery, also known as a bruit in the medical community. This technique is not currently used due to the difficulties of detecting the faint sound of a bruit in the noisy environment of the chest. To the authors’ knowledge, analysis of the acoustic transfer of cardiovascular sounds through the chest has not studied previously. We hypothesize that the acoustic transfer function at different locations on the chest surface is significantly dependent on the source location, which would provide a signal processing technique for detecting the presence of CAD.
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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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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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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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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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Reports on the topic "Diagnostics of 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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