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Automatic extraction of physiological features from vibro-acoustic heart signals: correlation with echo-doppler

G. Amit, N. Gavriely, J. Lessick, N. Intrator
2005 Computers in Cardiology, 2005  
Processing algorithms were developed to extract temporal and morphological feature from the signals. Spectral analysis was used to reconstruct the Doppler sonograms and estimate reference values.  ...  These vibro-acoustic signals carry valuable physiological information that can be potentially used for cardiac monitoring.  ...  The two major audible sounds in a normal cardiac cycle are the first and second heart sounds, S1 and S2.  ... 
doi:10.1109/cic.2005.1588096 fatcat:6xx6iuxfgrgfhblpjeyl4tgamy

Cluster analysis and classification of heart sounds

Guy Amit, Noam Gavriely, Nathan Intrator
2009 Biomedical Signal Processing and Control  
Heart sound signals (S1) were first identified and extracted from the acquired data, and then transformed to a raw feature space in the time-frequency plane.  ...  S1 signals were extracted from each cardiac cycle and aggregated for further processing.  ... 
doi:10.1016/j.bspc.2008.07.003 fatcat:pihxuempofgs5kgesgwo4z4lxu

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

Amitai Bickel, Arieh Eitan, Dimitry Melnik, Atalia Weiss, Noam Gavrieli, David Kniaz, Nathan Intrator
2012 Journal of Laparoendoscopic and Advanced Surgical Techniques  
Our aim was to quantitatively correlate cardiac functionality (as expressed by cardiac output) with the spectral energy of the first heart sound (S1) obtained from the phonocardiogram, during laparoscopic  ...  Cardiac output was maximally changed during anesthesia and abdominal insufflation and was obtained from the arterial pressure wave (FloTracÔ sensor and VigileoÔ monitor [Edwards Lifesciences Ltd.]).  ...  This novel signal processing and feature extraction method essentially followed and enabled us to delineate a linear relationship between the acoustic amplitude (spectral energy) of S1 and cardiac functionality  ... 
doi:10.1089/lap.2011.0360 pmid:22416808 fatcat:hibrpa6gsrga5dcw2snpldpmki

Robust Heart Sound Segmentation and Detection using biased Cramer-Rao Lower Bound Estimation and Variance Algorithm

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
A proposed variance algorithm (VA) is developed to identify the boundary locations of heart sounds and segment the featured signals into series of cardiac cycles.  ...  A brief analysis of the results showed that the proposed MVA scheme using biased CRLB exhibits 97.4±1.2% accuracy in identifying S1 and S2 heart sounds.  ...  and extracting features from PCG signals.  ... 
doi:10.35940/ijitee.b1004.1292s319 fatcat:v6a67iagnjg7rfuj2gmuiqzm7u

Mobile Phonocardiogram Diagnosis in Newborns Using Support Vector Machine

Amir Amiri, Mohammadreza Abtahi, Nick Constant, Kunal Mankodiya
2017 Healthcare  
Monitoring cardiac stress using features extracted from s1 heart sounds. IEEE Trans. Biomed. Eng. 2015, 62, 1169–1178. 8. Barma, S.; Chou, C.-H.; Kuan, T.-W.; Lin, P.C.; Wang, J.-F.  ...  Herzig [7] presented a type of cardiac monitoring based on heart sound analysis.  ... 
doi:10.3390/healthcare5010016 pmid:28335471 pmcid:PMC5371922 fatcat:rrhxomh4lrclxfdqt5cez323si

Classifying Heart Sounds Using Images of MFCC and Temporal Features [chapter]

Diogo Marcelo Nogueira, Carlos Abreu Ferreira, Alípio M. Jorge
2017 Lecture Notes in Computer Science  
Phonocardiogram signals contain very useful information about the condition of the heart. It is a method of registration of heart sounds, which can be visually represented on a chart.  ...  After that, we extract a group of features from the time and frequency domain (Mel-frequency cepstral coefficients) of the phonocardiogram.  ...  Acknowledgments This work is supported by the NanoSTIMA Project: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016 which is financed  ... 
doi:10.1007/978-3-319-65340-2_16 fatcat:mzmujzxzvvb4xlkdvpfebjfgx4

Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions

Jadyn Cook, Muneebah Umar, Fardin Khalili, Amirtahà Taebi
2022 Bioengineering  
In these methods, the acoustic signals are usually recorded by acoustic sensors, such as microphones and accelerometers, and are analyzed using various signal processing, machine learning, and computational  ...  In the past few decades, many non-invasive monitoring methods have been developed based on body acoustics to investigate a wide range of medical conditions, including cardiovascular diseases, respiratory  ...  Acknowledgments: Figure 1 was created using the art vectors available at vecteezy.com, accessed on 27 March 2022.  ... 
doi:10.3390/bioengineering9040149 pmid:35447708 pmcid:PMC9032059 fatcat:b6xrfal4trgaxjsu75pt32uevq

Non Invasive Foetal Monitoring with a Combined ECG - PCG System [chapter]

Mariano Ruffo, Mario Cesarelli, Craig Jin, Gaetano Gargiulo, Alistair McEwan, Colin Sullivan, Paolo Bifulco, Maria Romano, Richard W., Andr van
2011 Biomedical Engineering, Trends in Electronics, Communications and Software  
Biomedical Engineering Trends in Electronics, Communications and Software 348 and second heart sounds and QRS waves, which provide reliable measures of heart rate, and offer the potential of new information  ...  It is important to monitor the ST interval since it reflects the function of the foetal heart muscle during stress tests.  ...  of the valves (mitral and tricuspid), which produces the first heart sound (S1).  ... 
doi:10.5772/13554 fatcat:pm4dfmpavnczloaxe7os3dl6ka

A Six-Step Framework on Biomedical Signal Analysis for Tackling Noncommunicable Diseases: Current and Future Perspectives

Mohamed Elgendi, Newton Howard, Nigel Lovell, Andrzej Cichocki, Matt Brearley, Derek Abbott, Ian Adatia
2016 JMIR Biomedical Engineering  
Information extracted from biomedical signals might increase diagnostic precision while augmenting the robustness of health care workers' clinical decision making.  ...  Recently, interest has increased in using mobile health (mHealth) as a potential solution to overcome barriers to improving health care in LMICs.  ...  However, there have been few attempts to extract features from the heart sound in PAH subjects [19] [20] [21] [22] .  ... 
doi:10.2196/biomedeng.6401 fatcat:ktpzfax3zbhw3cgkoh5he5x7ha

Artificial intelligence and automation in valvular heart diseases

Qiang Long, Xiaofeng Ye, Qiang Zhao
2020 Cardiology Journal  
Thirdly, it introduces using AI algorithms to identify risk factors and predict mortality of cardiac surgery.  ...  Secondly, it discusses the application of AI in heart sound auscultation and medical image analysis for assistance in diagnosing VHDs.  ...  Feature extraction and feature selection This step aims to select and extract discriminative features either for more precise segmentation of heart sound or for following the disease classification step  ... 
doi:10.5603/cj.a2020.0087 pmid:32567669 pmcid:PMC8016001 fatcat:yzdeast6q5eurjwe7vsirs6rw4

Preliminary study in the analysis of the severity of cardiac pathologies using the higher-order spectra on the heart-beats signals

Sid Ahmed Berraih, Yettou Nour Elhouda Baakek, Sidi Mohammed El Amine Debbal
2021 Polish Journal of Medical Physics And Engineering  
The PCG signals are acoustic waves revealing a wealth of clinical information about cardiac health. They enable doctors to better understand heart sounds when presented visually.  ...  Hence, multiple approaches have been proposed to analyze heart sounds based on PCG recordings.  ...  The first heart sound (S1) is composed of several high components.  ... 
doi:10.2478/pjmpe-2021-0010 fatcat:3iwygs42pfcjtmf5mylkt2fslq

IoMT-based biomedical measurement systems for healthcare monitoring: a review

Imran Ahmed, Eulalia Balestrieri, Francesco Lamonaca
2021 ACTA IMEKO  
The presented IoMT-based BMS are applied to healthcare applications concerning, in particular, heart, brain and blood sugar diseases as well as internal body sound and blood pressure measurements.  ...  <p class="Abstract"><span lang="EN-US">Biomedical measurement systems (BMS) have provided new solutions for healthcare monitoring and the diagnosis of various chronic diseases.  ...  The system also features a Hilbert-Huang transform to reduce interference signals and help extract features of the first heart sound, S1, and the second heart sound, S2.  ... 
doi:10.21014/acta_imeko.v10i2.1080 fatcat:d6uok7qqlfeshn2yds3l6r2p3y

Analysis of Cardiac Vibration Signals Acquired From a Novel Implant Placed on the Gastric Fundus

Henry Areiza-Laverde, Cindy Dopierala, Lotfi Senhadji, Francois Boucher, Pierre Y. Gumery, Alfredo Hernández
2021 Frontiers in Physiology  
analysis of the cardiac cycles and computation of coherent mean from aligned ECG and ACC, (4) cardiac vibration components segmentation (S1 and S2) from the coherent mean ACC data, and (5) estimation  ...  The analysis of cardiac vibration signals has been shown as an interesting tool for the follow-up of chronic pathologies involving the cardiovascular system, such as heart failure (HF).  ...  Haemodynamic monitoring of cardiac status using heart sounds from an implanted cardiac device.  ... 
doi:10.3389/fphys.2021.748367 pmid:34867453 pmcid:PMC8640497 fatcat:ypqy37tp4ncrrm6yk4qcef76am

A Visual Domain Transfer Learning Approach for Heartbeat Sound Classification [article]

Uddipan Mukherjee, Sidharth Pancholi
2021 arXiv   pre-print
This research proposes to convert cleansed and normalized heart sound into visual mel scale spectrograms and then using visual domain transfer learning approaches to automatically extract features and  ...  These well-accepted models in the image domain showed to learn generalized feature representations of cardiac sounds collected from different environments with varying amplitude and noise levels.  ...  Literature review found out that none of the studies has investigated automated feature extraction using visual methods of cardiac sounds yet.  ... 
arXiv:2107.13237v2 fatcat:w5u2xass6bhgjohnbrxebkedam

Synthesis of Normal Heart Sounds Using Generative Adversarial Networks and Empirical Wavelet Transform

Pedro Narváez, Winston S. Percybrooks
2020 Applied Sciences  
A distortion metric (mel–cepstral distortion) was used to objectively assess the quality of synthetic heart sounds.  ...  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  ...  Currently, there are sophisticated equipment and tests for diagnosing heart disease, such as: electrocardiogram, holter monitoring, echocardiogram, stress test, cardiac catheterization, computed tomography  ... 
doi:10.3390/app10197003 fatcat:l2td6op2c5advkiipa62tfu3oy
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