scholarly journals Portable heart valve disease screening device using electronic stethoscope

2019 ◽  
Vol 15 (1) ◽  
pp. 122
Author(s):  
Mohd Zubir Suboh ◽  
Muhyi Yaakop ◽  
Mohd Azlan Abu ◽  
Mohd Syazwan Md Yid ◽  
Aizat Faiz Ramli ◽  
...  
Keyword(s):  
Heart Valve ◽  
Heart Sound ◽  
Disease Screening ◽  
Sound Signal ◽  
Valve Disease ◽  
Automated System ◽  
Heart Valve Disease ◽  
Electronic Stethoscope ◽  
Screening Device ◽  
Heart Sound Signal

<p><em>Heart sound analysis has been a popular topic of studies since a few decades ago. Most of the studies are done in PC platform since embedding the complex algorithm into a simple small device such as microcontroller board seems to be very difficult due to limited processing speed and memory. This study classifies normal and abnormal heart sound signal from four categories of Heart Valve Disease. An automated system that consists of segmentation, feature extraction and classification of the heart sound signal is developed in PC and hardware platforms. A multimedia board completed with a single board computer, audio codec and graphic LCD is used to make a portable heart valve disease screening device with electronic stethoscope as the input for the system. Both system recorded 96.3% specificity. However, the portable device has only 77.78% sensitivity and 87.04% accuracy compared to PC platform that have sensitivity and accuracy of more than 90%.</em></p>

Heart valve disease screening system — PC based

2017 ◽  
Author(s):  
Mohd Zubir Suboh ◽  
Muhyi Yaakop ◽  
Mohd Syazwan Md Yid ◽  
Mohd Azlan Abu ◽  
Imran Mohammad Sofi
Keyword(s):  
Heart Valve ◽  
Disease Screening ◽  
Valve Disease ◽  
Heart Valve Disease ◽  
Screening System

scholarly journals Classification of Heart Sound Signal Using Multiple Features

2018 ◽  
Vol 8 (12) ◽  
pp. 2344 ◽  
Author(s):  
Yaseen ◽  
Gui-Young Son ◽  
Soonil Kwon
Keyword(s):  
Heart Sound ◽  
Sound Signal ◽  
Support Vector ◽  
Multiple Features ◽  
Electronic Stethoscope ◽  
Heart Disorders ◽  
Discrete Wavelets ◽  
Mel Frequency Cepstral Coefficient ◽  
Heart Sound Signal

Cardiac disorders are critical and must be diagnosed in the early stage using routine auscultation examination with high precision. Cardiac auscultation is a technique to analyze and listen to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides the digital recording of the heart sound called phonocardiogram (PCG). This PCG signal carries useful information about the functionality and status of the heart and hence several signal processing and machine learning technique can be applied to study and diagnose heart disorders. Based on PCG signal, the heart sound signal can be classified to two main categories i.e., normal and abnormal categories. We have created database of 5 categories of heart sound signal (PCG signals) from various sources which contains one normal and 4 are abnormal categories. This study proposes an improved, automatic classification algorithm for cardiac disorder by heart sound signal. We extract features from phonocardiogram signal and then process those features using machine learning techniques for classification. In features extraction, we have used Mel Frequency Cepstral Coefficient (MFCCs) and Discrete Wavelets Transform (DWT) features from the heart sound signal, and for learning and classification we have used support vector machine (SVM), deep neural network (DNN) and centroid displacement based k nearest neighbor. To improve the results and classification accuracy, we have combined MFCCs and DWT features for training and classification using SVM and DWT. From our experiments it has been clear that results can be greatly improved when Mel Frequency Cepstral Coefficient and Discrete Wavelets Transform features are fused together and used for classification via support vector machine, deep neural network and k-neareast neighbor(KNN). The methodology discussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy. The code and dataset can be accessed at “https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-/blob/master/README.md”.

scholarly journals Automated Heart Sound Signal Segmentation and Identification using Abrupt Changes and Peak Finding Detection

2021 ◽  
Vol 179 ◽  
pp. 260-267
Author(s):  
Norezmi Jamal ◽  
Nabilah Ibrahim ◽  
MNAH Sha’abani ◽  
Farhanahani Mahmud ◽  
N. Fuad
Keyword(s):  
Heart Sound ◽  
Sound Signal ◽  
Signal Segmentation ◽  
Peak Finding ◽  
Abrupt Changes ◽  
Heart Sound Signal

Detection and localization of S1 and S2 heart sounds by 3rd order normalized average Shannon energy envelope algorithm

2021 ◽  
pp. 095441192199810
Author(s):  
Madhwendra Nath ◽  
Subodh Srivastava ◽  
Niharika Kulshrestha ◽  
Dilbag Singh
Keyword(s):  
Success Rate ◽  
Heart Sound ◽  
Heart Diseases ◽  
Heart Sounds ◽  
Sound Signal ◽  
Ecg Signal ◽  
Shannon Energy ◽  
Detection And Localization ◽  
Pcg Signals ◽  
Heart Sound Signal

Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15–30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

Evaluation of Patients with Heart Valve Disease

2019 ◽  
pp. 9-19
Author(s):  
Jose Zamorano ◽  
Ciro Santoro ◽  
Álvaro Marco del Castillo
Keyword(s):  
Heart Valve ◽  
Valve Disease ◽  
Heart Valve Disease

scholarly journals Inflammatory Regulation of Valvular Remodeling: The Good(?), the Bad, and the Ugly

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Gretchen J. Mahler ◽  
Jonathan T. Butcher
Keyword(s):  
Heart Valve ◽  
Cell Biology ◽  
Heart Valves ◽  
Valve Disease ◽  
Heart Valve Disease ◽  
Inflammatory Signaling ◽  
Current State ◽  
Valve Remodeling ◽  
Inflammatory Regulation ◽  
Key Questions

Heart valve disease is unique in that it affects both the very young and very old, and does not discriminate by financial affluence, social stratus, or global location. Research over the past decade has transformed our understanding of heart valve cell biology, yet still more remains unclear regarding how these cells respond and adapt to their local microenvironment. Recent studies have identified inflammatory signaling at nearly every point in the life cycle of heart valves, yet its role at each stage is unclear. While the vast majority of evidence points to inflammation as mediating pathological valve remodeling and eventual destruction, some studies suggest inflammation may provide key signals guiding transient adaptive remodeling. Though the mechanisms are far from clear, inflammatory signaling may be a previously unrecognized ally in the quest for controlled rapid tissue remodeling, a key requirement for regenerative medicine approaches for heart valve disease. This paper summarizes the current state of knowledge regarding inflammatory mediation of heart valve remodeling and suggests key questions moving forward.

scholarly journals Perioperative Management of Sectio Caesarea Surgery in Patient With Heart Valve Disorders

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Purwoko Purwoko ◽  
Zidni Afrokhul Athir
Keyword(s):  
Mitral Regurgitation ◽  
Cesarean Section ◽  
Maternal Mortality ◽  
Pulmonary Edema ◽  
Heart Valve ◽  
Mitral Stenosis ◽  
Valve Disease ◽  
Heart Valve Disease ◽  
Normal Limits ◽  
In Pregnancy

<div class="WordSection1"><p>Cardiovascular disease in pregnancy is common range from 1% to 3 and contributes to 10-15% of maternal mortality. Valvular heart disease accounts for about 25% of cases of cardiac complications in pregnancy and important cause of maternal mortality, some of which are mitral stenosis and mitral regurgitation. Cesarean delivery remains the preferred choice, as it reduces the hemodynamic changes that can occur in normal delivery and allows for better monitoring and hemodynamic management. Our paper provide in-depth information regarding the pathophysiology of heart valve disease in pregnant women and an appropriate perianesthesia approach to obtain a good prognosis. We report a case of a 26-year-old pregnant woman, with obstetric status G1P0A0, 36 weeks’ gestation, body weight 61 kg accompanied by severe mitral regurgitation and moderate mitral stenosis. This patient was planned to undergo elective cesarean section. The patient's condition in the perioperative examination was: GCS E4V5M6, other vital signs within normal limits, SpO2 98-99% in supine position. Other physical and laboratory examinations were also within normal limits. The goal of anesthesia during surgery in patients with heart valve disease undergoing cesarean section maintain pulmonary capillary pressure to prevent acute pulmonary edema. In this case, regional anesthesia of epidural anesthesia was chosen because it can reduce systemic vascular resistance and provide better post-cesarean section pain. The patient's hemodynamics perianesthesia tended to be stable without any complications such as pulmonary edema.</p><p> </p><p> </p></div><br clear="all" /> <br /><p> </p>

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