Quality Assessment and Classification of Heart Sounds Using PCG Signals

2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qurat-ul-ain Mubarak ◽  
Muhammad Usman Akram ◽  
Arslan Shaukat ◽  
Aneeqa Ramazan
Keyword(s):  
Quality Assessment ◽  
Heart Sounds ◽  
Pcg Signals

Analysis of PCG signals using quality assessment and homomorphic filters for localization and classification of heart sounds

2018 ◽  
Vol 164 ◽  
pp. 143-157 ◽  
Author(s):  
Qurat-ul-Ain Mubarak ◽  
Muhammad Usman Akram ◽  
Arslan Shaukat ◽  
Farhan Hussain ◽  
Sajid Gul Khawaja ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Heart Sounds ◽  
Pcg Signals

Classification of Heart Sounds Based on Quality Assessment and Wavelet Scattering Transform

2021 ◽  
pp. 104814
Author(s):  
Na Mei ◽  
Hongxia Wang ◽  
Yatao Zhang ◽  
Feifei Liu ◽  
Xinge Jiang ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Heart Sounds ◽  
Scattering Transform

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.

A Study on Technology-LED Solutions for Fruit Grading to Address Post-Harvest Handling Issues of Horticultural Crops

2020 ◽  
pp. 203-221
Author(s):  
Arun Kumar R. ◽  
Vijay S. Rajpurohit ◽  
Sandeep Kautish
Keyword(s):  
Quality Assessment ◽  
Value Addition ◽  
Research Gaps ◽  
Major Phase ◽  
Comprehensive Review ◽  
Post Harvest ◽  
Horticultural Crops ◽  
Fruit Grading ◽  
Labor Requirements

The reduction of post-harvest losses and value addition of the horticultural corps has attained the higher priority of the current research works. Grading is the major phase in post-harvest handling. Presently grading is done on the basis of observation and through experience. Various drawbacks associated with such manual grading are subjectivity, tediousness, labor requirements, availability, inconsistency, etc. Such problems can be alleviated by incorporating automation in the process. Researchers round the clock are working towards the development of technology-driven solutions in order to grade/sort/classify various agricultural and horticultural produce. With the motto of helping the researchers in the field of grading and quality assessment of fruits and other horticulture products, the present work endeavors the following major contributions: (1) a precise and comprehensive review on technology-driven solutions for grading/sorting/classification of fruits, (2) major research gaps addressed by the researchers, and (3) research gaps to be addressed.

Deep Learning for Heart Sounds Classification Using Scalograms and Automatic Segmentation of PCG Signals

2021 ◽  
pp. 583-596
Author(s):  
John Gelpud ◽  
Silvia Castillo ◽  
Mario Jojoa ◽  
Begonya Garcia-Zapirain ◽  
Wilson Achicanoy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Heart Sounds ◽  
Pcg Signals

scholarly journals Using benthic diatoms for estimating lake ecological quality: Comparing different taxonomic resolution

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Aldo Marchetto ◽  
Tommaso Sforzi
Keyword(s):  
Quality Assessment ◽  
Taxonomic Resolution ◽  
Nutrient Level ◽  
Ecological Quality ◽  
The European Union ◽  
Biological Communities ◽  
Weighted Averages ◽  
Specific Index

The Water Framework Directive asks to all Member States of the European Union to classify the ecological quality of significant waterbodies on the basis of the biological communities they host. One of the biological communities that must be used for the ecological quality assessment is the periphytic community, mainly composed by diatoms. In Italy, diatom-based lake quality assessment is performed using a specific index, named EPI-L, based on the method of weighted averages. For each species, a trophic score and an indicator weight were calculated.  In order to reduce the complexity of the lake quality assessment, we calibrated a variant of EPI-L, using diatoms genera instead of species, and we compared the performance of these two variants in terms of correlation with the nutrient level and of different classification of each lake.

scholarly journals Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

2020 ◽  
Vol 10 (14) ◽  
pp. 4791 ◽  
Author(s):  
Pedro Narváez ◽  
Steven Gutierrez ◽  
Winston S. Percybrooks
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Automatic Classification ◽  
The State ◽  
Heart Sounds ◽  
Empirical Wavelet Transform ◽  
Art Methods

A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

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