Detection of pathological heart sounds

Abdollahpur, Mostafa; Ghaffari, Ali; Ghiasi, Shadi; Mollakazemi, Mohammad Javad

doi:10.1088/1361-6579/aa7840

Cited by 57 publications

(28 citation statements)

References 26 publications

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“…The variation of normal (9857) and abnormal samples (3158) results in degrading the performance of the model [34]. Author Sensitivity% Specificity% Accuracy% Tang et al [19] 88.00 87.00 88.00 Dominguez et al [12] 93.20 95.12 97.00 Bradley et al [11] 90.07 88.45 89.26 Mostafa et al [13] 76.96 88.31 82.63 Masun et al [14] 79.60 80.60 80.10 Plesinger et al [17] 89.00 81.60 85.00 Vykintas et al [20] 80.63 87.66 84.15 Wei et al [21] 98.33 84.67 91.50 Philip et al [24] 77.00 80.00 79.00 Singh et al [33] 93.00 90.00 90.00 This study 94.08 91.95 92.47…”

Section: Discussionmentioning

confidence: 99%

Short unsegmented PCG classification based on ensemble classifier

Singh¹,

Majumder²

2020

Turk J Elec Eng & Comp Sci

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Diseases associated with the heart are one of the main reasons of death worldwide. Hence, early examination of the heart is important. For analysis of cardiac disorders, a study of heart sounds is a crucial and beneficial approach.Still, automated classification of heart sounds is a challenging task that mainly depends on segmentation of heart sounds and derivation of features using segmented samples. In the literature available for PCG classification provided by PhysioNet/CinC Challenge 2016, most of the research has focused on enhancing the accuracy of the classification model based on complicated segmentation processes and has failed to improve the sensitivity. In this paper, we present an automated heart sound classification by eliminating the segmentation steps using multidomain features, which results in enhanced sensitivity. The study is based on homomorphic envelogram, mel frequency cepstral coefficient (MFCC), power spectral density (PSD), and multidomain feature extraction. The extracted features are trained using the 5-fold cross-validation method based on an ensemble boosting algorithm over 100 independent iterations. Our proposed design is evaluated using public datasets published in PhysioNet/Computers in Cardiology Challenge 2016. Accuracy of 92.47% with improved sensitivity of 94.08% and specificity of 91.95% is achieved using our model. The output performance proves that our proposed model offers superior performance results.

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Section: Discussionmentioning

confidence: 99%

Short unsegmented PCG classification based on ensemble classifier

Singh¹,

Majumder²

2020

Turk J Elec Eng & Comp Sci

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“…Table 1 shows the mechanism of the generation of heart sounds, including the cause, features and signi cance of heart sounds [5]. e fundamental heart sounds (FHS) [6] used in clinical diagnosis include S1 and S2 (S3 appears only in the cardiac cycles of some healthy young people, and S4 does not appear in normal cardiac cycles). e period between S1 and S2 in the same cardiac cycle is called systole, and the one between S2 and S1 in the next cycle is called diastole.…”

Section: Heart Sounds and Cardiovascular Diseasesmentioning

confidence: 99%

A Review of Computer-Aided Heart Sound Detection Techniques

Tang

et al. 2020

BioMed Research International

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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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“…Moreover, it is the most used technique by doctors for the primary screening of early cardiac illnesses [ 8 ]. Cardiovascular diseases are one of the major causes of death in the world; hence, it is important to evaluate cardiac functions using a fast, non-invasive technique [ 23 ]. The main heart sounds include S1 and S2.…”

Section: Background and Factors To Considermentioning

confidence: 99%

Wearable Hardware Design for the Internet of Medical Things (IoMT)

Qureshi

Krishnan

2018

Sensors

122

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As the life expectancy of individuals increases with recent advancements in medicine and quality of living, it is important to monitor the health of patients and healthy individuals on a daily basis. This is not possible with the current health care system in North America, and thus there is a need for wireless devices that can be used from home. These devices are called biomedical wearables, and they have become popular in the last decade. There are several reasons for that, but the main ones are: expensive health care, longer wait times, and an increase in public awareness about improving quality of life. With this, it is vital for anyone working on wearables to have an overall understanding of how they function, how they were designed, their significance, and what factors were considered when the hardware was designed. Therefore, this study attempts to investigate the hardware components that are required to design wearable devices that are used in the emerging context of the Internet of Medical Things (IoMT). This means that they can be used, to an extent, for disease monitoring through biosignal capture. In particular, this review study covers the basic components that are required for the front-end of any biomedical wearable, and the limitations that these wearable devices have. Furthermore, there is a discussion of the opportunities that they create, and the direction that the wearable industry is heading in.

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Detection of pathological heart sounds

Cited by 57 publications

References 26 publications

Short unsegmented PCG classification based on ensemble classifier

Short unsegmented PCG classification based on ensemble classifier

A Review of Computer-Aided Heart Sound Detection Techniques

Wearable Hardware Design for the Internet of Medical Things (IoMT)

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