Unsegmented Heart Sound Classification Using Hybrid CNN-LSTM Neural Networks

Megalmani, Drishti Ramesh; Shailesh, B G; V, Achuth Rao M; Jeevannavar, Satish S; Ghosh, Prasanta Kumar

doi:10.1109/embc46164.2021.9629596

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Wang et al [76] test 10 different CNN models including GoogleNet, SqueezeNet, DarkNet19, ModileNetv2, Inception-ResNetv2, DenseNet201, Inceptionv3, ResNet101, NasNet-Large, and Xception to compare the performances. Since heart sounds are continuous sequence signals, it is also suitable to test RNN models [8, 12, 49, 66], while other studies combine both CNN and RNN [29, 64, 65, 67, 74]. Other models include Transformer [7], traditional neural network (NN), and some NN modifications such as time delay neural network, time growing neural network, and kernel sparse representation network.…”

Section: Resultsmentioning

confidence: 99%

“…Since detecting cardiac murmurs is a relatively more straightforward task compared to identifying specific diseases, there has been a notable accumulation of high-quality annotated data in recent years, which has been made available to the public [20, 94]. The abundance of accessible data has led to a surge in DL models research on cardiac murmurs detection [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56]. Although these models can only discern the presence of cardiac murmurs and cannot provide definitive diagnoses, they still play a crucial role in community-based disease screening.…”

Section: Resultsmentioning

confidence: 99%

“…At last, 71 original articles were included. These studies can be broadly categorized into several groups: methods (15 papers, including heart sound segmentation [6, 7, 8, 9, 10, 11, 12, 13], noise cancellation [14, 15, 16], algorithm development [17, 18, 19], and database development [20]), cardiac murmurs detection (36 papers [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56]), valvular heart disease (6 papers [57, 58, 59, 60, 61, 62]), congenital heart disease (4 papers [63, 64, 65, 66]), heart failure (4 papers [67, 68, 69, 70]), coronary artery disease (2 papers [71, 72]), rheumatic heart disease (2 papers [73, 74]), and extracardiac applications (2 papers [75, 76]).…”

Section: Methodsmentioning

confidence: 99%

“…The abundance of accessible data has led to a surge in DL models research on cardiac murmurs detection[21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56]. Although these models can only discern the presence of cardiac murmurs and cannot provide definitive diagnoses, they still play a crucial role in community-based disease screening.…”

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confidence: 99%

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Deep Learning for Heart Sound Analysis: A Literature Review

Zhao,

Geng,

Wang

et al. 2023

Preprint

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Heart sound auscultation is a physical examination routinely used in clinical practice to identify potential cardiac abnormalities. However, accurate interpretation of heart sounds requires specialized training and experience, thereby limiting its generalizability. Deep learning, a subset of machine learning, involves training artificial neural networks to learn from large datasets and perform complex tasks related to intricate patterns, such as disease diagnosis, event prediction, and clinical decision-making. Over the past decade, deep learning has been successfully applied to heart sound analysis with remarkable achievements. Meanwhile, as heart sound analysis is gaining attention, many public and private heart sound datasets have been established for model training. The massive accumulation of heart sound data improves the performance of deep learning-based heart sound models and extends their clinical application scenarios. In this review, we will compile the commonly used datasets in heart sound analysis, introduce the fundamentals and state-of-the-art techniques in heart sound analysis and deep learning, and summarize the current applications of deep learning for heart sound analysis and their limitations for future improvement.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Deep Learning for Heart Sound Analysis: A Literature Review

Zhao,

Geng,

Wang

et al. 2023

Preprint

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show abstract

“…Recently, Deng et al [29] constructed CRNN, which combines CNN and RNN to improve feature extraction capabilities. In recent studies, some non-segmented methods have been proposed in abnormal heart sound detection [23], [30], [31]. However, a large number of time-frequency models mainly learn the local features of heart sounds, and there are few studies on the global feature representation of heart sounds.…”

Section: Related Workmentioning

confidence: 99%