Augmented detection of septal defects using advanced optical coherence tomography network-processed phonocardiogram

Huang, Po-Kai; Yang, Ming-Chun; Wang, Zixuan; Huang, Yu-Jung; Lin, Wei‐Chen; Pan, Chung-Long; Guo, Meng

doi:10.3389/fcvm.2022.1041082

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…These signals were then utilized to train an advanced optical coherence tomography network model for heart sound classification. Remarkably, this model outperformed experienced cardiologists in detecting VSD and ASD with an accuracy of 93.4% and 85.3%, respectively [107]. Liu et al developed a residual convolution recurrent neural network model to detect ASD, VSD, PDA, and combined CHD using 884 heart sound recordings from children with left-to-right shunt CHD, with accuracy values ranging from 94.0% to 99.4% [65].…”

Section: Resultsmentioning

confidence: 99%

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%

Deep Learning for Heart Sound Analysis: A Literature Review

Zhao,

Geng,

Wang

et al. 2023

Preprint

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“…Vepa [7] investigated the use of features derived from the cepstrum of heart sound signals to classify murmurs into normal, systolic, and diastolic using a support vector machine (SVM) [7,8] trained on cepstral features. Huang et al [9] aimed to develop an intelligent diagnostic method for detecting heart murmurs in patients with ventricular and atrial septal defects. Shekhar et al [10] developed a computer algorithm to assist primary care providers in identifying Still's murmur in children, thereby decreasing overreferral to pediatric cardiologists.…”

Section: Introductionmentioning

confidence: 99%

Heart Murmur Classification Using a Capsule Neural Network

Tsai,

Liu,

Zheng

et al. 2023

Bioengineering

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The healthcare industry has made significant progress in the diagnosis of heart conditions due to the use of intelligent detection systems such as electrocardiograms, cardiac ultrasounds, and abnormal sound diagnostics that use artificial intelligence (AI) technology, such as convolutional neural networks (CNNs). Over the past few decades, methods for automated segmentation and classification of heart sounds have been widely studied. In many cases, both experimental and clinical data require electrocardiography (ECG)-labeled phonocardiograms (PCGs) or several feature extraction techniques from the mel-scale frequency cepstral coefficient (MFCC) spectrum of heart sounds to achieve better identification results with AI methods. Without good feature extraction techniques, the CNN may face challenges in classifying the MFCC spectrum of heart sounds. To overcome these limitations, we propose a capsule neural network (CapsNet), which can utilize iterative dynamic routing methods to obtain good combinations for layers in the translational equivariance of MFCC spectrum features, thereby improving the prediction accuracy of heart murmur classification. The 2016 PhysioNet heart sound database was used for training and validating the prediction performance of CapsNet and other CNNs. Then, we collected our own dataset of clinical auscultation scenarios for fine-tuning hyperparameters and testing results. CapsNet demonstrated its feasibility by achieving validation accuracies of 90.29% and 91.67% on the test dataset.

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Deep Learning in Heart Sound Analysis: From Techniques to Clinical Applications

Zhao,

Geng,

Wang

et al. 2024

Health Data Sci

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Importance: Heart sound auscultation is a routinely used physical examination in clinical practice to identify potential cardiac abnormalities. However, accurate interpretation of heart sounds requires specialized training and experience, which limits its generalizability. Deep learning, a subset of machine learning, involves training artificial neural networks to learn from large datasets and perform complex tasks with intricate patterns. Over the past decade, deep learning has been successfully applied to heart sound analysis, achieving remarkable results and accumulating substantial heart sound data for model training. Although several reviews have summarized deep learning algorithms for heart sound analysis, there is a lack of comprehensive summaries regarding the available heart sound data and the clinical applications. Highlights: This review will compile the commonly used heart sound datasets, 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, along with their limitations and areas for future improvement. Conclusions: The integration of deep learning into heart sound analysis represents a significant advancement in clinical practice. The growing availability of heart sound datasets and the continuous development of deep learning techniques contribute to the improvement and broader clinical adoption of these models. However, ongoing research is needed to address existing challenges and refine these technologies for broader clinical use.

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Augmented detection of septal defects using advanced optical coherence tomography network-processed phonocardiogram

Cited by 3 publications

References 34 publications

Deep Learning for Heart Sound Analysis: A Literature Review

Deep Learning for Heart Sound Analysis: A Literature Review

Heart Murmur Classification Using a Capsule Neural Network

Deep Learning in Heart Sound Analysis: From Techniques to Clinical Applications

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