A fusion framework based on multi-domain features and deep learning features of phonocardiogram for coronary artery disease detection

Han, Ling; Wang, Xinpei; Liu, Changchun; Zeng, Qiang; Zheng, Yansong; Chu, Xi; Yao, Lianke; Wang, Jikuo; Jiao, Yu; Karmakar, Chandan

doi:10.1016/j.compbiomed.2020.103733

Cited by 61 publications

(24 citation statements)

References 42 publications

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“…Along with sensitivity 92.5% and specificity 98.1%, the model exhibits satisfactory performance. A comprehensive basis for CAD diagnosis has been provided in [20] employing a novel multidomain feature fusion framework. For the in-house dataset used, the concerted feeding of the selected MFCC features with the deep features into a MLP classifier has obtained a promising accuracy of 90.43%.…”

Section: A Physiological Origin Of Cardiac Auscultationmentioning

confidence: 99%

CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings

et al. 2021

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The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases (CVDs) signify the crucial need for early detection schemes. Phonocardiogram (PCG) signals have been historically applied in this domain owing to its simplicity and cost-effectiveness. In this paper, we propose CardioXNet, a novel lightweight end-to-end CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse using raw PCG signal. The process has been automated by the involvement of two learning phases namely, representation learning and sequence residual learning. Three parallel CNN pathways have been implemented in the representation learning phase to learn the coarse and fine-grained features from the PCG and to explore the salient features from variable receptive fields involving 2D-CNN based squeeze-expansion. Thus, in the representation learning phase, the network extracts efficient timeinvariant features and converges with great rapidity. In the sequential residual learning phase, because of the bidirectional-LSTMs and the skip connection, the network can proficiently extract temporal features without performing any feature extraction on the signal. The obtained results demonstrate that the proposed end-toend architecture yields outstanding performance in all the evaluation metrics compared to the previous stateof-the-art methods with up to 99.60% accuracy, 99.56% precision, 99.52% recall and 99.68% F1-score on an average while being computationally comparable. This model outperforms any previous works using the same database by a considerable margin. Moreover, the proposed model was tested on PhysioNet/CinC 2016 challenge dataset achieving an accuracy of 86.57%. Finally the model was evaluated on a merged dataset of Github PCG dataset and PhysioNet dataset achieving excellent accuracy of 88.09%. The high accuracy metrics on both primary and secondary dataset combined with a significantly low number of parameters and end-to-end prediction approach makes the proposed network especially suitable for point of care CVD screening in low resource setups using memory constraint mobile devices.

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Section: A Physiological Origin Of Cardiac Auscultationmentioning

confidence: 99%

CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings

et al. 2021

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“…Li et al 10 introduced a new feature fusion approach that fed Mel-frequency cepstral coefficients to a CNN to output valuable features that were in turn fused and provided to a multilayer perceptron for classification. In subsequent work, Li et al 11 demonstrated the efficacy of a new dual-input neural network that analyzed simultaneously assembled PCG and ECG signals using combined deep learning and feature extraction to extract useful underlying information in the signals.…”

Section: Related Workmentioning

confidence: 99%

RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance

Khozeimeh

Sharifrazi

Izadi

et al. 2022

Sci Rep

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Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is costly and associated with risks. Noninvasive imaging like cardiac magnetic resonance (CMR) facilitates CAD assessment and can serve as a gatekeeper to downstream invasive testing. Machine learning methods are increasingly applied for automated interpretation of imaging and other clinical results for medical diagnosis. In this study, we proposed a novel CAD detection method based on CMR images by utilizing the feature extraction ability of deep neural networks and combining the features with the aid of a random forest for the very first time. It is necessary to convert image data to numeric features so that they can be used in the nodes of the decision trees. To this end, the predictions of multiple stand-alone convolutional neural networks (CNNs) were considered as input features for the decision trees. The capability of CNNs in representing image data renders our method a generic classification approach applicable to any image dataset. We named our method RF-CNN-F, which stands for Random Forest with CNN Features. We conducted experiments on a large CMR dataset that we have collected and made publicly accessible. Our method achieved excellent accuracy (99.18%) using Adam optimizer compared to a stand-alone CNN trained using fivefold cross validation (93.92%) tested on the same dataset.

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“…There are some researches that apply deep learning and deep neural networks in heartbeat abnormality detection [19][20][21][22][23]. Despite the high accuracy, deep learning architectures are limited with the decision-making time, since the electronic stethoscope must make a decision promptly.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence in Medicine: Real Time Electronic Stethoscope for Heart Diseases Detection

Omarov¹,

Saparkhojayev²,

Shekerbekova³

et al. 2022

Computers, Materials &Amp; Continua

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Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of an electronic stethoscope, (2) a decision-making subsystem, and (3) a subsystem for displaying and visualizing the results in an understandable form. The electronic stethoscope captures the patient's phonocardiographic sounds, filters and digitizes them, and then sends the resulting phonocardiographic sounds to the decision-making system. The decision-making system classifies sounds into normal and abnormal using machine learning techniques, and as a result identifies abnormal heart sounds. The display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff, but also for patients and recommends further actions to patients. As a result of the study, we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%. More accurately, the proposed stethoscope can identify normal heart sounds with 93.5% accuracy, abnormal heart sounds with 93.25% accuracy. Moreover, speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination.

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A fusion framework based on multi-domain features and deep learning features of phonocardiogram for coronary artery disease detection

Cited by 61 publications

References 42 publications

CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings

CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings

RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance

Artificial Intelligence in Medicine: Real Time Electronic Stethoscope for Heart Diseases Detection

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