Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

Noman, Fuad; Ting, Chee-Ming; Salleh, Shaharuddin; Ombao, Hernando

doi:10.1109/icassp.2019.8682668

Cited by 71 publications

(49 citation statements)

References 19 publications

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“…For this reason, heart signals have been critically studied to make a diagnosis[10][11][12][13]. CNN is considered a state-of-the-art tool for detecting and classification heart signals and has been studied with several variations like 1-dimensional, 2-dimensional, or the combination of both[14,15]. Similarly, Noman et al[15] proposed a framework based on 1-dimensional CNN for direct feature learning from raw heart signals and 2-dimensional CNN, which takes 2-dimensional time-frequency feature maps.…”

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confidence: 99%

Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

2021

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Cardiac disease is the leading cause of death worldwide. Cardiovascular diseases can be prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to as the diagnostic assistant tool for screening of cardiac disorder. The research purposes of a cardiac disorder detection system from 12-lead-based ECG Images. The healthcare institutes used various ECG equipment that present results in nonuniform formats of ECG images. The research study proposes a generalized methodology to process all formats of ECG. Single Shoot Detection (SSD) MobileNet v2-based Deep Neural Network architecture was used to detect cardiovascular disease detection. The study focused on detecting the four major cardiac abnormalities (i.e., myocardial infarction, abnormal heartbeat, previous history of MI, and normal class) with 98% accuracy results were calculated. The work is relatively rare based on their dataset; a collection of 11,148 standard 12-lead-based ECG images used in this study were manually collected from health care institutes and annotated by the domain experts. The study achieved high accuracy results to differentiate and detect four major cardiac abnormalities. Several cardiologists manually verified the proposed system’s accuracy result and recommended that the proposed system can be used to screen for a cardiac disorder.

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confidence: 99%

Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

2021

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“…Considering the sound processing field, few studies have been conducted in this field, especially classifying and grading fluids sounds, except the analysis and recognition of human voices, which is a highly active area and works such as speech [34][35][36][37][38][39][40] recognition have been performed in this regard. However, studies have been recently conducted on the classification of fluids sounds in other areas, including human heartbeat [41], urban sounds [42][43][44][45][46][47][48] play sounds, car horns, air conditioning sound, engine sounds, etc. ), and music [49][50][51][52][53][54][55][56], using deep learning.…”

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confidence: 99%

Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

Yazdani¹,

Mehr²,

Showkatyan³

et al. 2021

IJIGSP

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The present study, the main idea of which was based on one of the questions of I.P.T.2018 competition, aimed to develop a high-precision relationship between the fluid temperature and the sound produced when colliding with different surfaces, by creating a data collection tool. In fact, this paper was provided based on a traditional phenomenological project using the well-known deep neural networks, in order to achieve an acceptable accuracy in this project. In order to improve the quality of the paper, the data were analyzed in two ways: I. Using the images of data spectrogram and the known V.G.G.16 network. II. Applying the data audio signal and a convolutional neural network (C.N.N.). Finally, both methods have obtained an acceptable precision above 85%.

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“…The experiments in [26,28,[32][33][34][35][36][37]40] were performed with the Physionet database [20]. In [32], features were extracted in time, frequency, wavelet and statistics, obtaining a total of 29 features.…”

Section: Authormentioning

confidence: 99%

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

2020

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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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Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

Cited by 71 publications

References 19 publications

Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

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

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