Normal and Abnormal Heart Rates Recognition Using Transfer Learning

Alafif, Tarik; Boulares, Mehrez; Barnawi, Ahmed; Alafif, Talal; Althobaiti, Hassan; Alferaidi, Ali

doi:10.1109/kse50997.2020.9287514

Cited by 21 publications

(11 citation statements)

References 15 publications

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“…Conventionally, the 1D heart sound signals are first converted into 2D feature maps that represent the time and frequency characteristics of the heart sound signals and satisfy the unified standards for 2D CNN inputs for heart sounds classification. The feature maps most commonly used for heart sounds classification include MFSC [19,25,32,33], MFCC [26,32], and spectrograms [30,31,34]. Rubin et al [29] proposed a 2D CNN-based approach for the automatic recognition of normal and abnormal PCG signals.…”

Section: Cnn Methods For Heart Sounds Classificationmentioning

confidence: 99%

“…The majority vote strategy was used to determine the category of the PCG signals, affording better robustness when applied to long heart sounds. In addition to MFSC features, MFCC features obtained by eliminating the inter-dimensional correlation through the discrete cosine transform (DCT) have also been utilized as the input vector of the CNN [24,26,29,32].…”

Section: Cnn Methods For Heart Sounds Classificationmentioning

confidence: 99%

“…It should be noted that the utilized datasets were balanced, with each class of heart sound containing 200 recordings. Most deep learning-based methods do not utilize a segmentation algorithm to identify S1, S2, systole, and diastole heart sounds, such as [25][26][27][28]30,31,[33][34][35][36][37][38]44,45,[47][48][49][50][51]54,55]. The methods are nevertheless very efficient for automatic heart sounds classification.…”

Section: Hybrid Methods For Heart Sounds Classificationmentioning

confidence: 99%

“…Alafif et al [26] used transfer learning to automate the recognition of normal and abnormal heart sounds. They used the MFCC representation as the input to various pretrained CNN models such as SqueezeNet, GoogLeNet, Inception-V3, and Xception, which were fine-tuned on the new dataset.…”

Section: Training Efficiencymentioning

confidence: 99%

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Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Chen

Sun

Chen

et al. 2021

Entropy

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The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

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Section: Cnn Methods For Heart Sounds Classificationmentioning

confidence: 99%

Section: Cnn Methods For Heart Sounds Classificationmentioning

confidence: 99%

Section: Hybrid Methods For Heart Sounds Classificationmentioning

confidence: 99%

Section: Training Efficiencymentioning

confidence: 99%

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Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Chen

Sun

Chen

et al. 2021

Entropy

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“…The researchers stated that they first removed the noise in the heartbeat sound signals and then used LSTM and RNN structures to classify the feature maps they obtained. The researchers obtained an accuracy of 80.80% with the LSTM model they proposed.Alafif et al,9 in their study, divided the heartbeat sound signals into two different categories as normal and abnormal. In their study, the researchers first used the MFCC method to extract features from heart sounds.…”

mentioning

confidence: 99%

Automatic classification and diagnosis of heart valve diseases using heart sounds with MFCC and proposed deep model

Yıldırım

2022

Concurrency and Computation

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Summary Heart sounds have been widely used for years to monitor and classify heart diseases. Experts manually examine these sounds, which is arduous and time‐consuming. In addition, since interpreting these sounds requires experience, experts who do not have enough experience may misinterpret these sounds. For this reason, a new deep one‐dimensional Convolutional Neural Network (1D‐CNN) architecture has been proposed to increase the detection accuracy and alleviate the workload of experts in the classification of sound signals used in the diagnosis of heart valve diseases. In the developed model, first, feature maps were obtained from heart sounds by using the MFCC method. High performance was achieved when the feature maps obtained later were classified in the developed deep architecture. Furthermore, the feature maps generated by the MFCC approach were classified using traditional machine learning classifiers. When the obtained results were compared, it was observed that the suggested deep model was more successful. In the developed architecture, an accuracy rate of 99.5% was obtained. The accuracy rate obtained shows that the developed architecture can be used to classify heart sounds and diagnose heart valve diseases.

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Improvement in the performance of deep learning based on CNN to classify the heart sound by evaluating hyper-parameters

Roy,

Mandal

2024

Soft Comput

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Normal and Abnormal Heart Rates Recognition Using Transfer Learning

Cited by 21 publications

References 15 publications

Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Automatic classification and diagnosis of heart valve diseases using heart sounds with MFCC and proposed deep model

Improvement in the performance of deep learning based on CNN to classify the heart sound by evaluating hyper-parameters

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