CNN and Bidirectional GRU-Based Heartbeat Sound Classification Architecture for Elderly People

Yadav, Harshwardhan; Shah, Param; Gandhi, Neel R.; Vyas, Tarjni; Nair, Anuja; Desai, Shivani; Gohil, Lata; Tanwar, Sudeep; Sharma, Ravi; Verdeș, Marina; Răboaca, Maria Simona

doi:10.3390/math11061365

Cited by 10 publications

(10 citation statements)

References 52 publications

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“…In contrast to Ref. [29], which focused on MFCCs for signal classification, our approach included recordings from all cardiovascular locations. This resulted in a specialized CirCor DigiScope dataset that, despite its smaller size due to stringent selection criteria, offered a more accurate representation of heart sounds, thereby enhancing the reliability of our classification models.…”

Section: Comparative Analysis and Discussionmentioning

confidence: 99%

“…[7] and Ref. [29] implemented complex algorithms and data preprocessing techniques, our approach emphasized simplicity. The custom MIMO model, supported by K-Fold cross-validation, balanced complexity, and performance, is crucial in a field where over-complication can hinder general applicability.…”

Section: Comparative Analysis and Discussionmentioning

confidence: 99%

“…They tailored their model to the CirCor DigiScope dataset, combining CNN and BiGRU with attention mechanisms. This approach attained an impressive 90% accuracy despite the complexity of the data preprocessing and augmentation techniques employed [29]. So far, as demonstrated in Table 2, most researchers have used an older version of datasets along with a proposed separate model for identifying abnormal heartbeat and finding murmur.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Cardiac Auscultation Approach for the detection of abnormal Heartbeat and murmur based on Multimodal Deep Learning

Jan,

Tang

2023

Preprint

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Cardiovascular diseases are a leading cause of global mortality, and timely detection is essential to prevent their detrimental outcomes. Traditional diagnostic methods, such as segmentation, feature extraction, and subsequent heartbeat analysis, present inherent challenges. So far, the published deep learning methods have been designed for a single category, either finding abnormal signals or detecting murmur. Furthermore, most published deep learning methods used heartbeats recorded from different auscultations as similar, such that they are from the same origin. However, the flexibility in designing deep learning models made it possible to analyze heartbeats from various auscultation locations as distinct signals and categorize them in multiple labels. Therefore, this research proposed a novel multi-input-multi-output hybrid deep learning model for heartbeat analysis. This innovative approach integrated a Bidirectional Long Short Term Memory (BiLSTM) along with Dense layer as inputs and added UNet followed by Residual blocks as a backbone to find abnormal and murmured heartbeats simultaneously through multi-output architecture. The first four inputs used BiLSTM layers to extract temporal features independently from the four auscultation locations. The fifth input analyzed the patient's gender through the Dense layer and supported the model by focusing on gender while analyzing heartbeats. The UNet architecture transformed the temporal features into the required or more suitable format, and the Residual blocks managed the model's depth. Finally, all five networks combined and simultaneously classified the patient's heartbeats into two binary categories, providing information that either the signal was normal or the heartbeat had murmured. The Circor DigiScope dataset had missing features and, therefore, was unsuitable for experimentation. Thus, the dataset was modified by excluding samples lacking audio from essential auscultation sites. The proposed model, trained using the K-Fold cross-validation strategy, provided 95% accuracy and avoided overfitting on a small dataset. Therefore, the MIMO strategy provided an in-depth interpretation of cardiac signals by analyzing each signal separately through multiple perspectives. Consequently, it achieved better results and set the stage for future deep-learning algorithms in this domain. Furthermore, using less preprocessing and relying on the flexibility of deep learning algorithms underscored the model's potential to enhance diagnostic accuracy in real-world clinical scenarios.

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Section: Comparative Analysis and Discussionmentioning

confidence: 99%

Section: Comparative Analysis and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Enhanced Cardiac Auscultation Approach for the detection of abnormal Heartbeat and murmur based on Multimodal Deep Learning

Jan,

Tang

2023

Preprint

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“…Cardiovascular conditions encompass various ailments involving blood vessels and the heart, such as rheumatic heart disease, cerebrovascular disorders, and coronary heart disease, among others. Individuals afflicted with hyperlipidemia, diabetes, hypertension, etc., face elevated risks of cardiovascular issues [3], [4]. This disorder gradually becomes a more integral aspect of our lives as we age.…”

Section: Introductionmentioning

confidence: 99%

CNN-LSTM for Heartbeat Sound Classification

Aji,

Kurnianingsih,

Masuyama

et al. 2024

JOIV : Int. J. Inform. Visualization

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Cardiovascular disorders are among the primary causes of death. Regularly monitoring the heart is of paramount importance in preventing fatalities arising from heart diseases. Heart disease monitoring encompasses various approaches, including the analysis of heartbeat sounds. The auditory patterns of a heartbeat can serve as indicators of heart health. This study aims to build a new model for categorizing heartbeat sounds based on associated ailments. The Phonocardiogram (PCG) method digitizes and records heartbeat sounds. By converting heartbeat sounds into digital data, researchers are empowered to develop a deep learning model capable of discerning heart defects based on distinct cardiac rhythms. This study proposes the utilization of Mel-frequency cepstral coefficients for feature extraction, leveraging their application in voice data analysis. These extracted features are subsequently employed in a multi-step classification process. The classification process merges a convolutional neural network (CNN) with a long short-term memory network (LSTM), forming a comprehensive deep learning architecture. This architecture is further enhanced through optimization utilizing the Adagrad optimizer. To examine the effectiveness of the proposed method, its classification performance is evaluated using the "Heartbeat Sounds" dataset sourced from Kaggle. Experimental results underscore the effectiveness of the proposed method by comparing it with simple CNN, CNN with vanilla LSTM, and traditional machine learning methods (MLP, SVM, Random Forest, and k-NN).

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“…Every person has some or the other variations in their body compared to that of others. Thus, biometrics such as fingerprints, voice, iris patterns, electrocardiogram (ECG) signals 3 and so forth of people are also unique. This uniqueness can be exploited for various authentication and security purposes.…”

Section: Introductionmentioning

confidence: 99%

EEG‐based biometric authentication system using convolutional neural network for military applications

Vadher,

Patel,

Nair

et al. 2023

Security and Privacy

Self Cite

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In this technological era, as the need for security arises, the use of biometrics is increasing in authentication systems as a secure and convenient method of human identification and verification. Electroencephalogram (EEG) signals have gained significant attention among the various biometric modalities available because of their unique and unforgeable characteristics. In this study, we have proposed an EEG‐based multi‐subject and multi‐task biometric authentication system for the military applications that address the challenges associated with multi‐task variation in EEG signals. The proposed work considers the use of respective EEG signals for the access of artillery, entrance to highly confidential places for the military and so forth by authenticated personnel only. We have used a multi‐subject, multi‐session, and multi‐task () dataset. The dataset was partially preprocessed with basic signal processing techniques such as bad channel repairing, independent component analysis for artifact removal, downsampling to 250 Hz, and an audio filter of 0.01–200 Hz for signal improvisation. This partially preprocessed dataset was further processed and was used in our deep learning model (DL) architectures. For EEG‐based biometric authentication, convolutional neural network (CNN) outperforms many of the state‐of‐the‐art DL architectures with a validation accuracy of approximately 99.86%, training accuracy of 98.49% and precision, recall and F1‐score with values of 99.91% that makes this EEG‐based approach for authentication more reliable. The DL models were also compared based on training and inference time, where CNN used the most training time but took the least time to predict the output. We compared the performance of the CNN model for three preprocessing techniques by feeding mel spectrograms, chromagrams and mel frequency cepstral coefficients, out of which mel spectrograms provided better results. This proposed architecture proves to be robust and efficient for military applications.

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CNN and Bidirectional GRU-Based Heartbeat Sound Classification Architecture for Elderly People

Cited by 10 publications

References 52 publications

An Enhanced Cardiac Auscultation Approach for the detection of abnormal Heartbeat and murmur based on Multimodal Deep Learning

An Enhanced Cardiac Auscultation Approach for the detection of abnormal Heartbeat and murmur based on Multimodal Deep Learning

CNN-LSTM for Heartbeat Sound Classification

EEG‐based biometric authentication system using convolutional neural network for military applications

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