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Cardio-Dense: Diagnosis of Cardiac Abnormalities Based on Phonocardiogram Using Improved Swin Transformer Through
Alaa E S Ahmed1, Mostafa E A Ibrahim1, Yassine Daadaa1
1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.
Insights
Cardio-Dense accurately detects cardiovascular diseases (CVDs) using phonocardiogram (PCG) signals. This deep learning model offers a low-cost, non-invasive method for early heart valve disease diagnosis.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Medicine
- Cardiology
Background:
- Cardiovascular diseases (CVDs) are a leading global cause of mortality.
- Accurate, low-cost diagnostic tools for CVDs, particularly using phonocardiogram (PCG) signals, are essential.
- Current deep learning (DL) approaches for CVD detection face limitations in accuracy, computational resources, and data requirements.
Purpose of the Study:
- To propose Cardio-Dense, a novel hybrid deep learning framework for multi-class cardiovascular disease detection from PCG signals.
- To develop an efficient and accurate method for identifying various heart valve diseases using phonocardiogram data.
- To provide a cost-effective, non-invasive diagnostic solution suitable for clinical and portable applications.
Main Methods:
- Phonocardiogram (PCG) signals are denoised in the wavelet domain.
- Continuous Wavelet Transform (CWT) converts denoised PCG waveforms into 2D time-frequency spectrograms.
- A hybrid deep learning architecture combining Swin transformer and DenseBlocks is employed for feature extraction and classification.
Main Results:
- The Cardio-Dense model achieved high performance metrics, including 0.977 accuracy, 0.975 sensitivity, 0.992 specificity, 0.978 F1-score, 0.978 AUC, and 0.976 precision.
- The framework demonstrated low computational overhead, making it suitable for real-time inference.
- Experiments were conducted on multiple PCG datasets covering five distinct cardiovascular disease classes.
Conclusions:
- The proposed Cardio-Dense model offers an economical and non-invasive approach for the preliminary, signal-level identification of multi-class heart valve diseases.
- This method significantly reduces the reliance on laborious and error-prone manual analysis of PCG signals by clinicians.
- The model provides rapid, near-real-time classification capabilities beneficial for both clinical settings and portable diagnostic devices.
Abstract:
Background: Cardiovascular diseases (CVDs) are among the top sources of mortality worldwide. To properly diagnose cardiovascular diseases, a low-cost remedy based on phonocardiography (PCG) signals must be proposed. Several deep learning (DL)-driven CVD systems are now being developed to identify various phases of the disease. Nevertheless, the approaches' accuracy falls short of expectations, and they necessitate substantial processing resources and training data. Methods: This paper proposes Cardio-Dense, a hybrid framework for multi-class CVD detection from phonocardiogram signals. The PCG waveform is first denoised in the wavelet domain and then converted into a 2D time-frequency spectrogram using continuous wavelet transform (CWT). We design a joint architecture that combines a Swin transformer for capturing global contextual dependencies with lightweight DenseBlocks for efficient local feature refinement, enabling robust learning from PCG spectrograms across five disease classes. Results: Experiments on PCG datasets achieve up to 0.977 accuracy, 0.975 sensitivity, 0.992 specificity, 0.978 F1-score, 0.978 AUC, and 0.976 precision, while maintaining low computational overhead suitable for real-time inference. Conclusions: The findings indicate that the proposed model provides an economical, non-invasive method for preliminary signal-level identification of multi-class heart valve diseases. It benefits clinicians by decreasing the need for arduous and error-prone manual PCG analysis. Furthermore, it offers quick, near-real-time categorization suitable for clinical and portable applications.
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