Multi-branch convolutional network and LSTM-CNN for heart sound classification
Seyed Amir Latifi1, Hassan Ghassemian2, Maryam Imani1
1Image Processing and Information Analysis Lab, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Insights
This study introduces two deep learning models for fast, cost-effective cardiac disease diagnosis using heart sounds. The Long Short-Term Memory-Convolutional Neural (LSCN) model achieved high accuracy, outperforming existing methods for early cardiovascular disease detection.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Engineering
Background:
- Cardiovascular diseases are a major global health concern, demanding precise and timely diagnosis.
- Current diagnostic methods for cardiac conditions face challenges related to complexity, cost, and accessibility.
- Limited labeled medical datasets pose a significant hurdle in developing automated diagnostic tools.
Purpose of the Study:
- To develop novel deep learning architectures for automated cardiac disease diagnosis from heart sound analysis.
- To address the challenge of limited labeled datasets in medical AI applications.
- To provide fast, accurate, and cost-effective diagnostic solutions for cardiovascular abnormalities.
Main Methods:
- Proposed two deep learning models: a Multi-Branch Deep Convolutional Neural Network (MBDCN) and a Long Short-Term Memory-Convolutional Neural (LSCN) network.
- MBDCN utilizes diverse filter sizes and power spectrum input for enhanced feature extraction, mimicking auditory processing.
- LSCN integrates LSTM blocks with MBDCN to improve time-domain feature extraction, enhancing heart sound analysis.
Main Results:
- The LSCN model achieved a multiclass classification accuracy of 89.65% and binary classification accuracy of 93.93%.
- Both proposed models significantly outperformed traditional methods like Mel Frequency Cepstral Coefficients (MFCC) and wavelet transforms.
- Fivefold cross-validation confirmed the robustness and reliability of the proposed deep learning approaches.
Conclusions:
- The developed deep learning architectures, particularly LSCN, demonstrate high efficacy for automated heart sound analysis.
- These models offer clinically viable and computationally efficient solutions for the early detection of cardiovascular diseases.
- The study highlights the potential of AI in overcoming limitations of traditional diagnostic methods and improving patient outcomes globally.
Abstract:
Cardiovascular diseases represent a leading cause of mortality worldwide, necessitating accurate and early diagnosis for improved patient outcomes. Current diagnostic approaches for cardiac abnormalities often present challenges in clinical settings due to their complexity, cost, or limited accessibility. This study develops two deep learning architectures that offer fast, accurate, and cost-effective methods for automatic diagnosis of cardiac diseases, focusing specifically on addressing the critical challenge of limited labeled datasets in medical contexts. We propose two methodologies: first, a Multi-Branch Deep Convolutional Neural Network (MBDCN) that emulates human auditory processing by utilizing diverse convolutional filter sizes and power spectrum input for enhanced feature extraction; second, a Long Short-Term Memory-Convolutional Neural (LSCN) model that integrates LSTM blocks with MBDCN to improve time-domain feature extraction. The synergistic integration of multiple parallel convolutional branches with LSTM units enables superior performance in heart sound analysis. Experimental validation demonstrates that LSCN achieves multiclass classification accuracy of 89.65% and binary classification accuracy of 93.93%, significantly outperforming state-of-the-art techniques and traditional feature extraction methods such as Mel Frequency Cepstral Coefficients (MFCC) and wavelet transforms. A comprehensive fivefold cross-validation confirms robustness of our approach across varying data partitions. These findings establish the efficacy of our proposed architectures for automated heart sound analysis, offering clinically viable and computationally efficient solutions for early detection of cardiovascular diseases in diverse healthcare environments.
Related Concept Videos
01:30Classification of Signals
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
01:15Heart Sounds
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
01:22Force Classification
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
01:26Classification of Systems-I
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
01:22Multi-input and Multi-variable systems
In the absence of...
01:31Classification of Systems-II