Hybrid Sneaky algorithm-based deep neural networks for Heart sound classification using phonocardiogram.

Area of Science:

• Cardiology
• Artificial Intelligence
• Biomedical Signal Processing

Background:

• Heart sound analysis is vital for diagnosing cardiac disorders, necessitating accurate and efficient classification methods.
• Early detection of cardiac conditions improves patient outcomes and reduces healthcare burdens.
• Computerized heart sound classification offers a promising avenue for enhanced diagnostic accuracy and speed.

Purpose of the Study:

• To propose an automatic heart sound classification module using a hybrid optimization-controlled deep learning strategy.
• To optimize the Deep Neural Network (DNN) classifier parameters using a novel Hybrid Sneaky optimization algorithm.
• To enhance the classification performance by integrating various extracted features from Phonocardiogram (PCG) data.

Main Methods:

• A hybrid optimization-controlled deep learning strategy was developed for automatic heart sound classification.
• The Hybrid Sneaky optimization algorithm, incorporating questing and societal search traits, was employed for DNN parameter tuning.
• Feature extraction from the PCG database included statistical features, Heart Rate Variability (HRV), and Mel frequency Cepstral coefficients (MFCC).

Main Results:

• The developed Sneaky optimization-based DNN classifier demonstrated high performance metrics.
• Precision, accuracy, specificity, and sensitivity were reported at approximately 97%, 96.98%, 97%, and 96.9%, respectively.
• The integration of MFCC features further enhanced the model's classification capabilities.

Conclusions:

• The proposed hybrid optimization-controlled deep learning module offers an effective approach for automatic heart sound classification.
• The Hybrid Sneaky optimization algorithm successfully tuned DNN parameters, leading to superior diagnostic performance.
• This method holds significant potential for improving the early and accurate diagnosis of cardiac disorders.