Heart Sound Classification

By integrating cardiac cycle segmentation and CNN technology, we can achieve more accurate heart rate detection and analysis. Cardiac cycle segmentation is a method that divides electrocardiogram (ECG) signals into distinct cardiac cycles, enabling better understanding of cardiac function and conditions. Typical implementation involves using peak detection algorithms (like Pan-Tompkins) to identify R-peaks and define cycle boundaries, followed by signal normalization to ensure consistent input dimensions. CNN (Convolutional Neural Network), a deep learning algorithm, enhances heart rate detection accuracy through automated feature extraction and classification of ECG patterns. The architecture typically includes convolutional layers for local pattern recognition, pooling layers for dimensionality reduction, and fully connected layers for final classification. By combining these techniques - where segmentation preprocessing ensures temporal consistency and CNN handles complex pattern recognition - we can significantly improve heart rate analysis methodologies. This integrated approach provides more comprehensive and precise cardiac health assessments, with potential code implementation involving PyTorch/TensorFlow frameworks for CNN development and signal processing libraries like SciPy for cycle segmentation.