Heart Sound Classification Using Machine Learning and Deep Learning Methods

Heart sound classification involves the analysis of recorded heart sounds to distinguish between normal and abnormal rhythms, providing valuable insights for the diagnosis of cardiovascular diseases. Accurate classification of heart sounds plays a vital role in the diagnosis of cardiovascular diseases by identifying characteristic noises produced as the heart beats. These heart sounds are typically captured using stethoscopes, and advanced signal processing techniques are applied to categorize sounds into various classes: normal heartbeats, systolic murmurs, or diastolic murmurs. Deep Machine (DL) and Machine Learning (ML) methods. ML methods such as Support Vector Machines (SVM), typically rely on manually designed audio data that include frequency components or temporal patterns. Conversely, Convolutional neural networks (CNNs) and long short-term memory (LSTM) networks are two examples of deep learning (DL) models that automate feature extraction and learn directly from spectrograms or raw audio. Combining CNNs' attention to spatial patterns in the data, along with the LSTMs' capacity to capture temporal relationships, improves the accuracy and consistency of heart sound analysis for early diagnosis and detection.