Addressing Varying Lengths in PCG Signal Classification with BiLSTM Model and MFCC Features

In this paper, we propose a novel approach for Phonocardiogram (PCG) signal classification using a BiLSTM model with Mel Frequency Cepstral Coefficients (MFCC) features extracted from short PCG segments. The issue of handling PCG signals of varying lengths is addressed by segmenting the audio signal, allowing for feature extraction and organization with a fixed dimension that is compatible with the input layer of the BiLSTM model. Our approach achieves state-of-the-art performance while utilizing only two BiLSTM layers, making it an efficient and lightweight model for embedded applications. The combination of MFCC features, a handcrafted feature extraction method, with a BiLSTM architecture, addresses the issue of feature engineering for improving PCG signal classification performance. Our study is the first work in the literature to explore the potential benefits of using MFCC features with a BiLSTM model for PCG signal classification. The proposed approach has the potential to significantly impact the healthcare industry by improving the accuracy and efficiency of PCG signal classification, aiding in earlier diagnosis and treatment.