Congenital anomalies arising as a result of a defect in the structure of the
heart and great vessels are known as congenital heart diseases or CHDs. A PCG
can provide essential details about the mechanical conduction system of the
heart and point out specific patterns linked to different kinds of CHD. This
study aims to investigate the minimum signal duration required for the
automatic classification of heart sounds. This study also investigated the
optimum signal quality assessment indicator (Root Mean Square of Successive
Differences) RMSSD and (Zero Crossings Rate) ZCR value. Mel-frequency cepstral
coefficients (MFCCs) based feature is used as an input to build a
Transformer-Based residual one-dimensional convolutional neural network, which
is then used for classifying the heart sound. The study showed that 0.4 is the
ideal threshold for getting suitable signals for the RMSSD and ZCR indicators.
Moreover, a minimum signal length of 5s is required for effective heart sound
classification. It also shows that a shorter signal (3 s heart sound) does not
have enough information to categorize heart sounds accurately, and the longer
signal (15 s heart sound) may contain more noise. The best accuracy, 93.69%, is
obtained for the 5s signal to distinguish the heart sound.

心脏先天性疾病中的缺陷导致的先天畸形称为先天性心脏病或 CHDs。本研究通过使用最小信号持续时间来自动分类心音，并调查了最佳信号质量评估指标（RMSSD 和 ZCR）的价值。建立了基于 Mel 频率倒谱系数（MFCCs）的特征的基于 Transformer-Based 残差一维卷积神经网络，用于对心音进行分类。研究表明，0.4 是获得适合 RMSSD 和 ZCR 指标的信号的理想阈值，对于有效的心音分类，需要最小 5s 的信号长度。较短的信号（3s 的心音）不具有足够的信息来准确分类心音，而较长的信号（15s 的心音）可能含有更多噪音。通过对 5s 信号进行区分心音，获得了最佳准确率 93.69%。