  Time-Frequency Distributions (TFDs) support the heart sound characterisation
and classification in early cardiac screening. However, despite the frequent
use of TFDs in signal analysis, no study comprehensively compared their
performances on deep learning for automatic diagnosis. Furthermore, the
combination of signal processing methods as inputs for Convolutional Neural
Networks (CNNs) has been proved as a practical approach to increasing signal
classification performance. Therefore, this study aimed to investigate the
optimal use of TFD/ combined TFDs as input for CNNs. The presented results
revealed that: 1) The transformation of the heart sound signal into the TF
domain achieves higher classification performance than using of raw signals.
Among the TFDs, the difference in the performance was slight for all the CNN
models (within $1.3\%$ in average accuracy). However, Continuous wavelet
transform (CWT) and Chirplet transform (CT) outperformed the rest. 2) The
appropriate increase of the CNN capacity and architecture optimisation can
improve the performance, while the network architecture should not be overly
complicated. Based on the ResNet or SEResNet family results, the increase in
the number of parameters and the depth of the structure do not improve the
performance apparently. 3) Combining TFDs as CNN inputs did not significantly
improve the classification results. The findings of this study provided the
knowledge for selecting TFDs as CNN input and designing CNN architecture for
heart sound classification.
