# Classification of Heart Sounds Using Convolutional Neural Network
## 4 Discussion  
In this study, we presented a method for recognizing normal and abnormal heart sounds that uses an ensemble of a feature engineering method and a deep learning algorithm. The outcomes from the proposed method outperform the reported best accuracy (0.8602) released by PhysioNet/Computing in Cardiology Challenge2016. In this study, we have also explored the challenges that were mentioned in the introduction.  
Regarding the challenge of feature types, we extracted 497 features from eight domains based on our previous work. To the best of our knowledge, this feature set contains almost all of heart sounds features that have been referred to in the existing literature. We hope that these features will be helpful for the research of heart sounds or heart diseases. Regarding the method challenge, we explored a new method that combines a multi-features and deep learning method. For deep learning applications on temporal series, such as series of electroencephalogram (EEG), electrocardiogram (ECG) or Electromyography (EMG) signals, which have a low signal-to-noise (SNR), our proposed method may help with event detection or signal classification involving a combination of manually extracted features and deep learning algorithms. To some extent, the manual extraction of features is subjective. Combining automatically extracted features with deep learning algorithms, may make up the flaw of manually extracted features in some classification tasks. In addition, we introduced a GAP layer into the CNN model to improve the classifier's performance as reflected in Figure 4. The use of a GAP layer rather than a fully connected layer allows us to avoid overfitting when the number of PCG recordings is insufficient for a deep learning algorithm. On the class imbalance issue, which commonly arises in clinical practice, we adopted the strategy of setting class weights during the training process to solve the problems with class imbalance. The results shown in Figure 5 prove the effectiveness of this strategy.  
Besides, as the different metrics used in other research, it is confusion whether we should adopt the usually used accuracy (Acc) or the Macc as the final metric. In this study we compare the Acc and Macc in the same model as presented in Figure 6. We can get that--(1) when the specificity of the model is large, the Acc of the model is higher than the Macc and (2) the Macc is bigger than Acc when the sensitivity of the model is large. We believe Acc is not enough for the evaluation of the model, especially for the task of class imbalance. While, Macc is defined on the sensitivity and specificity, so it is suitable as an important metric.  
Although we have used the proposed method to achieve good performance on the heart sound dataset, there are some limitations to this research. The designed CNN was applied to manually extracted features in order to select features and classify normal and abnormal heart sounds. It has been the trend to perform classification or detection tasks that involve medical images or signals using end-to-end deep learning methods. However, in our experiments, the CNN model's performance was unsatisfactory when we used raw heart sounds as the input. On the other hand, the proposed method was able to classify normal and abnormal heart sounds, without the ability to recognize the categories of the abnormal heart sounds, which is crucial for the clinical diagnosis of related heart diseases. In further work, we will focus on the use of an end-to-end deep learning method for the classification of heart sounds and explore methods of mixing manual features and automatically extracted features in EEG signals for sleep staging.