The work presented here applies deep learning to the task of automated cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We describe an automated heart sound classification algorithm that combines the use of time-frequency heat map representations with a deep convolutional neural network (CNN). Given the cost-sensitive nature of misclassification, our CNN architecture is trained using a modified loss function that directly optimizes the trade-off between sensitivity and specificity. We evaluated our algorithm at the 2016 PhysioNet Computing in Cardiology challenge where the objective was to accurately classify normal and abnormal heart sounds from single, short, potentially noisy recordings. Our entry to the challenge achieved a final specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved the greatest specificity score out of all challenge entries and, using just a single CNN, our algorithm differed in overall score by only 0.02 compared to the top place finisher, which used an ensemble approach.
The work presented in this paper, is one of the first attempts at applying to the task of heart sound data analysis. However, there have been recent efforts to apply deep learning approaches to other types of physiological time series analysis tasks. An early work that applied deep learning to the domain of psychophysiology is described in @cite_28 . They advocate the use of for recognizing affect from physiological inputs such as and within a game-based user study. The authors argue against the use of manual ad-hoc feature extraction and selection in affective modeling, as this limits the creativity of attribute design to the researcher. One difference between the work of @cite_28 and ours is that they perform an initial unsupervised pre-training step using stacked convolutional denoising auto-encoders, whereas our network does not require this step and is instead trained in a supervised fashion .
@cite_28
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Clustering is one of the most fundamental tasks in data analysis and machine learning. It is central to many data-driven applications that aim to separate the data into groups with similar patterns. Moreover, clustering is a complex procedure that is affected significantly by the choice of the data representation method. Recent research has demonstrated encouraging clustering results by learning effectively these representations. In most of these works a deep auto-encoder is initially pre-trained to minimize a reconstruction loss, and then jointly optimized with clustering centroids in order to improve the clustering objective. Those works focus mainly on the clustering phase of the procedure, while not utilizing the potential benefit out of the initial phase. In this paper we propose to optimize an auto-encoder with respect to a discriminative pairwise loss function during the auto-encoder pre-training phase. We demonstrate the high accuracy obtained by the proposed method as well as its rapid convergence (e.g. reaching above 92 accuracy on MNIST during the pre-training phase, in less than 50 epochs), even with small networks.