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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State-of-the-art approaches for the previous emotion recognition in the wild challenges are usually built on prevailing Convolutional Neural Networks (CNNs). Although there is clear evidence that CNNs with increased depth or width can usually bring improved predication accuracy, existing top approaches provide supervision only at the output feature layer, resulting in the insufficient training of deep CNN models. In this paper, we present a new learning method named Supervised Scoring Ensemble (SSE) for advancing this challenge with deep CNNs. We first extend the idea of recent deep supervision to deal with emotion recognition problem. Benefiting from adding supervision not only to deep layers but also to intermediate layers and shallow layers, the training of deep CNNs can be well eased. Second, we present a new fusion structure in which class-wise scoring activations at diverse complementary feature layers are concatenated and further used as the inputs for second-level supervision, acting as a deep feature ensemble within a single CNN architecture. We show our proposed learning method brings large accuracy gains over diverse backbone networks consistently. On this year's audio-video based emotion recognition task, the average recognition rate of our best submission is 60.34 , forming a new envelop over all existing records.