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Electrocardiogram (ECG) is the graphical representation of electrical activity of the heart and is used to detect certain structural and functional heart conditions. Segmenting ECG waveforms and annotating constituent components is required for analysis of ECG and to arrive at a diagnosis. This paper proposes a Convolutional Long Short-Term Memory (ConvLSTM) neural network to segment the ECG waves. It consists of a convolutional layer followed by a Bidirectional LSTM architecture. The segmentation is achieved by adding additional features such as derivative of the ECG wave as well as the smoothened ECG wave and the model outperforms traditional Markov models.

Recommendation systems (RS) play an important role in e-commerce applications as they help the consumers in choosing the required items within reduced time. The traditional methods of collaborative filtering, fail to capture the visual data associated with the items. Visually-aware recommendation systems are upcoming in e-commerce applications that use the visual features of the products rather than the user profiles. Deep learning techniques are used for the classification and prediction in visual recommendation systems. However, the criticality of the visual recommendation system lies in identifying the similar images for a given target image. In this paper, a visual recommendation system is proposed based on Deep Visual Ensemble similarity metric (DVESM) using Convolutional Autoencoder (CAE) neural network for classification. The basic idea is to get a set of trained feature vectors for the image catalogue using CAE and find the similarity between the trained feature vectors and the target feature vector using DVESM method. The proposed methodology of using DVESM has been demonstrated with the state-of-the-art methods on Amazon 2014, 2015 and Street2Shop datasets. The results show that the DVESM method is most suitable for visually aware recommendation systems as it learns an ensemble of metrics to provide recommendations. [ABSTRACT FROM AUTHOR]