# A Novel 1-D CCANet for ECG Classification
## 1 Introduction and Related Work  
Cardiovascular diseases are the first cause of death in the world, with an estimated 17.9 million deaths each year. Among them, heart arrhythmia qualifies as an abnormal heart rhythm that can result in serious complications such as stroke or cardiac deaths. Early detection of arrhythmia is a major challenge for our society. With electrocardiograms (ECGs), heartbeats can be visually labelled according to several classes such as Normal beat, Supraventricular escape beat, etc. An ECG is a graph of voltage versus time of the electrical activity of the heart using electrodes placed on the skin. To assess the condition of the heart from different angles, an ECG has several leads, each of them being the signal generated by a pair of electrodes. In the last decades, researchers employed machine learning methods for the automatic classification of heartbeats contained in long-duration recordings of human ECGs [1, 2]. A traditional heartbeat classification pipeline includes data preprocessing, data segmentation, feature extraction, feature selection, and classification [3]. Data preprocessing is used to remove noise from the ECG raw signal. The most used techniques are median filters [4], discrete wavelet transform (DWT) [5, 6], adaptive filters [4, 7], and frequency selective filters [8, 9, 10]. Data segmentation is used to isolate heartbeats from the whole ECG recording. Once a time segment including the heartbeat is available, time domain [11, 12, 13, 14, 15, 16] or frequency domain [13, 16, 17, 18] or morphological [11, 12, 13, 15, 16] or statistical [13, 19] or neural features [20] are extracted. Feature selection is used to reduce the number of features used by the classifier thus reducing the complexity and time required for computation. Several approaches have been adopted: principal and independent component analysis [5, 6, 21, 22], linear discriminant analysis [6], and genetic algorithm [23]. Random forest [24, 25], support vector machines (SVMs) [13, 14, 15, 16, 18, 19], neural networks (NNs) [5, 6] or deep neural networks (DNNs) [2, 26, 27, 28, 29, 30, 31, 32] are employed to classify extracted features in one of the heartbeat classes. As discussed above, ECGs can be recorded in different locations of the body thus obtaining the so-called multilead ECGs. Up to 12 leads can be recorded and each lead represents a specific characteristic of the heart. Multilead ECGs better reflect the state of the heart compared with single lead ECGs. Taking into account multi leads may bring performance improvement. Existing literature is mainly focused on the processing of single lead ECGs [20]. In this paper, we focus on two-lead ECGs: we use lead V1, that is a chest lead, and lead II, that is a limb lead. We propose the combination of hand-crafted features with a canonical correlation analysis network (CCANet) and SVMs for two-lead heartbeats classification. The analysis of the correlation between two leads of the ECG is exploited to increase heartbeat classification performance [20]. Proposed CCANet is a 1-D variant of the original 2-D CCANet proposed by [PERSON] et al. [20] that allows to explore a deeper CCANet while maintaining a reasonable computational complexity and providing better results. CCANet has been originally proposed by [PERSON] et al. [33] for the processing of two-view images in 2017. Compared to one-view image-based PCANet and RandNet, CCANet demonstrated to perform better [33]. CCANet has also been employed in other computer vision tasks such as remote sensing scene classification [34] as well as ECG interpretation [20]. There are two types of CNNs that are commonly used for ECG classification: the 1-D CNN and 2-D CNN [35]. 2-D CNNs usually operate on transformed ECG data, such as spectrograms, gray-level co-occurrence matrices, combined features and others. 1-D CNNs operate directly on the raw ECG signal.