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Classification of one-dimensional (1D) data is important for a variety of complex problems. From the finance industry to audio processing to the medical field, there are many industries that utilize 1D data. Machine learning techniques have excelled at solving these classification problems, but there is still room for improvement because the techniques have not been perfected. This paper proposes a novel architecture called Multi-Head Augmented Temporal Transformer (MHATT) for 1D classification of time-series data. Highly modified vision transformers were used to improve performance while keeping the network exceptionally efficient. To showcase its efficacy, the network is applied to heartbeat classification using the MIT-BIH OSCAR dataset. This dataset was ethically-split to ensure a fair and intensive test for networks. The novel architecture is 94.6% more efficient and had a peak accuracy of 91.79%, which was a 13.6% reduction in error over a recent state-of-the-art network. The impressive performance and efficiency of the MHATT architecture can be exploited by edge devices for unmatched performance and flexibility of deployment.

Ethical data splitting is of paramount importance to ensure the validity of any solution that is based on data. If data is biased, it will not accurately represent how the solution will solve the problem. To ethically split data, the overall variance of the data needs to be fairly represented in the training and the testing sets of the dataset. To do this, the outliers of the data need to be determined so that they can be accounted for when splitting the data. Finding the principal components of the data using the L 2 -norm has been shown as an effective way to identify outliers of data to make a robust dataset that is resistant to outliers. It has been shown that the L 1 -norm is more resistant to outliers than the L 2 -norm, so it will allow the dataset to become more resistant to outliers. Therefore, utilizing L 1 -norm principal components when determining ethical data splits will result in more robust datasets.