Pragmatic analysis of ECG classification models and architectures from a statistical perspective

Electrocardiograms, also known as ECGs, are capable of representing a wide range of heart conditions, such as atrial fibrillation, arrhythmia, myopathy, and heart failure. Operations such as denoising, signal enhancement, feature extraction, feature selection, stratification, and post processing are developed in order to process ECG signals. Depending on the application that is being deployed, these processes may only work in their individual components or in combination with one another. Researchers over the years have developed a wide variety of algorithms, each specifically tailored to accomplish one of these tasks. The computational complexity of these algorithms, the number of diseases they can classify, the degree of accuracy they provide, the cost of deployment, and other factors can vary. For example, models that are based on convolutional neural networks (CNNs) have a high level of accuracy; however, these models are typically slow and highly complex to deploy, and as a result, they are not used in clinical applications that have a low cost. However, linear classification models such as support vector machines (SVMs) have acceptable accuracy, a moderate level of complexity, but are lacking in terms of precision; as a result, they are not suitable for use in applications that require real-time processing. Because there is such a wide range of algorithms that are available, it is unclear to researchers which models would be the best fit for their application, which leads to an increase in both the cost of deployment and the amount of time needed to bring the product to market. The text that lies beneath this one examines several of the recently proposed ECG classification models and assesses them based on a number of statistical parameters, including computational delay, complexity of deployment, classification accuracy, precision, number of heart diseases covered, and so on. This is done in an effort to clarify the situation and remove some of the ambiguity. After completing this analysis, the text then compares their performance and provides an estimate of the models that are the most appropriate. Researchers now have the ability to select and implement the algorithmic models that are best suited for their deployments based on this review. In addition to that, this reviwe suggests a number of different enhancements that can be made to the models that have been examined, and researchers can implement these suggestions in order to increase the effectiveness of the models.