Proposal of a Context-sensitive ECG Collection Mobile Health System for Ambulatory Cardiovascular Diseases

Abstract

Background: This study brings innovation oriented to the development of e-health services to improve the performance of healthcare and hospital care in free-living activities. Objective: This article seeks to propose an innovative mobile software that enables the contextualization of clinical data, while integrating the fundamental functionalities and usability criteria of a mobile health (mhealth) system. Methods: We examine the utility of a context-sensitive mobile electrocardiogram (ECG) collection system for the detection of cardiovascular diseases for patients in free-living conditions. Results: We propose an mhealth system that reinforces conventional healthcare systems in which electronic health records are limited by access and processing of data, limiting clinical–medical decision-making. This study highlights the importance of mobile applications for self-care and their usefulness for both patients and healthcare providers. The proposal offers an avenue for the study of contextualized ECG collection methods that can be used to create context-specific actions that reduce false alarms in computer-aided diagnosis of ambulatory ECG. Conclusion: Mobile applications for health care and monitoring improve the quality of life of people with cardiovascular diseases.

The Impact of Electronic Health Records on Patient Care in the US Healthcare System

“…Predictive models are constructed using a variety of machine learning methods, including supported vector machines, decision trees, and neural networks. Model performance is optimized by hyperparameter adjustment, and generalization is evaluated using cross-validation [19]. Evaluation metrics measure the efficacy of the model; these include precision-recall and accuracy.…”