Abstract
In the current digital era, cardiovascular activity analysis is becoming ubiquitous with the increasing availability of embedded systems, and, in some cases, these systems are even endowed with the capabilities of detecting health risk factors identified through electrocardiographic (ECG) markers. Aligned with this trend, and inspired by the results from our pilot study, we focus this research on exploring the potential of a low-cost and open source device for heart rhythm analysis, with emphasis on Atrial Fibrillation (AF). AF is the most common cardiac arrhythmia, defined as a complex cardiac disease which is highly correlated to stroke and heart failure, and its prevalence is especially high in elderly population. Given the importance and impact of such cardiovascular disease, we performed ECG acquisitions in a hospital setting to evaluate the potential of this device to identify heart rhythms. These ECG acquisitions were performed in 10 patients, accomplished simultaneously with the low-cost device and the gold standard devices used in the clinical routine of the hospital. The data collected from the low-cost device was analysed by the cardiologist specialized in rhythmology, along with the conventional ECG exam analysis, whom suggested 100% accuracy of the low-cost device in differentiating a sinus rhythm from AF. The results also present great accuracy in the detection of atypical cardiac events through ECG analysis, as well as a good agreement for the corroboration of the numerical data from the devices used for this study, as the RR intervals and QTc intervals values suggest.
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Acknowledgements
Our team would like to thank to all the anonymous volunteers that agreed to collaborate with this study and also to Hospital da Senhora da Oliveira Guimarães, namely, the Cardiology Department that supported with the data acquisition process.
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Lourenço, H.B.M. et al. (2019). Heart Rhythm Qualitative Analysis Using Low-Cost and Open Source Electrocardiography: A Study Based on Atrial Fibrillation Detection. In: Holzinger, A., Pope, A., Plácido da Silva, H. (eds) Physiological Computing Systems. PhyCS PhyCS PhyCS 2016 2017 2018. Lecture Notes in Computer Science(), vol 10057. Springer, Cham. https://doi.org/10.1007/978-3-030-27950-9_11
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