Abstract
Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a well-accepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively.
Highlights
Over the last few decades, life expectancy has increased considerably due to better quality in living standards and constant progress in medicine, among other causes [1]
We prove that measures obtained from symbolic recurrence quantification analysis (SRQA) are invariant under monotonic transformations, which means that the symbolic recurrence measures employed as covariates in our algorithm are not affected in those cases in which the calibration processes are based on linear regression
We have described the methodology for designing a prototype, but fully functional application, which meets the following requirements: (i) it is robust against low-quality data, enabling the use of multiple low-cost wrist-worn sensors generally characterized by low accuracy in comparison to professional electrocardiographs; and (ii) it is effective for analyzing dynamic changes based on a time series of RR intervals
Summary
Over the last few decades, life expectancy has increased considerably due to better quality in living standards and constant progress in medicine, among other causes [1]. The authors of Reference [9] proposed a multi-sensor data fusion scenario in order to improve the quality of heart disease detection They used a kernel random forest ensemble with several time and frequency domain features for their classification method. It is worth mentioning that several recent clinical trials on large populations focusing on AF detection have been carried out [17,18,19] In those clinical studies ECG data were obtained with portable sensors achieving outstanding results. Our main contributions are: (i) the integration in an application of our computing time efficient algorithm; (ii) the fact that our algorithm only employs RR intervals as input so it can virtually operate with any low-cost sensor; and (iii) predictive power accuracy not being affected by a data calibration procedure.
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