(Sunday, 7/29/2018) 4:30 PM - 5:00 PM
Room: Exhibit Hall | Forum 9
Purpose: The study developed a mobile application that can assist in the diagnosis of cardiovascular diseases and provide feedback on the wellness index.
Methods: Smart Outdoor Shirts are manufactured using elastic fabric such as spandex and polyester. 3 D image data are obtained for the typical body types in the 40 s–60 s age group to maintain a snug fit; these data are used for ECG measurements. The snug fit is coupled with inelastic and circular patterns that are comfortable to wearers. we used lasers to produce a packaging prototype. In the system, a medical data mining engine is modeled using a machine learning algorithm of the ECG features of HRV. The CAD system integrates the mining engine; if the new ECG signal is monitored, the CAD system detects an abnormal signal and extracts HRV features. Finally, the knowledge model of the medical data mining engine determines and classifies the signal as arrhythmic or normal.
Results: ECG measurements were performed using the sensor in real time while the shirt was worn. Expert review indicated that signal accuracy was 97.5 ± 1% in a state of immobility and 85.2 ± 2% during movement over a distance of approximately 10 m. ECG detection was performed using long-term real-time ECG data from the bioshirt and the signal processing algorithm developed in this study. we found that the accuracy of the algorithm was 98.2 ± 2% for the identification of arrhythmia. The system successfully differentiates AF from NSR. The algorithm automatically annotates N as NSR and AF as atrial fibrillation into the monitoring signal.
Conclusion: The study developed a mobile application that can assist in the diagnosis of cardiovascular diseases and provide feedback on the wellness index.
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