Abstract
A Chronic Kidney Disease (CKD) monitoring system was proposed for early detection of cardiovascular disease (CVD) and anemia using Fuzzy Logic. To determine the heart rate and blood oxygen saturation, the proposed model was simulated using MATLAB and Simulink to handle ECG and PPG inputs. The Pan-Tompkins method was used to determine the heart rate, while the Takuo Aoyagi algorithm was used to assess blood oxygen saturation levels. The findings show that the ECG recorded using the CKD model has all of the characteristics of a typical ECG wave cycle, but with reduced signal degradation in the 0.8–1.3mV region. The heart rate signal processing yielded findings between 78 and 83 beats per minute is within the range of the supplied heart rate. Takuo Aoyagi's pulse oximeter simulation generated the same findings. For real-time verification, the proposed model was implemented in hardware using ESP8266 32-bit microcontroller with IoT integration via Wireless Fidelity for data storage and monitoring. In comparison with the Fuzzy Logic simulation done on MATLAB and Simulink, the CKD monitoring device has 100% accuracy in patient status detection. The CKD monitoring system has an overall accuracy of 99% in comparison with a commercial fingertip pulse oximeter.
Highlights
Healthcare monitoring systems or e-Health systems are devices that use a wireless sensor network (WSN) to observe severe or chronic diseases in humans [1] In this era, there are many smart watches out in the market that claim to track the condition of the body accurately
Confirming that the Pan-Tompkins algorithm used to estimate the heart rate (HR) in this simulation is acceptable
A model of a simulation was designed to imitate the functionality of a low-cost monitoring system for early detection of cardiovascular disease (CVD) and anemia for Chronic Kidney Disease (CKD) patients
Summary
Healthcare monitoring systems or e-Health systems are devices that use a wireless sensor network (WSN) to observe severe or chronic diseases in humans [1] In this era, there are many smart watches out in the market that claim to track the condition of the body accurately. These smartwatches could not be used to diagnose a medical condition. Healthcare monitoring systems serve an important role in monitoring patients' vital signs and early detection of prevailing diseases. The suggested method is implemented in hardware using ESP8266 microcontroller for real-time verification
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