A Novel Machine Learning Approach to Classify and Detect Atrial Fibrillation Using Optimized Implantable Electrocardiogram Sensor

There are some constraints such as external electrodes, a failure to capture most paroxysmal atrial fibrillation (AFib), low power transfer efficiency (PTE) for 24/7 charging technology, a short period of monitoring, and automatic detection of AFib in conventional electrocardiogram (ECG) sensors. To overcome these constraints, an implantable ECG sensor with a 2-coil inductive link with maximum power transfer efficiency (PTE) is designed to continuously monitor patients and efficiently detect AFib using global covering rule discovery and the minimum description length (MDL) algorithm. Among different combinations of ECG coils, the square spiral-square spiral coil demonstrates the maximum PTE, 56.23%, at the resonant frequency of 13.56 MHz and it is used in the implantable ECG sensor. The QRS complex from ECG signals of twenty-nine AFib patients is detected using different operation methods (DOM). The MDL algorithm is used to group 12 features of heart rate variability (HRV) parameters. The global covering rule discovery is proposed as a novel classification technique of AFib in ECG data. The average classification accuracy was 96.67 ± 7.03, and then the average recall, precision, F1-measures, and an average number of generated rules were 97.08 ± 6.23, 97.08 ± 6.23, 96.57 ± 7.23, and 7.9 ± 0.32, respectively. We found that the NN50, pNN50, and LF parameters can distinguish the AFib patient better than a healthy one. Among these parameters, pNN50 showed that it is greater than 34.75 in 41.38% of patients. The optimized implantable ECG sensor with a maximum PTE of 56.23% along with novel AFib detection and classification methods is suitable for its implementation in future implantable ECG sensors.