– Reinforcement Learning Personal Authentication System Using ECG Feature
Electrocardiogram (ECG) data changes daily basis as the measuring point and environment changes. Since ECG data has unique characteristics for individual, we measured and test the dataset for personal authentication.
After the measuring process, we reduced its noise by using Finite Impulse Response(FIR) filter.
Additionally, we extracted the 31 features such as amplitude, interval, slope, and angle etc. Then those 31 features are entered into reinforcement learning network and received the high-cost features for its outcome.
Now, using the high-cost features and previous 31 features are plugged into Support Vector Machine (SVM) and Random Forest (RF) to get the final output of the following features: amplitude, interval, and angles. As a result, accuracy of combined feature classification are varied. However, the obtained features by reinforcement learning is considerably high.