With the COVID-19 pandemic, it has become necessary to monitor cardiac activities, not only for heart patients but for everyone. However, the traditional way to use heavy machines which are non-portable, intrusive, to check the electrocardiography (ECG) is not possible for everyone. As an alternative, there are sensors that can collect magnetocardiography (MCG) signals by measuring the magnetic field produced by the electrical currents in the heart and can be converted into ECG signals. The sensor for MCG is very sensitive, consume low power, portable, and can be a good alternative to check cardiac activities. But the challenging part of these sensors would be the noise at the low frequencies because the heart also oscillates at the low frequencies. As the relevant signal and noise share the same spectral properties, standard linear filtering techniques are not efficient. In this paper, we propose a physical reservoir computing technique using a circuit that can act as a reservoir and a lightweight machine learning model. The output is modeled to reduce the noise and extract the ECG signals out of the MCG ones.