A Physical Reservoir Computing Processor for ECG-to-PCG Signals Prediction

An electrocardiogram (ECG) is a medical test that records the electrical activity of the heart over a period of time. This non-invasive and painless test makes ECG an essential tool in cardiology for diagnosing and monitoring heart health. Phonocardiogram (PCG) are typically used as an adjunct to the expertise of healthcare professionals rather than as a standalone diagnostic tool. By applying ECG signals to predict the corresponding PCG signals, no additional test for PCG is needed. These predictions can assist doctors in making informed decisions about patient care and treatment plans by merely collecting ECG signals. Physical reservoir computing (RC), as a bio-inspired algorithm, has received growing research interest. Under the term neuromorphic, physical RC processes the raw signals in the analogue domain with in-memory computing, thus reducing massive power consumption. At the same time, RC is a special type of Recurrent Neural Network (RNN) that is suitable for time-dependent signal processing. The ECG/PCG dataset we used here was experimentally collected by 10 people. We applied a recently proposed physical RC architecture called Rotating Neuron Reservoir and achieved an average NRMSE of 0.3690 for prediction, aiming to propose and construct a neuromorphic processing core for the ECG-to-PCG prediction task.