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Fixed complexity tiny reservoir heterogeneous network for on-line ECG learning of anomalies

The electrocardiogram (ECG), being one of the most extensively used signals to monitor cardiovascular diseases (CVDs), captures the heart’s arrhythmias. Patients with such pathology are often monitored for extended periods of time, requiring data storage, and a very time-consuming off-line search of anomalies. This is especially inefficient when indicative patterns in the biological signals are infrequent, requiring more analysis time of medical doctors, and entailing a difficult visual search task for the diagnosis.In this paper, we propose an automated deep learning pipeline based on reservoir computing (RC), followed by principal component analysis (PCA) and one-class support vector machine (OC-SVM), that can be used to perform on-line learning and real-time anomaly detection of pathological conditions and therefore to raise warnings. The learning step requires fixed computational complexity and memory that are affordable on a low-power microcontroller unit (MCU). During the learning phase, it uses a very limited amount of input data, e.g., 60 KB, which makes this work suitable for fast personalization when device restart is required. The detection accuracy has been evaluated on the publicly available MIT-BIH arrhythmia dataset, also modified to mimic temporal sliding of input tensors on the ECG streaming data. Best F1 score and accuracy are 91.5%, 95.4% with variance over the processed data of 0.001.