A multi-layer perceptron trained on short-time Fourier transform sparse signatures of ECG heartbeats is reported to reach 95.7% average classification accuracy on MIT-BIH, but the evaluation uses a beat-level split with patient leakage.
Encoding Data for HTM Systems
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abstract
Hierarchical Temporal Memory (HTM) is a biologically inspired machine intelligence technology that mimics the architecture and processes of the neocortex. In this white paper we describe how to encode data as Sparse Distributed Representations (SDRs) for use in HTM systems. We explain several existing encoders, which are available through the open source project called NuPIC, and we discuss requirements for creating encoders for new types of data.
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Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG
A multi-layer perceptron trained on short-time Fourier transform sparse signatures of ECG heartbeats is reported to reach 95.7% average classification accuracy on MIT-BIH, but the evaluation uses a beat-level split with patient leakage.