Key result
A time-series-to-network approach combined with a simple neural MLP model successfully classified normal and abnormal ECG beats with good precision and recall scores.
Why the study?
Anomaly detection in biomedical time series has diagnostic implications, requiring algorithms with low computational complexity suitable for digital data acquisition systems and bedside monitoring.
Answers are grounded in this paper and cite their sources.
A novel time-series-to-network algorithm combined with a neural MLP model enables computationally efficient anomaly detection in ECG signals, showing promise for low-cost IoT bedside monitoring.
Anomaly detection in time series or any ordered data sequence is described using a multilayered network-based algorithm. An anomaly in the biomedical context has an obvious diagnostic implication. The first step in this time-series-to-network (T2N) approach is to partition the signal intensity in q user-defined intervals where, q ≪ N , N being the number of discrete sampling points of the time series. This maps the time series into a multigraph GM, the state transition between different signal partitions serving as directed edges E of the graph GM ( V , E ) , q being the cardinality of the vertex set V . Eventually, GM may have self-loops. Next, GM is split into a simple graph G = G ( V , H ) ,with edge-set H devoid of self-loops and three sets of parameters, namely, node strength index (NSI), interaction strength ratio (AISR), self-loop weights. The order of computational tasks reduces from O ( N ) to O ( 3 q ) with q ≪ N . Using such decomposition, we have performed beat classification of ECG data collected from MIT BIH repository. The network derived feature sets enables us to drastically compress the training set size. We next show how a simple neural MLP model with a single hidden layer with ∼ 100 neurons is able to learn to classify normal and abnormal single beats with good precision and recall scores. The results showcase the possibility of performing anomaly detection in bio-signals with an algorithm exhibiting low computational complexity, showing the promise of implementation on low cost IoT hardware devices integrated with modern digital data acquisition systems for bedside patient monitoring.
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K et al. (2021) studied ECG anomaly detection. Time-series-to-network (T2N) approach with neural MLP model was evaluated on Beat classification (normal vs abnormal single beats). A time-series-to-network approach combined with a simple neural MLP model successfully classified normal and abnormal ECG beats with good precision and recall scores.
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