Recursive versus sequential multiple error measures reduction: A curve simplification approach to ECG data compression

Previous time domain compression methods have been tackled by sequential one point at a time sub-optimal selection strategies running in ∼O(N) or all points at a time optimal strategies running in ∼O(N3) temporal complexities. Yet basically, the selected dominant points (DPs) are locally only significant in these methods, which may lead to inaccurate reconstruction or even loss of clinical data. Alternatively, the recursive one point at a time selection strategy, through different variants of the Douglas–Peucker line simplification algorithm, computes globally significant DPs for an ∼O(N·log2(N)) temporal complexity. We illustrate that the recursive strategy performs numerically almost two times better than the sequential one. The piecewise linear approximation of the input ECG is formally expressed as a curve simplification problem, through reduction of the Hausdorff error measure. We also illustrate that reduction of two error measures performs better than reduction of one error measure. An additional compression option is proposed in the case of the recursive strategy through simplification of the sorted distances associated to the selected set of points. The outcome is a compression algorithm that yields compression ratios ranging from 8:1 to 22:1 for a perceptually good reconstruction quality and near linear execution time. The tests have been conducted on the MIT-BIH public ECG database. Results show that the proposed recursive algorithm is an excellent compromise on compression ratio – computational time – reconstruction quality.