{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T15:20:18Z","timestamp":1767972018536,"version":"3.49.0"},"reference-count":25,"publisher":"World Scientific Pub Co Pte Lt","issue":"01n02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adv. Adapt. Data Anal."],"published-print":{"date-parts":[[2011,4]]},"abstract":"<jats:p> Sample entropy is a widely used tool for quantifying complexity of a biological system. Computing sample entropy directly using its definition requires large computational costs. We propose a fast algorithm based on a k-d tree data structure for computing sample entropy. We prove that the time complexity of the proposed algorithm is [Formula: see text] and its space complexity is O(N log N), where N is the length of the input time series and m is the length of its pattern templates. We present a numerical experiment that demonstrates significant improvement of the proposed algorithm in computing time. <\/jats:p>","DOI":"10.1142\/s1793536911000775","type":"journal-article","created":{"date-parts":[[2011,9,8]],"date-time":"2011-09-08T09:53:56Z","timestamp":1315475636000},"page":"167-186","source":"Crossref","is-referenced-by-count":35,"title":["A FAST ALGORITHM FOR COMPUTING SAMPLE ENTROPY"],"prefix":"10.1142","volume":"03","author":[{"given":"YING","family":"JIANG","sequence":"first","affiliation":[{"name":"Guangdong Province Key Lab of Computational Science, Sun Yat-Sen University, Guangzhou 510275, P. R. 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