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Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic Signals

1
INRS-Énergie matériaux et télécommunications, Montreal, QC H5A 1K6, Canada
2
Centre de recherche LICEF, TELUQ university, Montreal, QC H2S 3L5, Canada
3
Laboratoire de recherche en imagerie et orthopédie, Centre de recherche du CHUM, Montreal, QC H2X 0A9, Canada
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2019, 1(3), 768-784; https://doi.org/10.3390/make1030045
Received: 22 March 2019 / Revised: 25 June 2019 / Accepted: 26 June 2019 / Published: 5 July 2019
(This article belongs to the Section Learning)
The analysis of knee kinematic data, which come in the form of a small sample of discrete curves that describe repeated measurements of the temporal variation of each of the knee three fundamental angles of rotation during a subject walking cycle, can inform knee pathology classification because, in general, different pathologies have different kinematic data patterns. However, high data dimensionality and the scarcity of reference data, which characterize this type of application, challenge classification and make it prone to error, a problem Duda and Hart refer to as the curse of dimensionality. The purpose of this study is to investigate a sample-based classifier which evaluates data proximity by the two-sample Hotelling T 2 statistic. This classifier uses the whole sample of an individual’s measurements for a better support to classification, and the Hotelling T 2 hypothesis testing made applicable by dimensionality reduction. This method was able to discriminate between femero-rotulian (FR) and femero-tibial (FT) knee osteoarthritis kinematic data with an accuracy of 88.1 % , outperforming significantly current state-of-the-art methods which addressed similar problems. Extended to the much harder three-class problem involving pathology categories FR and FT, as well as category FR-FT which represents the incidence of both diseases FR and FT in a same individual, the scheme was able to reach a performance that justifies its further use and investigation in this and other similar applications. View Full-Text
Keywords: pattern classification; hotelling statistic; kinematic signals; knee osteoarthritis pattern classification; hotelling statistic; kinematic signals; knee osteoarthritis
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Ben Nouma, B.; Mitiche, A.; Ouakrim, Y.; Mezghani, N. Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic Signals. Mach. Learn. Knowl. Extr. 2019, 1, 768-784.

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