Probabilistic gait classification in children with cerebral palsy: A Bayesian approach

Gestel, Leen Van; Laet, Tinne De; Lello, Enrico Di; Bruyninckx, Herman; Molenaers, Guy; Campenhout, Anja Van; Aertbeliën, Erwin; Schwartz, Michael H.; Wambacq, Hans; Cock, Paul De; Desloovere, Kaat

doi:10.1016/j.ridd.2011.07.004

Cited by 39 publications

(38 citation statements)

References 27 publications

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“…Based on these results, suggestions to improve current pattern definitions were made. The results further suggest that algorithms, which could automate this classification [13], are likely to be successful. Future research should establish to what extent the patterns are responsive to treatment and how they could be incorporated in the clinical reasoning process.…”

Section: Resultsmentioning

confidence: 90%

“…A confirmation of this second hypothesis provides evidence for the feasibility of developing algorithms for automatic classification (e.g. Bayesian networks [13], [15]) and for the classes and classification rules of the consensus study [11]. In light of this, the third hypothesis stated that the pathological patterns at the level of each joint differ from each other during at least one phase of the gait cycle.…”

Section: Introductionmentioning

confidence: 86%

“…However, the clinical interpretation of kinematic and kinetic gait data is subjective and therefore less reliable [5]. To support the clinical understanding of gait data, many attempts have been made to recognize different gait patterns from kinematic and kinetic reports, using either qualitative or quantitative approaches [6–13]. Regarding quantitative approaches, complex clinical interpretation of the patterns hinders the applicability of the classifications in medical practice.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Parametric Mapping to Identify Differences between Consensus-Based Joint Patterns during Gait in Children with Cerebral Palsy

et al. 2017

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Experts recently identified 49 joint motion patterns in children with cerebral palsy during a Delphi consensus study. Pattern definitions were therefore the result of subjective expert opinion. The present study aims to provide objective, quantitative data supporting the identification of these consensus-based patterns. To do so, statistical parametric mapping was used to compare the mean kinematic waveforms of 154 trials of typically developing children (n = 56) to the mean kinematic waveforms of 1719 trials of children with cerebral palsy (n = 356), which were classified following the classification rules of the Delphi study. Three hypotheses stated that: (a) joint motion patterns with ‘no or minor gait deviations’ (n = 11 patterns) do not differ significantly from the gait pattern of typically developing children; (b) all other pathological joint motion patterns (n = 38 patterns) differ from typically developing gait and the locations of difference within the gait cycle, highlighted by statistical parametric mapping, concur with the consensus-based classification rules. (c) all joint motion patterns at the level of each joint (n = 49 patterns) differ from each other during at least one phase of the gait cycle. Results showed that: (a) ten patterns with ‘no or minor gait deviations’ differed somewhat unexpectedly from typically developing gait, but these differences were generally small (≤3°); (b) all other joint motion patterns (n = 38) differed from typically developing gait and the significant locations within the gait cycle that were indicated by the statistical analyses, coincided well with the classification rules; (c) joint motion patterns at the level of each joint significantly differed from each other, apart from two sagittal plane pelvic patterns. In addition to these results, for several joints, statistical analyses indicated other significant areas during the gait cycle that were not included in the pattern definitions of the consensus study. Based on these findings, suggestions to improve pattern definitions were made.

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Section: Resultsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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Statistical Parametric Mapping to Identify Differences between Consensus-Based Joint Patterns during Gait in Children with Cerebral Palsy

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“…Quantitative approaches use machine learning techniques to pre-process and classify 3D gait data. Qualitative approaches optimally rely on expert knowledge but are limited by their subjective nature and inconsistency [2,7,8]. Quantitative approaches on the other hand are objective and powerful when it comes to analysing complex data, however, obtaining clinically relevant results and incorporating expert knowledge at the same time is often challenging [8].…”

Section: Introductionmentioning

confidence: 99%

Does expert knowledge improve automatic probabilistic classification of gait joint motion patterns in children with cerebral palsy?

et al. 2017

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BackgroundThis study aimed to improve the automatic probabilistic classification of joint motion gait patterns in children with cerebral palsy by using the expert knowledge available via a recently developed Delphi-consensus study. To this end, this study applied both Naïve Bayes and Logistic Regression classification with varying degrees of usage of the expert knowledge (expert-defined and discretized features). A database of 356 patients and 1719 gait trials was used to validate the classification performance of eleven joint motions.HypothesesTwo main hypotheses stated that: (1) Joint motion patterns in children with CP, obtained through a Delphi-consensus study, can be automatically classified following a probabilistic approach, with an accuracy similar to clinical expert classification, and (2) The inclusion of clinical expert knowledge in the selection of relevant gait features and the discretization of continuous features increases the performance of automatic probabilistic joint motion classification.FindingsThis study provided objective evidence supporting the first hypothesis. Automatic probabilistic gait classification using the expert knowledge available from the Delphi-consensus study resulted in accuracy (91%) similar to that obtained with two expert raters (90%), and higher accuracy than that obtained with non-expert raters (78%). Regarding the second hypothesis, this study demonstrated that the use of more advanced machine learning techniques such as automatic feature selection and discretization instead of expert-defined and discretized features can result in slightly higher joint motion classification performance. However, the increase in performance is limited and does not outweigh the additional computational cost and the higher risk of loss of clinical interpretability, which threatens the clinical acceptance and applicability.

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“…In general, kinematic data, kinetic data and the data related to EMG signal are applied to the differential diagnosis in the methods presented. In addition, numerous quantitative and qualitative methods are studied by researchers in concern with the analysis and classification of the gate as neural networks, Bayesian, fuzzy logic systems and classifiers such as SVM [6,7,8,9]. Some of these methods such as SVM are linear classifier while the dynamic changes of the parameters of the gate is derived from nonlinear processes [7].…”

Section: Introductionmentioning

confidence: 99%

Discrimination between the patients with CIPD and Charcot-Marie-Tooth utilizing the fuzzy logic system based classifiers

Aminian

Kobravi

Boostani

2013

2013 13th Iranian Conference on Fuzzy Systems (IFSC)

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In this paper, a diagnostic approach has been presented for discriminating between patients with acquired neuropathy called CIDP and congenital neuropathy called Charcot-Marie-Tooth. In the presented approach, nine timedomain and frequency-domain features extracted from the surface EMG signals of tibialis anterior and rectus femoris muscles recorded during walking in patients.All data is the result of 82 human examinations done on the three groups of healthy individuals (34 samples), CIDP patients (25 samples) and Charcot-Marie-Tooth patients (23 samples).70% of the gathered data was used for training and the rest 30% for testing the classifiers. Then, fuzzy KNN classifiers and Adaptive neuro fuzzy inference system(ANFIS) were utilized to detect the discrepancies between two groups of patients. The performance of fuzzy KNN and Anfis were compared. The results show that the accuracies were 98.4% and 96.8% for fuzzy KNN and Anfis classifiers ,respectively.

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Probabilistic gait classification in children with cerebral palsy: A Bayesian approach

Cited by 39 publications

References 27 publications

Statistical Parametric Mapping to Identify Differences between Consensus-Based Joint Patterns during Gait in Children with Cerebral Palsy

Statistical Parametric Mapping to Identify Differences between Consensus-Based Joint Patterns during Gait in Children with Cerebral Palsy

Does expert knowledge improve automatic probabilistic classification of gait joint motion patterns in children with cerebral palsy?

Discrimination between the patients with CIPD and Charcot-Marie-Tooth utilizing the fuzzy logic system based classifiers

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