Topological Analysis and Gaussian Decision Tree: Effective Representation and Classification of Biosignals of Small Sample Size.
IEEE Trans Biomed Eng
; 64(9): 2288-2299, 2017 09.
Article
em En
| MEDLINE
| ID: mdl-28113294
ABSTRACT
GOAL Bucking the trend of big data, in microdevice engineering, small sample size is common, especially when the device is still at the proof-of-concept stage. The small sample size, small interclass variation, and large intraclass variation, have brought biosignal analysis new challenges. Novel representation and classification approaches need to be developed to effectively recognize targets of interests with the absence of a large training set. METHODS:
Moving away from the traditional signal analysis in the spatiotemporal domain, we exploit the biosignal representation in the topological domain that would reveal the intrinsic structure of point clouds generated from the biosignal. Additionally, we propose a Gaussian-based decision tree (GDT), which can efficiently classify the biosignals even when the sample size is extremely small.RESULTS:
This study is motivated by the application of mastitis detection using low-voltage alternating current electrokinetics (ACEK) where five categories of bisignals need to be recognized with only two samples in each class. Experimental results demonstrate the robustness of the topological features as well as the advantage of GDT over some conventional classifiers in handling small dataset.CONCLUSION:
Our method reduces the voltage of ACEK to a safe level and still yields high-fidelity results with a short assay time.SIGNIFICANCE:
This paper makes two distinctive contributions to the field of biosignal analysis, including performing signal processing in the topological domain and handling extremely small dataset. Currently, there have been no related works that can efficiently tackle the dilemma between avoiding electrochemical reaction and accelerating assay process using ACEK.
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Interpretação Estatística de Dados
/
Artefatos
/
Condutometria
/
Leite
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Análise de Alimentos
Tipo de estudo:
Diagnostic_studies
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Health_economic_evaluation
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Prognostic_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article