Classification of respiratory disturbances in Rett Syndrome patients using Restricted Boltzmann Machine
| Autor: | Chi-Sang Poon, Mustafa Sahin, Juan Manuel Mayor, Walter E. Kaufmann, Heather M. O’Leary |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.medical_specialty
Modality (human–computer interaction) Apnea Methyl-CpG-Binding Protein 2 Central apnea Speech recognition Rett syndrome 02 engineering and technology medicine.disease Tachypnea Support vector machine 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Neurodevelopmental disorder Rett Syndrome 0202 electrical engineering electronic engineering information engineering medicine Humans 020201 artificial intelligence & image processing medicine.symptom F1 score Psychology 030217 neurology & neurosurgery |
| Zdroj: | EMBC |
| DOI: | 10.1109/embc.2017.8036857 |
| Popis: | Rett syndrome (RTT) is a severe neurodevelopmental disorder that can cause pervasive wakeful respiratory disturbances that include tachypnea, breath-holding, and central apnea. Quantitative analysis of these respiratory disturbances in RTT is considered a promising outcome measure for clinical trials. Currently, machine learning methodologies have not been employed to automate the classification of RTT respiratory disturbances. In this paper, we propose using temporal, flow, and autocorrelation features taken from the respiratory inductance plethsymography chest signal. We tested the performance of six classifiers including: Support Vector Machine, Restricted-Boltzmann-Machine, Back-propagation, Levenberg-Marquardt, and Decision-Fusion. We evaluate this classification in two modalities: (1) a subject-independent modality (leave-one-subject-out) obtaining the best F1 score in 93.67%, and (2) a trial-independent modality (leave-one-trial-out per subject) obtaining the best F1 score in 78.21%. |
| Databáze: | OpenAIRE |
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