Filter Method Ensemble with Neural Networks

Chakraborty, Anuran; De, Rajonya; Chatterjee, Agneet; Schwenker, Friedhelm; Sarkar, Ram

doi:10.1007/978-3-030-30484-3_59

Cited by 3 publications

(1 citation statement)

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“…Also, Glodek et al [ 30 ] have worked on ensemble approaches for density estimation using Gaussian Mixture Models (GMMs) by combining individual mixture models incorporating a high diversity to create a more stable and accurate model. Chakraborty et al [ 31 ] have performed ensemble of filter methods, such as optimal subsets of features using filter methods Mutual Information (MI), Chi-square, and Anova F-Test, and with the selected features building learning models using MLP based classifier.…”

Section: Related Workmentioning

confidence: 99%

Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study

Mukherjee

Dhar

Schwenker

et al. 2021

Sensors

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Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disease. In-time diagnosis of apnea is needed which can be observed by the application of a proper health monitoring system. In this work, we focus on Obstructive Sleep Apnea (OSA) detection from the Electrocardiogram (ECG) signals obtained through the body sensors. Our work mainly consists of an experimental study of different ensemble techniques applied on three deep learning models—two Convolutional Neural Network (CNN) based models, and a combination of CNN and Long Short-Term Memory (LSTM) models, which were previously proposed in the OSA detection domain. We have chosen four ensemble techniques—majority voting, sum rule and Choquet integral based fuzzy fusion and trainable ensemble using Multi-Layer Perceptron (MLP) for our case study. All the experiments are conducted on the benchmark PhysioNet Apnea-ECG Database. Finally, we have achieved highest OSA detection accuracy of 85.58% using the MLP based ensemble approach. Our best result is also able to surpass many of state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%