Frequency Features Selection Using Decision Tree for Classification of Sleep Breathing Sound

Nguyen, Tan Loc; Won, Yonggwan

doi:10.1007/978-981-10-0557-2_130

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We proposed a stacked ensemble model to further enhance the performance of our system in classifying breathing waveform data. We compared the performance of several classifiers that have been proven to be effective in similar cases such as Multinomial Logistic Regression (MLR) [24], Decision Tree (DT) [25], [26], Random Forest (RF) [27], Support Vector Machine (SVM) [28], [29], eXtreme Gradient Boosting (XGB) [30], [31], Light Gradient Boosting Machine (LGBM), CatBoosting Classifier (CB), Multilayer Perceptron (MLP) [32], [33] and three proposed stacked ensemble models.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time

Purnomo

Komariah

Lin

et al. 2022

IEEE Trans. Biomed. Circuits Syst.

View full text Add to dashboard Cite

A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously Manuscript

show abstract

Section: Introductionmentioning

confidence: 99%

Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time

Purnomo

Komariah

Lin

et al. 2022

IEEE Trans. Biomed. Circuits Syst.

View full text Add to dashboard Cite

show abstract

Attention Based Convolutional Neural Network with Multi-frequency Resolution Feature for Environment Sound Classification

Li¹,

Huang

Zhang³

2022

Neural Process Lett

View full text Add to dashboard Cite

The environmental sound classification has great research significance in the fields of intelligent audio monitoring and other fields. A novel multi-frequency resolution (MFR) feature is proposed in this paper to solve the problem that the existing single frequency resolution time–frequency features of sound cannot effectively express the characteristics of multiple types of sound. The MFR feature is composed of three features with different frequency resolutions, which are compressed in varying degrees at the time dimension. This method not only has the effect of data augmentation but also can obtain more context information during the feature extraction. And the MFR features of Log-Mel Spectrogram, Cochleagram, and Constant Q-Transform are combined to form a multi-channel MFR feature. Also, a network named SacNet is built, which can effectively solve the problem that the time–frequency feature map of sound contains more invalid information. The basic structural unit of the SacNet consists of two parallel branches, one using depthwise separable convolution as the main feature extractor, and the other using spatial attention module to extract more effective information. Experiment results have demonstrated that the proposed method achieves the state-of-the-art accuracy of 97.5%, 93.1%, and 95.3% on three benchmark datasets of ESC10, ESC50, and UrbanSound8K respectively, which are increased by 3.3%, 0.5%, and 2.3% respectively compared with the previous advanced methods.

show abstract

Frequency Features Selection Using Decision Tree for Classification of Sleep Breathing Sound

Cited by 2 publications

References 6 publications

Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time

Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time

Attention Based Convolutional Neural Network with Multi-frequency Resolution Feature for Environment Sound Classification

Contact Info

Product

Resources

About