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Author: search.filters.author.Tretriluxana, SuradejSubject: search.filters.subject.polysomnography

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    Effect of Class Weights on Imbalanced Classes in Bi-directional LSTM Training for Sleep Apnea Classification
    (2024-01-01)
    Jansri, Ukkrit
    ;
    Tretriluxana, Suradej
    Sleep apnea, which is defined as the repetitive cessations of breathing during sleep, is the common disorder worldwide. The cost and the process of sleep test to obtain the polysomnogram is not optimal for sleep apnea screening in the large population. A deep learning model was developed to classify the normal and apnea events in a single time-series signal exported from the US National Institute of Health (NIH) sponsored database. Our challenge was to train the model with imbalanced dataset between normal and abnormal respiratory events. Three different methods, Synthetic Minority Over-sampling Technique (SMOTE), Random Under-Sampling (RUS), and the Class Weights (CW) were chosen to improve the model performance over the original data on five selected signals from polysomnographic dataset. The binary classification outputs were evaluated by four metrics. Our results showed (1) Matthews Correlation Coefficient was highest (MCC= 0.1385) in the Class Weights method on the nasal airflow signal. (2) Cohen's Kappa score, was highest (k= 0.0819) in SMOTE technique on the abdominal signal, followed by the Class Weights method on the abdominal signal (k= 0.0687) and RUS technique on nasal airflow signal (k= 0.0441). (3) F1-score was highest (F1= 11.89%) in SMOTE technique on the abdominal signal, followed by the Class Weights method on nasal airflow signal (F1 = 11.17%) and RUS technique on nasal airflow signal (F1= 9.16%). The findings suggest that the Class Weights method on nasal airflow and the Class Weights method on abdominal signal were the two combinations to be used in the DL model.
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    Comparing the performances of deep learning model with different signals underlying resampling techniques to classify sleep apnea
    (2023-01-01)
    Jansri, Ukkrit
    ;
    Tretriluxana, Suradej
    Millions of people around the world are suffering from long term Sleep Apnea. Full scale sleep test is costly and time-consuming. This paper, using deep learning model, chose a single candidate signal from multi-channel polysomnogram data for sleep apnea screening. Nature of data, however, shows an imbalance class of dataset between normal and apneic events. To increase the binary classification output performance, two resampling techniques; Synthetic Minority Over-sampling Technique (SMOTE) and Random Under-Sampling (RUS), were employed in Bidirectional Long Short-Term Memory (Bi-LSTM) model training. One hundred polysomnography (PSG) records were randomly selected from the Multi-Ethic of Atherosclerosis (MESA) database in this study. They were trained under three conditions; original, SMOTE and RUS datasets. Our results showed (1) Cohen's kappa score was greater in resampling (SMOTE, RUS) datasets than original one. (2) Between the resampling techniques, metrices in SMOTE were better than ones in RUS. (3) Within SMOTE, the abdominal belt was the best among other signals with Cohen's kappa score of 0.2078 and 58.99% in F1-score. These findings suggested that abdominal belt was the best candidate signal for sleep apnea screening.