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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.
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    Bluetooth Breathing Sound Detection Device Based on Time and Frequency Domain Analyze
    (2022-04-20)
    Chunhakam, Puripak
    ;
    Panpho, Phakakorn
    ;
    Wardkein, Paramote
    In this paper, a wearable and simple system for breathing sounds detection from the left and right External Auditory Canal (EAC) was proposed. This system consists of two main parts: 1) Hardware design for detecting input breathing sound signal and 2) software design for processing and displaying the output results. Two condenser microphones are used to detect breathing sound and the detected signal is transmitted to the main processor by a Bluetooth channel. Two main proposed algorithms to detect breathing sounds and evaluate the number of respirations were presented: the first algorithm employs sound signals in the time domain to detect breathing sound with power window threshold scanning and count breathing pulses. For the second one, Fast Fourier transforms (FFT) along with detecting the maximum magnitude of its low-frequency elements in the breathing frequency band is presented and it is interpreted as respiration rate. The results of both algorithms show that the system can accurately monitor breathing. The percentage of error for the 1st and 2nd algorithms was 6.77% and 5.00%, respectively. The experimental results presented that this breathing detection system can measure and provide the breathing and respiration rate as expected.
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    Effect of Resampling Techniques on Deep Learning Model Training in Sleep Apnea Classification
    (2022-01-01)
    Jansri, Ukkrit
    ;
    Tretriluxana, Suradej
    This study is using deep learning model to classify the respiratory events of Sleep Disordered Breathing (SDB) data. Our pilot results showed the missed identification in some classes even the total accuracy is high. This is the result of unbalanced training dataset given to the model. Two different resampling techniques; Synthetic Minority Over-sampling Technique (SMOTE) and Random Under-Sampling (RUS), were introduced to balance the data. One hundred overnight nasal airflow signals were randomly selected from NIH funded polysomnography database. They were used to train and test these two algorithms with Bi-directional Long Short-Term Memory (Bi-LSTM) model. The results showed greater agreement index when compared between with and without data resampling process. However, SMOTE in sum performed better than RUS (93.72% vs 70.01% in overall accuracy and 0.91 vs 0.55 in Cohen's kappa). It demonstrates that the over-sampling technique is more powerful than under-sampling one. Other resampling techniques will be investigated to make the robust conclusion.