Sleep apnea detection using deep learning

Chaw, Hnin Thiri; Kamolphiwong, Sinchai; Wongsritrang, Krongthong

doi:10.31803/tg-20191104191722

Cited by 21 publications

(12 citation statements)

References 28 publications

(30 reference statements)

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“…Precision= TP TP+FP (7) • Recall: The ratio of true positive predictions compared to the total number of true positive data.…”

Section: Resultsmentioning

confidence: 99%

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Dealing with Imbalanced Sleep Apnea Data Using DCGAN

Wicaksono¹,

Philip²,

Alam³

et al. 2022

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Data in the health sector are often lacking and unbalanced. It is because collecting data takes time and many resources. One example is sleep apnea data which takes about 8–10 hours to get data and uses specialized hardware like polysomnography (PSG). This study proposes a data augmentation technique to handle unbalanced data using DCGAN and several deep learning models such as 1D-CNN, ANN, LSTM, and 1D-CNN+LSTM as a classifier for apnea detection. The DCGAN architecture used is CNN on the generator and discriminator. DCGAN will create new synthetic data by mimicking the original dataset. This experiment uses a dataset from PhysioNet, the Apnea-ECG, and the MIT-BIH PSG Database. Furthermore, the dataset is preprocessed to remove noise, and the features are extracted manually. The test scenario is to create 10% synthetic data and 50% sleep apnea data to be added to the original dataset. Then compare the performance of multiple deep learning models before and after adding data. The results indicate that augmentation with DCGAN can improve the performance of almost all models, with the highest increase of 1.78% on the 1D-CNN+LSTM model and 4.80% on the LSTM model for the Apnea-ECG and MIT-BIH datasets, respectively.

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“…Precision= TP TP+FP (7) • Recall: The ratio of true positive predictions compared to the total number of true positive data.…”

Section: Resultsmentioning

confidence: 99%

“…Chaw et al [7] used SPO2 data from patients taken from the sleep lab to train the CNN classifier to detect OSA with an accuracy reaching 91.30%, which is better than the ANN model. Erdenebayar et al [8] tried using six DL models for OSA detection.…”

Section: Related Workmentioning

confidence: 99%

Dealing with Imbalanced Sleep Apnea Data Using DCGAN

Wicaksono¹,

Philip²,

Alam³

et al. 2022

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“…In another study, the detection of sleep apnea was performed by a CNN model built from scratch based on oxygen saturation (SpO2) signals. The CNN model outperformed other models including linear discriminant analysis (LDA), SVM, bagging representation tree, and artificial neural networks (ANN) [23]. In [22], six DL methods were validated to find the optimal method for automatic detection of SA events from ECG signals.…”

Section: Discussionmentioning

confidence: 99%

“…They have been favored in many data analytics applications, such as computer vision, natural language processing, speech recognition, and healthcare [18], [20]. The DL approaches that have been used previously for SA detection include long short-term memory (LSTM) [21], [22], convolutional neural network (CNN) model [22], [23], and pre-trained CNN models [24]. Despite their promising results, all these studies have used data collected via devices and sensors with the aforementioned limitations (obtrusive, uncomfortable, etc.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

Azimi

Bouchard

et al. 2020

IEEE Access

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Polysomnography (PSG) is the standard test for diagnosing sleep apnea. However, the approach is obtrusive, time-consuming, and with limited access for patients in need of sleep apnea diagnosis. In recent years, there have been many attempts to search for an alternative device or approach that avoids the limitations of PSG. Pressure-sensitive mats (PSM) have proven to be able to detect central sleep apneas (CSA) and be a potential alternative for PSG. In the current study, we combine advanced machine learning approaches with a practical unobtrusive home monitoring device (PSM) to detect CSA events from data collected nocturnally and unattended. Two deep learning methods are implemented for the automatic detection of CSA events: a temporal convolutional network (TCN) and a bidirectional long short-term memory (BiLSTM) network. The deep learning models are compared to a classical machine learning approach (linear support vector machine, SVM) and a simple threshold-based algorithm. Considering the characteristics of each method, we choose strategies, including resampling and weighted cost-functions, to optimize the methods and to perform CSA detection as anomaly detection in an imbalanced data set. We evaluate the performance of all models on a database containing 7 days of data from 9 elderly patients. From the resulting 63 days, data from 7 patients (49 days) are devoted to training for optimizing hyperparameters, and data from 2 patients (14 days) are devoted to testing. Experimental results indicate that the bestperforming model achieves an accuracy of 95.1% through training an BiLSTM network. Overall, the implemented deep learning methods achieve better performance than the conventional classification approach (SVM) and the simple threshold-based method, and show good potential for the use of PSM for practical unobtrusive monitoring of CSA. INDEX TERMS Biomedical measurement, data analysis, deep learning, machine learning, patient monitoring, pressure measurement, central sleep apnea detection.

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“…Word2vec is a word vector generation tool based on deep learning released by Google in 2013. The goal is to use high-quality document data to train high-quality word vectors, and then perform many NLP-related clustering and part-of-speech tagging [Chaw, 2019]. Word2vec is essentially a neural network structure; there are two main models, namely Continuous Bag-of-Words Model (CBOW) and Skip-Gram model.…”

Section: Short Text Representation Of Word2vecmentioning

confidence: 99%

Analysis on an Auto Increment Detection System of Chinese Disaster Weibo Text

Bai¹,

Yu²,

Yu³

et al. 2021

jucs

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With the rapid development of Internet information technology, the advantages of social media in terms of speed, content, form, and effect of communication are becoming increasingly significant. In recent years, more and more researchers have paid attention to the special value and role of social media tools in disaster information emergency management. Weibo is the most widely used Chinese social media tool. To effectively mine and apply the emergency function of disaster situation microblogs, a disaster situation information discovery and collection system capable of online incremental identification and collection are constructed for massive and disordered disaster microblog text streams. First, based on the deep learning- trained word vector model and a large-scale corpus, an unsupervised short-text feature representation method of disaster situation Weibo information is developed. According to the experimental results of the feature combination test and the training set scale test, the SVM algorithm was selected for disaster microblog information classification, which realized effective identification of disaster situation micro-bloggings. Then, the temporal information similarity and geographic information similarity are used to improve the single text similarity algorithm, and a Chinese disaster event online real-time detection model is constructed. Furthermore, the disaster-affected areas can be achieved in real-time based on the detection results. By crawling and classifying the micro-bloggings from the disaster-affected areas, it is possible to realize the incremental identification and collection of online disaster situation Weibo information. Finally, the empirical analysis of disaster events such as the “Leshan Earthquake” shows that the real- time intelligent identification and collection system for disaster situation Weibo micro-bloggings developed in this paper can obtain large-scale and useful data for disaster emergency management, which proving that this system is effective and efficient.

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Sleep apnea detection using deep learning

Cited by 21 publications

References 28 publications

Dealing with Imbalanced Sleep Apnea Data Using DCGAN

Dealing with Imbalanced Sleep Apnea Data Using DCGAN

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

Analysis on an Auto Increment Detection System of Chinese Disaster Weibo Text

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