Phenotype-Based and Self-Learning Inter-Individual Sleep Apnea Screening With a Level IV-Like Monitoring System

Wu, Hau‐Tieng; Wu, Jhao-Cheng; Huang, Po-Chiun; Lin, Ting-Yu; Wang, Tsai‐Yu; Huang, Yuan-Hao; Lo, Yu‐Lun

doi:10.3389/fphys.2018.00723

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…The training and testing databases had nearly the same distribution over various severity levels, as presented in Table 2. In our previous studies [23,34], SVM was used and followed by a state machine for screening OSAHS. The SVM model is divided into three types.…”

Section: Resultsmentioning

confidence: 99%

“…First, the original SVM uses 50% of participants for training and 50% of participants for testing. Second, in the phenotype-based SVM [34], K = 15 nearest subjects of all data are selected according to gender, BMI and age with weights of 4, 2 and 1, respectively. Third, in the phenotype-based SVM with comorbidity information, the most similar 20 subjects are first selected and then the nearest 15 subjects are selected from these candidates using the K-nearest neighborhood method.…”

Section: Resultsmentioning

confidence: 99%

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Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network

Chang

Huang

et al. 2020

Sensors

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Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the gold standard for diagnosing OSAHS; however, this PSG evaluation process is unsuitable for home screening. To solve this problem, a measuring module integrating abdominal and thoracic triaxial accelerometers, a pulsed oximeter (SpO2) and an electrocardiogram sensor was devised in this study. Moreover, a long short-term memory recurrent neural network model is proposed to classify four types of sleep breathing patterns, namely obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP) events and normal breathing (NOR). The proposed algorithm not only reports the apnea-hypopnea index (AHI) through the acquired overnight signals but also identifies the occurrences of OSA, CSA, HYP and NOR, which assists in OSAHS diagnosis. In the clinical experiment with 115 participants, the performances of the proposed system and algorithm were compared with those of traditional expert interpretation based on PSG signals. The accuracy of AHI severity group classification was 89.3%, and the AHI difference for PSG expert interpretation was 5.0±4.5. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network

Chang

Huang

et al. 2020

Sensors

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“…Additionally, the model output can be fed back in real‐time fashion to correlate the identified patterns such as DO to symptoms at the same time to provide most comprehensive evaluation. Furthermore, we could imagine adding additional clinical variables to further enhance system performance while potentially allowing us to recognize more complex patterns in UDS beyond just DO 12 …”

Section: Discussionmentioning

confidence: 99%

Pattern recognition algorithm to identify detrusor overactivity on urodynamics

Wang

Cahill

Panagides

et al. 2020

Neurourology and Urodynamics

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Aims: Detrusor overactivity (DO) of the bladder is a finding on urodynamic studies (UDS) that often correlates with lower urinary tract symptoms and drives management. However, UDS interpretation remains nonstandardized. We sought to develop a mathematical model to reliably identify DO in UDS. Methods: We utilized UDS archive files for studies performed at our institution between 2013 and 2019. Raw tracings of vesical pressure, abdominal pressure, detrusor pressure, infused volume, and all annotations during UDS were obtained. Patients less than 1 year old, studies with calibration issues, or those with significant artifacts were excluded. In the training set, five representative DO patterns were identified. Candidate Pdet signal segments were matched to representative DO patterns. Manifold learning and dynamic time warping algorithms were used. Five-fold cross validation (CV) was used to evaluate the performance. Results: A total of 799 UDS studies were included. The median age was 9 years (range, 1-33). There were 1,742 DO events that did not overlap with annotated artifacts (cough, cry, valsalva, movements). The AUC of the training sets from the five-fold CV was 0.84 ± 0.01. The five-fold CV leads to an overall accuracy 81.35%, and sensitivity and specificity of detecting DO events are 76.92% and 81.41%, respectively, in the testing set. Conclusions: Our predictive model using machine learning algorithms provides promising performance to facilitate automated identification of DO in UDS. This would allow for standardization and potentially more reliable UDS interpretation. Signal processing and machine learning interpretation of the other components of UDS are forthcoming.

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“…Obstructive Sleep Apnea (OSA), one of the most common sleep disorders, is estimated to affect up to 7% of adults in the USA [85,86]. Wearables have been successful in detecting OSA in a variety of ways, including with personalized machine learning algorithms based on similarity to other OSA patients (detection accuracies between 90 and 93.6%) [41,87]. Sleep impacts chronic illness, neurological conditions and acute cognitive function including memory and mood [22,[88][89][90].…”

Section: Sleep Neurology Mental Health and Movement Disordersmentioning

confidence: 99%

Wearables and the Medical Revolution

2018

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Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and their progress toward clinical application. We describe technologies underlying common, commercially available wearable sensors and early-stage devices and outline research, when available, to support the use of these devices in healthcare. We cover applications in the following health areas: metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. Finally, we discuss challenges associated with the adoption of wearable sensors in the current healthcare ecosystem and discuss areas for future research and development.

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Phenotype-Based and Self-Learning Inter-Individual Sleep Apnea Screening With a Level IV-Like Monitoring System

Cited by 6 publications

References 20 publications

Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network

Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network

Pattern recognition algorithm to identify detrusor overactivity on urodynamics

Wearables and the Medical Revolution

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