Obstructive Sleep Apnea Diagnosis: The Bayesian Network Model Revisited

Rodrigues, Pedro Pereira; Ferreira-Santos, Daniela; Leite, Liliana

doi:10.1109/cbms.2015.47

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…In this clinical outcome, diagnostic models need to have a high sensitivity, as false negatives should be avoided, to prevent excluding patients with moderate or severe OSA from performing polysomnography (PSG) [24,34], the standard test for OSA final diagnosis. This study aimed to define auxiliary diagnostic methods that can support the decision to perform PSG, based on risk and diagnostic factors by means of interactive models or risk matrix.…”

Section: Introductionmentioning

confidence: 99%

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Ferreira-Santos

Rodrigues

2018

Int J Data Sci Anal

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In obstructive sleep apnea, respiratory effort is maintained but ventilation decreases/disappears due to upper-airway partial/total occlusion. This condition affects about 4% of men and 2% of women worldwide. This study aimed to define an auxiliary diagnostic method that can support the decision to perform polysomnography, based on risk and diagnostic factors. Our sample performed polysomnography between January and May 2015. Two Bayesian classifiers were used to build the models: Naïve Bayes and Tree Augmented Naïve Bayes, using 38 variables identified by literature review or just a selection of 6. Area under the ROC curve, sensitivity, specificity and predictive values were evaluated using leave-one-out and cross-validation techniques. From a total of 241 patients, only 194 fulfilled the inclusion criteria, 123 (63%) were male, with a mean age of 58 years, 66 (34%) patients had a normal result and 128 (66%) a diagnosis of obstructive sleep apnea. The cross-validated AUCs for each model were: NB38: 69.2%; TAN38: 69.0%; NB6: 74.6% and TAN6: 63.6%. Regarding risk matrix, female gender presented a starting rate of 8%, comparing to 20% in male gender, almost 3 times higher. The high (34%) proportion of normal results confirms the need for a pre-evaluation prior to polysomnography, making the search for a validated model to screen patients with suspicion of obstructive sleep apnea essential, especially at primary care level.

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Section: Introductionmentioning

confidence: 99%

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Ferreira-Santos

Rodrigues

2018

Int J Data Sci Anal

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“…Ardından hastalar uzman görüşüne ve literatür bilgisine dayalı olarak istatistiksel modelde aşırı uygunluğu (overfitting) ve aşırı uç değerlerin etkisini önlemek, yorum kolaylığı sağlamak ve işlem süresini kısaltmak için kategorilere ayrıldı. Yaş genç (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35), orta yaşlı (36-55) ve ileri yaşlı (≥56) olarak üç gruba, VKİ normal kilolu (VKİ, 18,5-24,9 kg/m 2 ), fazla kilolu (VKİ, 25-29,9 kg/m 2 ), I. Derece obez (VKİ, 30-34,9 kg/m 2 ), II. Derece obez (BMI, 35-39,9 kg/m 2 ), III.…”

Section: Veri Setiunclassified

Aditif Bayes Ağları Yöntemi ile Obstrüktif Uyku Apnesi Şiddetinin Tahminlenmesi

Bozkurt¹,

Bostancı²,

Turhan³

2018

Akd Med J

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Amaç: Günümüzde makine öğrenmesi temelli tahmin modelleri, birçok farklı alanda yaygın bir şekilde kullanılmaktadır. Bu çalışmanın amacı obstrüktif uyku apnesi (OUA) şiddetinin tahminlenmesinde hastaların klinik özelliklerini içeren bir aditif bayes ağ modeli geliştirilmesidir. Gereç ve Yöntemler: Bu çalışmaya Ocak 2014-Ağustos 2015 tarihleri arasında Akdeniz Üniversitesi Kulak-Burun-Boğaz Hastalıkları Anabilim Dalı'nda OUA ön tanısı ile polisomnografi yapılmış 338 hasta dahil edilmiştir. OUA şiddeti ile ilişkili tüm klinik değişkenler ve bu değişkenlerin birbirleriyle olan ilişkileri aditif bayes ağ modeli ile modellenmiştir. Bulgular: Nihai Bayes modelindeki en önemli öngördürücü değişkenler, yumuşak damak ve uvula boyutu (Odds oranı (OR):10.94 [1.87-64.11]) ile mallampati skoru (OR: 4.5 [1.46-43.22]) idi. Nihai modelin 10 kat çapraz geçerlilik test sonucu, OUA şiddetinin 0.632 duyarlılık ve 0.529 seçicilik ile tahminlenebileceğini gösterdi. Ayrıca, geliştirilen modelin ağır OUA hastalarını sınıflandırmada 0.777 duyarlılık ve 0.646 seçiciliğe sahip olduğu saptandı. Sonuç: Makine öğrenmesi temelli tahmin modelleri, klinik değişkenler arasındaki kompleks ilişkileri analiz ederek OUA tanısı ve şiddetinin tahmin edilmesini kolaylaştırabilir.

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“…At the event level, each single apnea and hypopnea event is identified and classified and hence the AHI is accordingly calculated for the diagnosis. For the classification, various machine learning techniques such as support vector machine (SVM) [12], ensemble classifiers [13], and Bayesian network-based classifier [14] have been used to identify the sleep apnea events. Recently, a convolutional-neural-network-based deep learning framework [15] was proposed to detect obstructive sleep apnea events.…”

Section: Introductionmentioning

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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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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Obstructive Sleep Apnea Diagnosis: The Bayesian Network Model Revisited

Cited by 7 publications

References 9 publications

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Aditif Bayes Ağları Yöntemi ile Obstrüktif Uyku Apnesi Şiddetinin Tahminlenmesi

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

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