Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea

Álvarez, Daniel; Crespo, Andrea; Vaquerizo-Villar, Fernando; Gutiérrez‐Tobal, Gonzalo C.; Cerezo-Hernández, Ana; Barroso-García, Verónica; Ansermino, J. Mark; Dumont, Guy A.; Hornero, Roberto; Campo, Félix del; Garde, Ainara

doi:10.1088/1361-6579/aae2a8

Cited by 11 publications

(39 citation statements)

References 52 publications

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“…The use of the oximetry signal to assist in the diagnosis of pediatric SAHS has been frequently advocated, being especially suitable for children [3]. The development of portable monitoring devices has allowed to record the SpO2 signals with a pulse oximeter at the patient's home in children suffering from SAHS [10].…”

Section: Discussionmentioning

confidence: 99%

“…Recent works have focused on the application of signal processing techniques to the SpO2 signal in order to assist in the diagnosis of pediatric SAHS [5]- [10]. These studies used conventional machine-learning approaches: feature extraction, feature selection, and classification and regression algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…These studies used conventional machine-learning approaches: feature extraction, feature selection, and classification and regression algorithms. These studies reported accuracies in the range 75-85%, 81-84%, and 85-91% for the detection of mild (AHI≥1), moderate (AHI≥5), and severe SAHS (AHI≥10), respectively [5]- [10]. However, these studies focused on detecting the presence and severity of pediatric SAHS [5]- [10], whereas our study aims at detecting AH events.…”

Section: Discussionmentioning

confidence: 99%

“…SpO2 value usually presents drops associated to AH events [2], thus being useful to detect these episodes. In this sense, previous works have employed a methodology based on the automated analysis of the SpO2 signal in order to assist in the diagnosis of childhood SAHS [5]- [10].…”

Section: Introductionmentioning

confidence: 99%

“…These studies have applied conventional oximetric indices, as well as time, frequency, and nonlinear methods to obtain features that may characterize the properties of the SpO2 signal [5]- [10]. In addition, feature selection algorithms were used to reduce the dimensionality and improve performance, whereas classification and regression algorithms were employed to detect pediatric SAHS [5]- [10]. However, these machinelearning based frameworks require to determine which features to extract from the physiological signal, as well as to decide how many features are needed, being a difficult task [11].…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry

Vaquerizo-Villar

Álvarez

Kheirandish‐Gozal

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children's health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagnosis (overnight polysomnography) has several limitations, which has led to the search for alternative tests. In this sense, automated analysis of overnight oximetry has emerged as a simplified technique. Previous studies have focused on the extraction of ad-hoc features from the blood oxygen saturation (SpO2) signal, which may miss useful information related to apnea and hypopnea (AH) events. In order to overcome this limitation of traditional approaches, we propose the use of convolutional neural networks (CNN), a deep learning technique, to automatically detect AH events from the SpO2 raw data. CHAT-baseline dataset, composed of 453 SpO2 recordings, was used for this purpose. A CNN model was trained using 60-s segments from the SpO2 signal using a training set (50% of subjects). Optimum hyperparameters of the CNN architecture were obtained using a validation set (25% of subjects). This model was applied to a third test set (25% of subjects), reaching 93.6% accuracy to detect AH events. These results suggest that the application of CNN may be useful to detect changes produced in the oximetry signal by AH events in pediatric SAHS patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry

Vaquerizo-Villar

Álvarez

Kheirandish‐Gozal

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta‐analysis

Gutiérrez‐Tobal

Álvarez

Kheirandish‐Gozal

et al. 2021

Pediatric Pulmonology

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Background Machine‐learning approaches have enabled promising results in efforts to simplify the diagnosis of pediatric obstructive sleep apnea (OSA). A comprehensive review and analysis of such studies increase the confidence level of practitioners and healthcare providers in the implementation of these methodologies in clinical practice. Objective To assess the reliability of machine‐learning‐based methods to detect pediatric OSA. Data Sources Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references. Eligibility Criteria Articles or reviews (Year 2000 onwards) that applied machine learning to detect pediatric OSA; reported data included information enabling derivation of true positive, false negative, true negative, and false positive cases; polysomnography served as diagnostic standard. Appraisal and Synthesis Methods Pooled sensitivities and specificities were computed for three apnea‐hypopnea index (AHI) thresholds: 1 event/hour (e/h), 5 e/h, and 10 e/h. Random‐effect models were assumed. Summary receiver‐operating characteristics (SROC) analyses were also conducted. Heterogeneity (I 2) was evaluated, and publication bias was corrected (trim and fill). Results Nineteen studies were finally retained, involving 4767 different pediatric sleep studies. Machine learning improved diagnostic performance as OSA severity criteria increased reaching optimal values for AHI = 10 e/h (0.652 sensitivity; 0.931 specificity; and 0.940 area under the SROC curve). Publication bias correction had minor effect on summary statistics, but high heterogeneity was observed among the studies.

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Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures

Álvarez

Gutiérrez‐Tobal

Vaquerizo-Villar

et al. 2022

Advances in the Diagnosis and Treatment of Sleep Apnea

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Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea

Abstract: Symbolic dynamics provides complementary information to conventional oximetry analysis enabling reliable detection of moderate-to-severe paediatric OSAHS from portable oximetry.

Cited by 11 publications

References 52 publications

Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry

Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry

Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta‐analysis

Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures

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