Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification

Dramatic raising of Deep Learning (DL) approach and its capability in biomedical applications lead us to explore the advantages of using DL for sleep Apnea-Hypopnea severity classification. To reduce the complexity of clinical diagnosis using Polysomnography (PSG), which is multiple sensing platform, we incorporates our proposed DL scheme into one single Airflow (AF) sensing signal (subset of PSG). Seventeen features have been extracted from AF and then fed into Deep Neural Networks to classify in two studies. First, we proposed a binary classifications which use the cutoff indices at AHI = 5, 15 and 30 events/hour. Second, the multiple Sleep Apnea-Hypopnea Syndrome (SAHS) severity classification was proposed to classify patients into 4 groups including no SAHS, mild SAHS, moderate SAHS, and severe SAHS. For methods evaluation, we used a higher number of patients than related works to accommodate more diversity which includes 520 AF records obtained from the MrOS sleep study (Visit 2) database. We then applied the 10-fold cross-validation technique to get the accuracy, sensitivity and specificity. Moreover, we compared the results from our main classifier with other two approaches which were used in previous researches including the Support Vector Machine (SVM) and the Adaboost-Classification and Regression Trees (AB-CART). From the binary classification, our proposed method provides significantly higher performance than other two approaches with the accuracy of 83.46 %, 85.39 % and 92.69 % in each cutoff, respectively. For the multiclass classification, it also returns a highest accuracy of all approaches with 63.70 %

An explainable deep-learning architecture for pediatric sleep apnea identification from overnight airflow and oximetry signals

Producción CientíficaDeep-learning algorithms have been proposed to analyze overnight airflow (AF) and oximetry (SpO2) signals to simplify the diagnosis of pediatric obstructive sleep apnea (OSA), but current algorithms are hardly interpretable. Explainable artificial intelligence (XAI) algorithms can clarify the models-derived predictions on these signals, enhancing their diagnostic trustworthiness. Here, we assess an explainable architecture that combines convolutional and recurrent neural networks (CNN + RNN) to detect pediatric OSA and its severity. AF and SpO2 were obtained from the Childhood Adenotonsillectomy Trial (CHAT) public database (n = 1,638) and a proprietary database (n = 974). These signals were arranged in 30-min segments and processed by the CNN + RNN architecture to derive the number of apneic events per segment. The apnea-hypopnea index (AHI) was computed from the CNN + RNN-derived estimates and grouped into four OSA severity levels. The Gradient-weighted Class Activation Mapping (Grad-CAM) XAI algorithm was used to identify and interpret novel OSA-related patterns of interest. The AHI regression reached very high agreement (intraclass correlation coefficient > 0.9), while OSA severity classification achieved 4-class accuracies 74.51% and 62.31%, and 4-class Cohen’s Kappa 0.6231 and 0.4495, in CHAT and the private datasets, respectively. All diagnostic accuracies on increasing AHI cutoffs (1, 5 and 10 events/h) surpassed 84%. The Grad-CAM heatmaps revealed that the model focuses on sudden AF cessations and SpO2 drops to detect apneas and hypopneas with desaturations, and often discards patterns of hypopneas linked to arousals. Therefore, an interpretable CNN + RNN model to analyze AF and SpO2 can be helpful as a diagnostic alternative in symptomatic children at risk of OSA.Ministerio de Ciencia e Innovación /AEI/10.13039/501100011033/ FEDER (grants PID2020-115468RB-I00 and PDC2021-120775-I00)CIBER -Consorcio Centro de Investigación Biomédica en Red- (CB19/01/00012), Instituto de Salud Carlos IIINational Institutes of Health (HL083075, HL083129, UL1-RR-024134, UL1 RR024989)National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002)Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación- “Ramón y Cajal” grant (RYC2019-028566-I

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Sleep apnea-hypopnea syndrome (SAHS) is a chronic and highly prevalent disease considered a major health problem in industrialized countries. The gold standard diagnostic methodology is in-laboratory nocturnal polysomnography (PSG), which is complex, costly, and time consuming. In order to overcome these limitations, novel and simplified diagnostic alternatives are demanded. Sleep scientists carried out an exhaustive research during the last decades focused on the design of automated expert systems derived from artificial intelligence able to help sleep specialists in their daily practice. Among automated pattern recognition techniques, artificial neural networks (ANNs) have demonstrated to be efficient and accurate algorithms in order to implement computer-aided diagnosis systems aimed at assisting physicians in the management of SAHS. In this regard, several applications of ANNs have been developed, such as classification of patients suspected of suffering from SAHS, apnea-hypopnea index (AHI) prediction, detection and quantification of respiratory events, apneic events classification, automated sleep staging and arousal detection, alertness monitoring systems, and airflow pressure optimization in positive airway pressure (PAP) devices to fit patients’ needs. In the present research, current applications of ANNs in the framework of SAHS management are thoroughly reviewed

Automatic analysis of overnight airflow to help in the diagnosis of pediatric obstructive sleep apnea

La apnea obstructiva del sueño (AOS) pediátrica es una enfermedad respiratoria altamente prevalente e infradiagnosticada que puede afectar negativamente a las funciones fisiológicas y cognitivas de los niños, causándoles graves deficiencias neurocognitivas, cardiometabólicas y endocrinas. El método estándar para su diagnóstico es la polisomnografía nocturna, una prueba compleja, de elevado coste, altamente intrusiva y poco accesible, lo que genera largas listas de espera y retrasos en el diagnóstico. Por ello, es necesario desarrollar pruebas diagnósticas más sencillas. Una de estas alternativas es el análisis automático de señales cardiorrespiratorias. Así, esta tesis doctoral presenta un compendio de cuatro publicaciones que proponen el uso de novedosos métodos de procesado de señal (no lineal, espectral, bispectral, gráficos de recurrencia y wavelet) que permiten caracterizar exhaustivamente el comportamiento del flujo aéreo nocturno de los niños y simplificar el diagnóstico de la apnea obstructiva del sueño pediátrica.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Wavelet analysis of overnight airflow to detect obstructive sleep apnea in children

Producción CientíficaThis study focused on the automatic analysis of the airflow signal (AF) to aid in the diagnosis of pediatric obstructive sleep apnea (OSA). Thus, our aims were: (i) to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, (ii) to evaluate its diagnostic utility, and (iii) to assess its complementarity with the 3% oxygen desaturation index (ODI3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: (i) DWT-derived feature extraction, (ii) feature selection, and (iii) pattern recognition. AF recordings from OSA patients showed both lower detail coefficients and decreased activity associated with the normal breathing band. Wavelet analysis also revealed that OSA disturbed the frequency and energy distribution of the AF signal, increasing its irregularity. Moreover, the information obtained from the wavelet analysis was complementary to ODI3. In this regard, the combination of both wavelet information and ODI3 achieved high diagnostic accuracy using the common OSA-positive cutoffs: 77.97%, 81.91%, and 90.99% (AdaBoost.M2), and 81.96%, 82.14%, and 90.69% (Bayesian multi-layer perceptron) for 1, 5, and 10 apneic events/hour, respectively. Hence, these findings suggest that DWT properly characterizes OSA-related severity as embedded in nocturnal AF, and could simplify the diagnosis of pediatric OSA.Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación y Fondo Europeo de Desarrollo Regional (FEDER) - (Projects DPI2017-84280-R and RTC-2017-6516-1)Comisión Europea y Fondo Europeo de Desarrollo Regional (FEDER) - (POCTEP 0702_MIGRAINEE_2_E)Instituto de Salud Carlos III y Fondo Europeo de Desarrollo Regional (FEDER) - (CIBER-BBN)Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación y Fondo Social Europeo - (grant RYC2019- 028566-I)Ministerio de Educación, Cultura y Deporte - (grant FPU16/02938)Institutes of Health - (grants HL130984, HL140548, and AG061824

수면 호흡음을 이용한 폐쇄성 수면 무호흡 중증도 분류

학위논문 (박사)-- 서울대학교 융합과학기술대학원 융합과학부, 2017. 8. 이교구.Obstructive sleep apnea (OSA) is a common sleep disorder. The symptom has a high prevalence and increases mortality as a risk factor for hypertension and stroke. Sleep disorders occur during sleep, making it difficult for patients to self-perceive themselves, and the actual diagnosis rate is low. Despite the existence of a standard sleep study called a polysomnography (PSG), it is difficult to diagnose the sleep disorders due to complicated test procedures and high medical cost burdens. Therefore, there is an increasing demand for an effective and rational screening test that can determine whether or not to undergo a PSG. In this thesis, we conducted three studies to classify the snoring sounds and OSA severity using only breathing sounds during sleep without additional biosensors. We first identified the classification possibility of snoring sounds related to sleep disorders using the features based on the cyclostationary analysis. Then, we classified the patients OSA severity with the features extracted using temporal and cyclostationary analysis from long-term sleep breathing sounds. Finally, the partial sleep sound extraction, and feature learning process using a convolutional neural network (CNN, or ConvNet) were applied to improve the efficiency and performance of previous snoring sound and OSA severity classification tasks. The sleep breathing sound analysis method using a CNN showed superior classification accuracy of more than 80% (average area under curve > 0.8) in multiclass snoring sounds and OSA severity classification tasks. The proposed analysis and classification method is expected to be used as a screening tool for improving the efficiency of PSG in the future customized healthcare service.Chapter 1. Introduction ................................ .......................1 1.1 Personal healthcare in sleep ................................ ..............1 1.2 Existing approaches and limitations ....................................... 9 1.3 Clinical information related to SRBD ................................ .. ..12 1.4 Study objectives ................................ .........................16 Chapter 2. Overview of Sleep Research using Sleep Breathing Sounds ........... 23 2.1 Previous goals of studies ................................ ................23 2.2 Recording environments and related configurations ........................ 24 2.3 Sleep breathing sound analysis ................................ ...........27 2.4 Sleep breathing sound classification ..................................... 35 2.5 Current limitations ................................ ......................36 Chapter 3. Multiple SRDB-related Snoring Sound Classification .................39 3.1 Introduction ................................ .............................39 3.2 System architecture ................................ ......................41 3.3 Evaluation ................................ ...............................52 3.4 Results ................................ ..................................55 3.5 Discussion ................................ ...............................59 3.6 Summary ................................ ..................................63 Chapter 4. Patients OSA Severity Classification .............................65 4.1 Introduction ................................ .............................65 4.2 Existing Approaches ................................ ......................69 4.3 System Architecture ................................ ......................70 4.4 Evaluation ................................ ...............................85 4.5 Results ................................ ..................................87 4.6 Discussion ................................ ...............................94 4.7 Summary ................................ ..................................97 Chapter 5. Patient OSA Severity Prediction using Deep Learning Techniques .....99 5.1 Introduction ................................ .............................99 5.2 Methods ................................ ..................................101 5.3 Results ................................ ..................................109 5.4 Discussion ................................ ...............................115 5.5 Summary ................................ ..................................118 Chapter 6. Conclusions and Future Work ........................................120 6.1 Conclusions ................................ ..............................120 6.2 Future work ................................ ..............................127Docto

Positive airway pressure and electrical stimulation methods for obstructive sleep apnea treatment: a patent review (2005-2014)

Producción CientíficaIntroduction. Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a major health problem with significant negative effects on the health and quality of life. Continuous positive airway pressure (CPAP) is currently the primary treatment option and it is considered the most effective therapy for OSAHS. Nevertheless, comfort issues due to improper fit to patient’s changing needs and breathing gas leakage limit the patient’s adherence to treatment. Areas covered. The present patent review describes recent innovations in the treatment of OSAHS related to optimization of the positive pressure delivered to the patient, methods and systems for continuous self-adjusting pressure during inspiration and expiration phases, and techniques for electrical stimulation of nerves and muscles responsible for the airway patency. Expert opinion. In the last years, CPAP-related inventions have mainly focused on obtaining an optimal self-adjusting pressure according to patient’s needs. Despite intensive research carried out, treatment compliance is still a major issue. Hypoglossal electrical nerve stimulation could be an effective secondary treatment option when CPAP primary therapy fails. Several patents have been granted focused on selective stimulation techniques and parameter optimization of the stimulating pulse waveform. Nevertheless, there remain important issues to address, like effectiveness and adverse events due to improper stimulation.Ministerio de Economía y Competitividad (TEC2011-22987)Junta de Castilla y León (VA059U13

Pattern recognition applied to airflow recordings to help in sleep Apnea-Hypopnea Syndrome diagnosis

El Síndrome de la Apnea Hipopnea del Sueño (SAHS) es un trastorno caracterizado por pausas respiratorias durante el sueño. Se considera un grave problema de salud que afecta muy negativamente a la calidad de vida y está relacionada con las principales causas de mortalidad, como los accidentes cardiovasculares y cerebrovasculares. A pesar de su elevada prevalencia (2–7%) se considera una enfermedad infradiagnosticada. El diagnóstico estándar se realiza mediante polisomnografía (PSG) nocturna, que es un método complejo y de alto coste. Estas limitaciones han originado largas listas de espera. Esta Tesis Doctoral tiene como principal objetivo simplificar la metodología de diagnóstico del SAHS . Para ello, se propone el análisis exhaustivo de la señal de flujo aéreo monocanal. La metodología propuesta se basa en tres fases (i) extracción de características, (ii) selección de características, y (iii) procesado de la señal mediante métodos de reconocimiento de patrones. Los resultados obtenidos muestran un alto rendimiento diagnóstico de la propuesta tanto en la detección como en la determinación del grado de severidad del SAHS. Por ello, la principal conclusión de la Tesis Doctoral es que los métodos de reconocimiento automático de patrones aplicados sobre la señal de flujo aéreo monocanal resultan de utilidad para reducir la complejidad del proceso de diagnóstico del SAHS.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemátic