679 research outputs found
A Panoramic Study of Obstructive Sleep Apnea Detection Technologies
This study offers a literature research reference value for bioengineers and practitioner medical doctors. It could reduce research time and improve medical service efficiency regarding Obstructive Sleep Apnea (OSA) detection systems. Much of the past and the current apnea research, the vital signals features and parameters of the SA automatic detection are introduced.The applications for the earlier proposed systems and the related work on real-time and continuous monitoring of OSA and the analysis is given. The study concludes with an assessment of the current technologies highlighting their weaknesses and strengths which can set a roadmap for researchers and clinicians in this rapidly developing field of study
Exploring the Spectral Information of Airflow Recordings to Help in Pediatric Obstructive Sleep Apnea-Hypopnea Syndrome Diagnosis
Producción CientíficaThis work aims at studying the usefulness of
the spectral information contained in airflow (AF) recordings
in the context of Obstructive Sleep Apnea-Hypopnea
Syndrome (OSAHS) in children. To achieve this goal, we
defined two spectral bands of interest related to the
occurrence of apneas and hypopneas. We characterized these
bands by extracting six common spectral features from each
one. Two out of the 12 features reached higher diagnostic
ability than the 3% oxygen desaturation index (ODI3), a
clinical parameter commonly used as screener for OSAHS.
Additionally, the stepwise logistic regression (SLR) featureselection
algorithm showed that the information contained in
the two bands was complementary, both between them and
with ODI3. Finally, the logistic regression method involving
spectral features from the two bands, as well as ODI3,
achieved high diagnostic performance after a bootstrap
validation procedure (84.6±9.6 sensitivity, 87.2±9.1
specificity, 85.8±5.2 accuracy, and 0.969±0.03 area under
ROC curve). These results suggest that the spectral
information from AF is helpful to detect OSAHS in childrenMinisterio de Economía y Competitividad (TEC2011-22987)Junta de Castilla y León (VA059U13
Diseño y evaluación de metodologías de análisis automático de la oximetría nocturna como método simplificado de detección del síndrome de apnea-hipopnea obstructiva del sueño en niños. Validación en el hospital y en el domicilio.
El síndrome de apnea-hipopnea obstructiva del sueño (SAHOS) es una enfermedad
de alta prevalencia en la población infantil, con una importante morbilidad y elevado
impacto sociosanitario, en la que la detección precoz es esencial para iniciar un adecuado
tratamiento, el cual debe ser siempre individualizado. El SAHOS es una alteración
fisiopatológica compleja y multifactorial, en la que no sólo influye una susceptibilidad
genética e individual (factores anatómicos y dinámicos), sino también de estilo de vida. Los
factores de riesgo más frecuentes son la hipertrofia adenoamigdalar y la obesidad. Los
síntomas en los niños son escasos, son principalmente nocturnos y requieren un alto nivel
de sospecha. El SAHOS no diagnosticado o no tratado se relaciona con diferentes
consecuencias metabólicas, cardiovasculares, neurocognitivas, inflamatorias, conductuales
y falta de desarrollo estaturoponderal, lo que conduce a un empeoramiento del estado de
salud en términos generales y disminución de calidad de vida.Departamento de Anatomía y RadiologíaDoctorado en Investigación en Ciencias de la Salu
Entropy analysis of acoustic signals recorded with a smartphone for detecting apneas and hypopneas: A comparison with a commercial system for home sleep apnea diagnosis
Obstructive sleep apnea (OSA) is a prevalent disease, but most patients remain undiagnosed and untreated. Here we propose analyzing smartphone audio signals for screening OSA patients at home. Our objectives were to: (1) develop an algorithm for detecting silence events and classifying them into apneas or hypopneas; (2) evaluate the performance of this system; and (3) compare the information provided with a type 3 portable sleep monitor, based mainly on nasal airflow. Overnight signals were acquired simultaneously by both systems in 13 subjects (3 healthy subjects and 10 OSA patients). The sample entropy of audio signals was used to identify apnea/hypopnea events. The apnea-hypopnea indices predicted by the two systems presented a very high degree of concordance and the smartphone correctly detected and stratified all the OSA patients. An event-by-event comparison demonstrated good agreement between silence events and apnea/hypopnea events in the reference system (Sensitivity = 76%, Positive Predictive Value = 82%). Most apneas were detected (89%), but not so many hypopneas (61%). We observed that many hypopneas were accompanied by snoring, so there was no sound reduction. The apnea/hypopnea classification accuracy was 70%, but most discrepancies resulted from the inability of the nasal cannula of the reference device to record oral breathing. We provided a spectral characterization of oral and nasal breathing to correct this effect, and the classification accuracy increased to 82%. This novel knowledge from acoustic signals may be of great interest for clinical practice to develop new non-invasive techniques for screening and monitoring OSA patients at homePeer ReviewedPostprint (published version
Statistical and Nonlinear Analysis of Oximetry from Respiratory Polygraphy to Assist in the Diagnosis of Sleep Apnea in Children
Producción CientíficaObstructive Sleep Apnea-Hypopnea Syndrome
(OSAHS) is a sleep related breathing disorder that has
important consequences in the health and development of
infants and young children. To enhance the early detection of
OSAHS, we propose a methodology based on automated
analysis of nocturnal blood oxygen saturation (SpO2) from
respiratory polygraphy (RP) at home. A database composed of
50 SpO2 recordings was analyzed. Three signal processing
stages were carried out: (i) feature extraction, where statistical
features and nonlinear measures were computed and combined
with conventional oximetric indexes, (ii) feature selection using
genetic algorithms (GAs), and (iii) feature classification through
logistic regression (LR). Leave-one-out cross-validation (loo-cv)
was applied to assess diagnostic performance. The proposed
method reached 80.8% sensitivity, 79.2% specificity, 80.0%
accuracy and 0.93 area under the ROC curve (AROC), which
improved the performance of single conventional indexes. Our
results suggest that automated analysis of SpO2 recordings from
at-home RP provides essential and complementary information
to assist in OSAHS diagnosis in children.Ministerio de Economía y Competitividad (TEC2011-22987)Fundación General CSIC (Proyecto Cero 2011 sobre Envejecimiento)Obra social de la Caixa y CSICJunta de Castilla y León (VA059U13
Nocturnal Oximetry-based Evaluation of Habitually Snoring Children
Rationale: The vast majority of children around the world
undergoing adenotonsillectomy for obstructive sleep
apnea–hypopnea syndrome (OSA) are not objectively diagnosed by
nocturnal polysomnography because of access availability and cost
issues. Automated analysis of nocturnal oximetry (nSpO2), which is
readily and globally available, could potentially provide a reliable and
convenient diagnostic approach for pediatric OSA.
Methods: DeidentifiednSpO2 recordings froma total of 4,191 children
originating from13 pediatric sleep laboratories around the worldwere
prospectively evaluated after developing and validating an automated
neural network algorithm using an initial set of single-channel nSpO2
recordings from 589 patients referred for suspected OSA.
Measurements and Main Results: The automatically
estimated apnea–hypopnea index (AHI) showed high
agreement with AHI from conventional polysomnography
(intraclass correlation coefficient, 0.785) when tested in 3,602
additional subjects. Further assessment on the widely used AHI
cutoff points of 1, 5, and 10 events/h revealed an incremental
diagnostic ability (75.2, 81.7, and 90.2% accuracy; 0.788, 0.854, and
0.913 area under the receiver operating characteristic curve,
respectively).
Conclusions: Neural network–based automated analyses of
nSpO2 recordings provide accurate identification of OSA
severity among habitually snoring children with a high pretest
probability of OSA. Thus, nocturnal oximetry may enable a
simple and effective diagnostic alternative to nocturnal
polysomnography, leading to more timely interventions and
potentially improved outcomes.Supported in part by project VA037 U16 from the Consejer´ıa de Educacio´ n de la Junta de Castilla y Leo´ n and the European Regional Development Fund (FEDER), project RTC-2015-3446-1 from the Ministerio de Econom´ıa y Competitividad and FEDER, and project 153/2015 of the Sociedad Espan˜ ola de Neumolog´ıa y Cirug´ıa Tora´ cica (SEPAR). L.K.-G. is supported by NIH grant 1R01HL130984. M.F.P. was supported by a Fellowship Educational grant award from the Kingdom of Saudi Arabia. D.´A. was in receipt of a Juan de la Cierva grant from the Ministerio de Econom´ıa y Competitividad. The funders played no role in the study design, data collection, data analysis, interpretation, and writing of the manuscript
Nocturnal Oximetry-based Evaluation of Habitually Snoring Children
Rationale: The vast majority of children around the world
undergoing adenotonsillectomy for obstructive sleep
apnea–hypopnea syndrome (OSA) are not objectively diagnosed by
nocturnal polysomnography because of access availability and cost
issues. Automated analysis of nocturnal oximetry (nSpO2), which is
readily and globally available, could potentially provide a reliable and
convenient diagnostic approach for pediatric OSA.
Methods: DeidentifiednSpO2 recordings froma total of 4,191 children
originating from13 pediatric sleep laboratories around the worldwere
prospectively evaluated after developing and validating an automated
neural network algorithm using an initial set of single-channel nSpO2
recordings from 589 patients referred for suspected OSA.
Measurements and Main Results: The automatically
estimated apnea–hypopnea index (AHI) showed high
agreement with AHI from conventional polysomnography
(intraclass correlation coefficient, 0.785) when tested in 3,602
additional subjects. Further assessment on the widely used AHI
cutoff points of 1, 5, and 10 events/h revealed an incremental
diagnostic ability (75.2, 81.7, and 90.2% accuracy; 0.788, 0.854, and
0.913 area under the receiver operating characteristic curve,
respectively).
Conclusions: Neural network–based automated analyses of
nSpO2 recordings provide accurate identification of OSA
severity among habitually snoring children with a high pretest
probability of OSA. Thus, nocturnal oximetry may enable a
simple and effective diagnostic alternative to nocturnal
polysomnography, leading to more timely interventions and
potentially improved outcomes.Supported in part by project VA037 U16 from the Consejer´ıa de Educacio´ n de la Junta de Castilla y Leo´ n and the European Regional Development Fund (FEDER), project RTC-2015-3446-1 from the Ministerio de Econom´ıa y Competitividad and FEDER, and project 153/2015 of the Sociedad Espan˜ ola de Neumolog´ıa y Cirug´ıa Tora´ cica (SEPAR). L.K.-G. is supported by NIH grant 1R01HL130984. M.F.P. was supported by a Fellowship Educational grant award from the Kingdom of Saudi Arabia. D.´A. was in receipt of a Juan de la Cierva grant from the Ministerio de Econom´ıa y Competitividad. The funders played no role in the study design, data collection, data analysis, interpretation, and writing of the manuscript
Overnight oximetry as a screening tool for moderate to severe obstructive sleep apnoea in South African children
Background. Obstructive sleep apnoea (OSA) is common in children yet often overlooked, as symptom-based screening is unreliable. Polysomnography is regarded as the gold standard for the diagnosis of OSA, but is not widely available in South Africa (SA). Overnight oximetry is a validated screening tool for OSA.Objectives. To describe the impact and utility of overnight oximetry at a tertiary children’s hospital in SA.Methods. A retrospective descriptive study was conducted of patients screened for OSA by overnight oximetry at a paediatric referral hospital from December 2012 to December 2014. Clinical data were retrieved from the oximetry database and medical records. Recordings of ≥6 hours were considered adequate and included in the study. OSA severity was determined using the McGill score. Details on management and outcome were documented.Results. Oximetry studies in 137 of 153 patients were suitable for analysis (88 males (64.2%), median age 31.4 months (interquartile range (IQR) 15.8 - 65.8). Adenotonsillar hypertrophy was common (n=97, 70.8%), and 65 children (47.4%) had two or more underlying OSA risk factors. McGill’s score classified patients as follows: no/mild OSA n=55 (40.1%), moderate OSA n=23 (16.8%), severe OSA n=23 (16.8%) and very severe OSA n=36 (26.3%). Male gender, adenotonsillar hypertrophy and a lower weight-for-age z-score (–1.3 v. –0.7; p=0.038) were associated with severe to very severe OSA. Seventy-eight children (56.9%) were referred for surgery, 33 (24.1%) receiving urgent surgery within a median of 6 days (IQR 4 - 12). In contrast, 59 children (43.1%) with suspected OSA did not require surgical intervention.Conclusions. Overnight oximetry is a simple low-cost tool to assess severity of OSA and prioritise appropriate OSA management in resource-constrained settings such as SA
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