28 research outputs found
Multimodale signaalanalyse voor de onopvallende karakterisatie van obstructieve slaapapneu
No full textObstructive sleep apnea (OSA) is the most prevalent sleep related breathing disorder, nevertheless subjects suffering from it often remain undiagnosed due to the cumbersome diagnosis procedure. Moreover, the prevalence of OSA is increasing and a better phenotyping of patients is needed in order to prioritize treatment. The goal of this thesis was to tackle those challenges in OSA diagnosis, by means of advanced signal processing algorithms, proposed in this thesis. Additionally, two main algorithmic contributions, which are generally applicable were proposed. The binary interval coded scoring algorithm was extended to multilevel problems and novel monotonicity constraints were introduced. Moreover, improvements to the random-forest based feature selection were proposed including the use of the Cohen's kappa value, patient independent validation, and further feature pruning steered by the correlation between features.
The first part of this thesis focused on the development of reliable, multimodal OSA screening methods based on unobtrusive measurements such as oxygen saturation (SpO2), electrocardiography (ECG), pulse photoplethysmography (PPG), and respiratory measures. The novel SpO2 model was the best performing OSA screening method, obtaining accuracies of over 88%, outperforming most of the state-of-the-art algorithms. Different multimodal OSA detection approaches were explored, but this performance could not be further improved. Finally, a main contribution of this PhD was to test the developed ECG and PPG OSA detection algorithms on unobtrusive signals, including capacitively-coupled ECG and bioimpedance, and wearable PPG recordings. Although these experiments showed promising results, the limitations of the current algorithms on the unobtrusive data were also highlighted.
In the second part of this PhD a contribution towards a better characterization of OSA patients beyond the AHI was proposed. Novel pulse oximetry markers were developed and investigated to assess the cardiovascular status of OSA subjects. It was found that patients with cardiovascular comorbidities experienced more severe oxygen desaturations and incomplete resaturations to the baseline SpO2 values. The novel multilevel interval coded scoring was used to train a model to predict the cardiovascular status of OSA patients based on the age, BMI and the SpO2 parameters. The final model obtained good classification performances on a clinical population, but the predictive power of this model should be further validated.status: Publishe
A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG
No full textCardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 one-min, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems.Signal Processing System
Unobtrusive, through-clothing ECG and Bioimpedance Monitoring in Sleep Apnea Patients
No full textA real-life validation of a system for simultaneous acquisition of capacitively-coupled ECG (ccECG) and capacitively-coupled bioimpedance (ccBioz) is presented. The heart rate (HR) and respiration rate (RR) estimation performance was evaluated using polysomnography (PSG) signals as ground-truth, in recordings from 28 patients with suspected obstructive sleep apnea (OSA). A ccECG beat detection sensitivity of 98.4% and an R-R interval mean absolute error (MAE) of 17.1 ms were achieved when applying quality-based algorithms. RR MAE values of 3.48 and 6.37 breaths per minute were also achieved when using two different RR extraction methods. High similarity between unobtrusive signals and PSG ground-truth was observed, with a correlation between ccECG and psgECG of 91.5% and a correlation between ccBioz and PSG thoracic belt (TB) of 89.5%. Even in episodes containing OSA events, the characteristic respiration behavior of TB signals was also observed in the ccBioz signals. This shows the potential of ccECG and ccBioz for use in long-term monitoring without adding discomfort to the patient or user. Sleep-related applications as well as more generic cardiorespiratory monitoring in (patient) beds are obvious applications, but also other daily life monitoring can be done using a similar approach (e.g. in seats).Signal Processing System
Sleep-Wake Classification for Home Monitoring of Sleep Apnea Patients
No full textSleep apnea is a common sleep disorder, whose diagnosis can strongly benefit from home-based screening. As the total sleep time is essential to assess the sleep apnea severity, a sleep-wake classifier was developed based on heart rate and respiration. These two signals were selected as they can be measured using unobtrusive sensors. A 1D convolutional neural network (CNN) was designed to classify 30s epochs of tachograms and respiratory inductance plethysmography (RIP) signals. The input based on beat-to-beat variability allows the use of different sensor types. A dataset of 56 patients with an apnea-hypopnea index (AHI) below 10 was used to train and validate the network. This CNN was applied to an independent test set of ECG and RIP signals of 25 subjects. Of these, 8 subjects were simultaneously monitored using an unobtrusive capacitive-coupled ECG (ccECG) sensor integrated in a mattress. Artefact removal and data correction was performed on this acquired data. The performance on the independent dataset of ECG and RIP is comparable to state-of-the-art, with ? = 0.48. However, application on the ccECG data resulted in a drop in performance, with ? = 0.30. This was caused by a low amount of remaining wake epochs after data cleaning. Importantly, the network classified 30s segments of sleep apnea patients, without relying on past or future information for feature extraction.Signal Processing System
Detection and Classification of Sleep Apnea and Hypopnea using PPG and SpO2 signals
No full textIn this work, a detection and classification method for sleep apnea and hypopnea, using photopletysmography (PPG) and peripheral oxygen saturation (SpO 2) signals, is proposed. The detector consists of two parts: one that detects reductions in amplitude fluctuation of PPG (DAP)and one that detects oxygen desaturations. To further differentiate among sleep disordered breathing events (SDBE), the pulse rate variability (PRV) was extracted from the PPG signal, and then used to extract features that enhance the sympatho-vagal arousals during apneas and hypopneas. A classification was performed to discriminate between central and obstructive events, apneas and hypopneas. The algorithms were tested on 96 overnight signals recorded at the UZ Leuven hospital, annotated by clinical experts, and from patients without any kind of co-morbidity. An accuracy of 75.1% for the detection of apneas and hypopneas, in one-minute segments,was reached. The classification of the detected events showed 92.6% accuracy in separating central from obstructive apnea, 83.7% for central apnea and central hypopnea and 82.7% for obstructive apnea and obstructive hypopnea. The low implementation cost showed a potential for the proposed method of being used as screening device, in ambulatory scenarios.sponsorship: Manuscript received April 20, 2020; revised July 28, 2020; accepted September 20, 2020. Date of publication September 30, 2020; date of current version April 21, 2021. This work was partly supported by Ecole Doctorale MathSTIC (University of Rennes 1, France) through a scholarship attributed to the first author for the internship with the BSICoS group (University of Zaragoza, Spain). (Corresponding author: Guy Carrault.) (Ecole Doctorale MathSTIC (University of Rennes 1, France))status: Publishe
Feature Selection Algorithm based on Random Forest applied to Sleep Apnea Detection
No full textThis paper presents a new feature selection method based on the changes in out-of-bag (OOB) Cohen kappa values of a random forest (RF) classifier, which was tested on the automatic detection of sleep apnea based on the oxygen saturation signal (SpO2). The feature selection method is based on the RF predictor importance defined as the increase in error when features are permuted. This method is improved by changing the classification error into the Cohen kappa value, by adding an extra factor to avoid correlated features and by adapting the OOB sample selection to obtain a patient independent validation. When applying the method for sleep apnea classification, an optimal feature set of 3 parameters was selected out of 286. This was half of the 6 features that were obtained in our previous study. This feature reduction resulted in an improved interpretability of our model, but also a slight decrease in performance, without affecting the clinical screening performance. Feature selection is an important issue in machine learning and especially biomedical informatics. This new feature selection method introduces interesting improvements of RF feature selection methods, which can lead to a reduced feature set and an improved classifier interpretability.sponsorship: This work was supported by: Agentschap Innoveren en Ondernemen (VLAIO) Project #: SWT 150466 -OSA+. imec funds 2017. imec ICON projects: ICON HBC.2016.0167. European Research Council: The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Advanced Grant: BIOTENSORS (n 339804). This paper reflects only the authors' views and the Union is not liable for any use that may be made of the contained information. Carolina Varon is a postdoctoral fellow of the Research Foundation-Flanders (FWO). (Agentschap Innoveren en Ondernemen (VLAIO)|SWT 150466 -OSA+, European Research Council, European Research Council under the European Union|339804)status: Publishe
Assessing Cardiovascular Comorbidities in sleep apnea patients using Sp02
No full text© 2017 IEEE Computer Society. All rights reserved. Several studies have demonstrated the relationship between Obstructive Sleep Apnea Syndrome (OSAS) and cardiovascular comorbidities. It is even suggested that timely OSAS treatment can prevent the development of such comorbidities. Hence, it is important to identify the patients with a high risk for cardiovascular comorbidities and prioritize their treatment. This study investigates if the blood oxygen saturation (SpO2) signal could be used to assess the cardiovascular status of the patient. This on its turn can improve the phenotyping of OSAS patients. SpO2signals from 100 OSAS patients, of which half have a known cardiovascular comorbidity, are investigated. The individual oxygen desaturations are extracted and these desaturations are classified as caused by a respiratory event or not. This classification is then used to compute patient averaged features of apneic and non-apneic desaturations. The most discriminative features to differentiate between patients with and without cardiac comorbidity are selected. Using these, a Leastsquares Support Vector Machine (LS-SVM) classifier reached an accuracy of 76.7 % on separating test set patients according to their cardiac comorbidity status. These results suggest that the analysis of the SpO2signal has an added value in the assessment of the cardiovascular risk of OSAS patients.sponsorship: [
"This work is supported by: Agentschap voor Innovatie door Wetenschap en Technologie (IWT) Project #:",
"SWT 150466 - OSA+. imec funds 2017. imec ICON projects: ICON HBC.2016.0167. European Research Council: The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC Advanced Grant: BIOTENSORS (no 339804). This paper reflects only the authors' views and the Union is not liable for any use that may be made of the contained information."
] (Agentschap voor Innovatie door Wetenschap en Technologie (IWT)|SWT 150466, Agentschap voor Innovatie door Wetenschap en Technologie (IWT)|HBC.2016.0167, European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC Advanced Grant: BIOTENSORS|339804)status: Publishe
Automatic screening of sleep apnea patients based on the sp02 signal
No full textOBJECTIVE: This paper presents a methodology to automatically screen for sleep apnea based on the detection of apnea and hypopnea events in the blood oxygen saturation (SpO2) signal. METHODS: It starts by detecting all desaturations in the SpO2 signal. From these desaturations, a total of 143 time-domain features are extracted. After feature selection, the six most discriminative features are used to construct classifiers to predict if desaturations are caused by respiratory events. From these, a random forest classifier yielded the best classification performance. The number of desaturations, classified as caused by respiratory events per hour of recording, can then be used as an estimate of the apnea-hypopnea index (AHI), and to predict whether or not a patient suffers from sleep apnea-hypopnea syndrome (SAHS). All classifiers were developed based on a subset of 500 subjects of the Sleep Heart Health Study (SHHS) and tested on three different datasets, containing 8052 subjects in total. RESULTS: An averaged desaturation classification accuracy of 82.8% was achieved over the different test sets. Subjects having SAHS with an AHI greater than 15 can be detected with an average accuracy of 87.6%. CONCLUSION: The achieved SAHS screening outperforms SpO2 methods from the literature on the SHHS test dataset. Moreover, the robustness of the method was shown when tested on different independent test sets. SIGNIFICANCE: These results show that an algorithm based on simple features of SpO2 desaturations can outperform more elaborate methods in the detection of apneic events and the screening of SAHS patients.sponsorship: This work was supported in part by Bijzonder Onderzoeksfonds KU Leuven (BOF) Center of Excellence (CoE) #: PFV/10/002 (OPTEC); in part by SPARKLE Sensor-based Platformfor the Accurate and Remote monitoring of Kinematics Linked to E-health #: IDO-13-0358; in part by The effect of perinatal stress on the later outcome in preterm babies #: C24/15/036; in part by TARGID - Development of a novel diagnostic medical device to assess gastric motility #: C32-16-00364; in part by Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (FWO) Project #: G.0A5513N (Deep brain stimulation); in part by Agentschap voor Innovatie door Wetenschap en Technologie (IWT) Project #: SWT 150466 - OSA+, O&O HBC 2016 0184 eWatch; in part by imec funds 2017. imec ICON projects ICON HBC. 2016.0167, 'SeizeIT'; in part by Belgian Federal Science Policy Office IUAP #P7/19/(DYSCO, 'Dynamical systems, control and optimization', 2012-2017); in part by Belgian Foreign Affairs-Development Cooperation VLIR UOS programs (2013-2019); in part by EU: European Union's Seventh Framework Programme (FP7/2007-2013); in part by EU MC ITN TRANSACT 2012, #316679; in part by The HIP Trial: #260777; in part by ERASMUS + INGDIVS 2016-1-SE01-KA203-022114; in part by the European Research Council; and in part by the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Advanced BIOTENSORS (339804). Carolina Varon is a postdoctoral fellow of the Research Foundation-Flanders (FWO). (Bijzonder Onderzoeksfonds KU Leuven (BOF) Center of Excellence (CoE)|PFV/10/002, SPARKLE Sensor-based Platformfor the Accurate and Remote monitoring of Kinematics Linked to E-health|IDO-13-0358, effect of perinatal stress on the later outcome in preterm babies|C24/15/036, TARGID - Development of a novel diagnostic medical device to assess gastric motility|C32-16-00364, Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (FWO) Project|G.0A5513N, Agentschap voor Innovatie door Wetenschap en Technologie (IWT) Project|SWT 150466 - OSA+, Agentschap voor Innovatie door Wetenschap en Technologie (IWT) Project|O&O HBC 2016 0184 eWatch, imec funds 2017, imec ICON projects|ICON HBC. 2016.0167, Belgian Federal Science Policy Office IUAP|P7/19, Belgian Foreign Affairs-Development Cooperation VLIR UOS programs (2013-2019), EU: European Union's Seventh Framework Programme (FP7/2007-2013), EU MC ITN TRANSACT 2012|316679, HIP Trial|260777, ERASMUS + INGDIVS|2016-1-SE01-KA203-022114, European Research Council, European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Advanced BIOTENSORS|339804)status: Publishe
Are we training our heartbeat classification algorithms properly?
No full textDespite the multiple studies dealing with heartbeat classification, the accurate detection of Supraventricular heartbeats (SVEB) is still very challenging. Therefore, this study aims to question the current protocol followed to report heartbeat classification results, which impedes the improvement of the SVEB class without falling on over-fitting. In this study, a novel approach based on Variational Mode Decomposition (VMD) as source of features is proposed, and the impact of the use of the MIT-BIH Arrhythmia database is analyzed.The method proposed is based on single-lead electrocardiogram, and it characterizes heartbeats by a set of 45 features: 5 related to the time intervals between consecutive heartbeats, and the rest related to VMD. Each heartbeat is decomposed in their variational modes, which are, on their turn, characterized by their frequency content, morphology and higher order statistics. The 10 most relevant features are selected using a backwards wrapper feature selector, and they are fed into an LS-SVM classifier, which is trained to separate Normal (N), Supraventricular (SVEB), Ventricular (VEB) and Fusion (F) heartbeats. An inter-patient approach, using patient independent training, is considered as suggested in the literature.The method achieves sensitivities above 80% for the three most important classes of the database (N, SVEB and VEB), and high specificities for the N and VEB classes. Given the challenges related to the SVEB and F class present in the literature, the composition of the MIT-BIH database is analyzed and alternatives are suggested in order to train heartbeat classification algorithms in a novel and more realistic way.sponsorship: Agentschap Innoveren & Ondernemen (VLAIO): STW 150466 OSA+. imec funds 2017. C. Varon is a postdoctoral fellow of the Research Foundation-Flanders (FWO). R. Willems is supported as postdoctoral clinical researcher by the Fund for Scientific Research Flanders. (Agentschap Innoveren & Ondernemen (VLAIO): OSA+. imec funds 2017|STW 150466 OSA+, Fund for Scientific Research Flanders, imec funds 2017)status: Publishe
Sleep apnea hypopnea syndrome classification in SpO2 signals using wavelet decomposition and phase space reconstruction
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