18 research outputs found
A Robust Estimation of the Cardiorespiratory Coupling in the Presence of Abnormal Beats
No full textRespiratory sinus arrhythmia (RSA) is one of the forms of cardiorespiratory coupling. It has been suggested as a potential biomarker for diverse illnesses and conditions. In general, methods for non-invasive quantification of the RSA combine information from heart rate variability (HRV) and respiratory signals. Abnormal beats, which commonly occur in different populations, alter the reliability of the HRV and thus hinder the quantification of the RSA. To overcome this problem, several methods for detection and correction of irregular beats have been reported in literature. However, the effect of each of these methods on the quantification of the RSA is not well understood yet. For this reason, an approach that avoids this step might be useful. This paper presents an alternative based on robust regression models. For comparison purposes, an algorithm to detect and correct for irregular beats, in combination with a state-of-the-art RSA estimate, are tested. A similar performance is achieved with both approaches. These results show that the proposed robust methodology is able to capture the strength of the RSA, even when irregular beats are present, avoiding the irregularities correction step.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
Evaluation of a Commercial Ballistocardiography Sensor for Sleep Apnea Screening and Sleep Monitoring
No full textThere exists a technological momentum towards the development of unobtrusive, simple, and reliable systems for long-term sleep monitoring. An off-the-shelf commercial pressure sensor meeting these requirements is the Emfit QS. First, the potential for sleep apnea screening was investigated by revealing clusters of contaminated and clean segments. A relationship between the irregularity of the data and the sleep apnea severity class was observed, which was valuable for screening (sensitivity 0.72, specificity 0.70), although the linear relation was limited ( R 2 of 0.16). Secondly, the study explored the suitability of this commercial sensor to be merged with gold standard polysomnography data for future sleep monitoring. As polysomnography (PSG) and Emfit signals originate from different types of sensor modalities, they cannot be regarded as strictly coupled. Therefore, an automated synchronization procedure based on artefact patterns was developed. Additionally, the optimal position of the Emfit for capturing respiratory and cardiac information similar to the PSG was identified, resulting in a position as close as possible to the thorax. The proposed approach demonstrated the potential for unobtrusive screening of sleep apnea patients at home. Furthermore, the synchronization framework enabled supervised analysis of the commercial Emfit sensor for future sleep monitoring, which can be extended to other multi-modal systems that record movements during sleep.sponsorship: Agentschap Innoveren en Ondernemen (VLAIO): 150466: OSA+; Agentschap voor Innovatie door Wetenschap en Technologie (IWT): O&O HBC 2016 0184 eWatch; imec funds 2017; 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 (nr 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)|150466, Agentschap voor Innovatie door Wetenschap en Technologie (IWT)|O&O HBC 2016 0184 eWatch, imec funds 2017, European Research Council, European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Advanced Grant: BIOTENSORS|339804)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
Sleep apnea hypopnea syndrome classification in SpO2 signals using wavelet decomposition and phase space reconstruction
No full textTechnical aspects of cardiorespiratory estimation using subspace projections and cross entropy
No full textBackground.Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. Its quantification has been suggested as a biomarker to diagnose different diseases. Two state-of-the-art methods, based on subspace projections and entropy, are used to estimate the RSA strength and are evaluated in this paper. Their computation requires the selection of a model order, and their performance is strongly related to the temporal and spectral characteristics of the cardiorespiratory signals.Objective.To evaluate the robustness of the RSA estimates to the selection of model order, delays, changes of phase and irregular heartbeats as well as to give recommendations for their interpretation on each case.Approach.Simulations were used to evaluate the model order selection when calculating the RSA estimates introduced before, as well as three different scenarios that can occur in signals acquired in non-controlled environments and/or from patient populations: the presence of irregular heartbeats; the occurrence of delays between heart rate variability (HRV) and respiratory signals; and the changes over time of the phase between HRV and respiratory signals.Main results.It was found that using a single model order for all the calculations suffices to characterize RSA correctly. In addition, the RSA estimation in signals containing more than 5 irregular heartbeats in a period of 5 min might be misleading. Regarding the delays between HRV and respiratory signals, both estimates are robust. For the last scenario, the two approaches tolerate phase changes up to 54°, as long as this lasts less than one fifth of the recording duration.Significance.Guidelines are given to compute the RSA estimates in non-controlled environments and patient populations.sponsorship: BOF (C24/18/097). FWO-PhD/Postdoc grants. VLAIO (150466: OSA+). EU H2020 FETOPEN 'AMPHORA' #766456. EU H2020 MSCA-ITN-2018: 'INSPiRE-MED' #813120. EU H2020 MSCA-ITN-2018: 'INFANS' #813483. Health - SeizeIT2: 19263. This research received funding from the Flemish Government (AI Research Program). KU Leuven Stadius acknowledges the financial support of imec. Carolina Varon acknowledges the financial support of ESA, BELSPO. (BOF|C24/18/097, FWO-PhD/Postdoc grants, VLAIO|150466: OSA+, EU|766456, EU|813120, EU|813483, Health - SeizeIT2|19263, Flemish Government (AI Research Program), imec, ESA, BELSPO, Marie Curie Actions (MSCA)|813483)status: Publishe
Automatic Quality Assessment of Capacitively-Coupled Bioimpedance Signals for Respiratory Activity Monitoring
No full textsponsorship: The authors would like to thank Neide Simoes-Capela for her guiding inputs and useful advice. This work was supported by imec funds 20192020. S. Van Huffel and C. Varon were supported by the VLAIO grant 150466: OSA+ and the funding from the Flemish government (AI Research Program). (imec, VLAIO|150466: OSA+, Flemish government (AI Research Program))status: Publishe
From unsupervised to semi-supervised adversarial domain adaptation in electroencephalography-based sleep staging.
No full textObjective.The recent breakthrough of wearable sleep monitoring devices has resulted in large amounts of sleep data. However, as limited labels are available, interpreting these data requires automated sleep stage classification methods with a small need for labeled training data. Transfer learning and domain adaptation offer possible solutions by enabling models to learn on a source dataset and adapt to a target dataset.Approach.In this paper, we investigate adversarial domain adaptation applied to real use cases with wearable sleep datasets acquired from diseased patient populations. Different practical aspects of the adversarial domain adaptation framework are examined, including the added value of (pseudo-)labels from the target dataset and the influence of domain mismatch between the source and target data. The method is also implemented for personalization to specific patients.Main results.The results show that adversarial domain adaptation is effective in the application of sleep staging on wearable data. When compared to a model applied on a target dataset without any adaptation, the domain adaptation method in its simplest form achieves relative gains of 7%-27% in accuracy. The performance in the target domain is further boosted by adding pseudo-labels and real target domain labels when available, and by choosing an appropriate source dataset. Furthermore, unsupervised adversarial domain adaptation can also personalize a model, improving the performance by 1%-2% compared to a non-personalized model.Significance.In conclusion, adversarial domain adaptation provides a flexible framework for semi-supervised and unsupervised transfer learning. This is particularly useful in sleep staging and other wearable electroencephalography applications. (Clinical trial registration number: S64190.)
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
