Publications by authors named "Ronald M Aarts"

63 Publications

Pitfalls in EEG Analysis in Patients With Nonconvulsive Status Epilepticus: A Preliminary Study.

Clin EEG Neurosci 2021 Nov 1:15500594211050492. Epub 2021 Nov 1.

534522Eindhoven University of Technology, Eindhoven, the Netherlands.

Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis were still not reliable for the diagnosis of nonconvulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) was visually analyzed by 2 independent raters. We investigated whether unreliable EEG visual interpretations quantified by low interrater agreement can be predicted by the characteristics of ictal discharges and individuals' clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, 2 epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Short ictal discharges with a gradual onset (developing over 3 s in length) were liable to be misinterpreted. An extra 2 min of ictal discharges contributed to an increase in the kappa statistics of >0.1. Other problems were the misinterpretation of abnormal background activity (slow-wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. A longer duration criterion for NCSE-EEGs than 10 s that is commonly used in NCSE working criteria is recommended. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.
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http://dx.doi.org/10.1177/15500594211050492DOI Listing
November 2021

A deep transfer learning approach for wearable sleep stage classification with photoplethysmography.

NPJ Digit Med 2021 Sep 15;4(1):135. Epub 2021 Sep 15.

Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.

Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen's kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.
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http://dx.doi.org/10.1038/s41746-021-00510-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8443610PMC
September 2021

Characterizing cardiorespiratory interaction in preterm infants across sleep states using visibility graph analysis.

J Appl Physiol (1985) 2021 04 4;130(4):1015-1024. Epub 2021 Feb 4.

Department of Neonatology, Máxima Medical Centre, Veldhoven, The Netherlands.

Cardiorespiratory interaction (CRI) has been intensively studied in adult sleep, yet not in preterm infants, in particular across different sleep states including wake (W), active sleep (AS), and quiet sleep (QS). The aim of this study was to quantify the interaction between cardiac and respiratory activities in different sleep states of preterm infants. The postmenstrual age (PMA) of preterm infants was also taken into consideration. The CRI during sleep was analyzed using a visibility graph (VG) method, enabling the nonlinear analysis of CRI in a complex network. For each sleep state, parameters quantifying various aspects of the CRI characteristics from constructed VG network including mean degree () and its variability (), clustering coefficient (CC) and its variability (CC), assortativity coefficient (AC), and complexity () were extracted from the CRI networks. The interaction effect of sleep state and PMA was found to be statistically significant on all CRI parameters except for AC and . The main effect between sleep state and CRI parameters was statistically significant except for CC, and that between PMA and CRI parameters was statistically significant except for . In conclusion, the CRI of preterm infants is associated with sleep states and PMA in general. For preterm infants with a larger PMA, CRI has a more clustered pattern during different sleep states, where QS shows a more regular, stratified, and stronger CRI than other states. In the future, these parameters can be potentially used to separate sleep states in preterm infants. The interaction between cardiac and respiratory activities is investigated in preterm infant sleep using an advanced nonlinear method (visibility graph) and some important characteristics are shown to be significantly different across sleep states, which has not been studied before.
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http://dx.doi.org/10.1152/japplphysiol.00333.2020DOI Listing
April 2021

Video-based actigraphy is an effective contact-free method of assessing sleep in preterm infants.

Acta Paediatr 2021 06 12;110(6):1815-1816. Epub 2021 Jan 12.

Department of Neonatology, Máxima Medical Center, Veldhoven, The Netherlands.

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http://dx.doi.org/10.1111/apa.15740DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247861PMC
June 2021

Freezing of gait detection in Parkinson's disease via multimodal analysis of EEG and accelerometer signals.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:847-850

Parkinson's disease (PD) patients with freezing of gait (FOG) can suddenly lose their forward moving ability leading to unexpected falls. To overcome FOG and avoid the falls, a real-time accurate FOG detection or prediction system is desirable to trigger on-demand cues. In this study, we designed and implemented an in-place movement experiment for PD patients to provoke FOG and meanwhile acquired multimodal physiological signals, such as electroencephalography (EEG) and accelerometer signals. A multimodal model using brain activity from EEG and motion data from accelerometers was developed to improve FOG detection performance. In the detection of over 700 FOG episodes observed in the experiments, the multimodal model achieved 0.211 measured by Matthews Correlation Coefficient (MCC) compared with the single-modal models (0.127 or 0.139).Clinical Relevance- This is the first study to use multimodal: EEG and accelerometer signal analysis in FOG detection, and an improvement was achieved.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175288DOI Listing
July 2020

False alarms reduction in non-convulsive status epilepticus detection via continuous EEG analysis.

Physiol Meas 2020 06 10;41(5):055009. Epub 2020 Jun 10.

Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands. Academic Centre for Epilepsy Kempenhaeghe, Heeze, The Netherlands.

Objective: Frequent false alarms from computer-assisted monitoring systems may harm the safety of patients with non-convulsive status epilepticus (NCSE). In this study, we aimed at reducing false alarms in the NCSE detection based on preventing from three common errors: over-interpretation of abnormal background activity, dense short ictal discharges and continuous interictal discharges as ictal discharges.

Approach: We analyzed 10 participants' hospital-archived 127-hour electroencephalography (EEG) recordings with 310 ictal discharges. To reduce the false alarms caused by abnormal background activity, we used morphological features extracted by visibility graph methods in addition to time-frequency features. To reduce the false alarms caused by over-interpreting short ictal discharges and interictal discharges, we created two synthetic classes-'Suspected Non-ictal' and 'Suspected Ictal'-based on the misclassified categories and constructed a synthetic 4-class dataset combining the standard two classes-'Non-ictal' and 'Ictal'-to train a 4-class classifier. Precision-recall curves were used to compare our proposed 4-class classification model and the standard 2-class classification model with or without the morphological features in the leave-one-out cross validation stage. The sensitivity and precision were primarily used as performance metrics for the detection of a seizure event.

Main Results: The 4-class classification model improved the performance of the standard 2-class model, in particular increasing the precision by 15% at an 80% sensitivity level when only time-frequency features were used. Using the morphological features, the 4-class classification model achieved the best performances: a sensitivity of 93% ± 12% and a precision of 55% ± 30% in the group level. 100% accuracy was reached in a participant's 4.3-hour recording with 5 ictal discharges.

Significance: False alarms in the NCSE detection were remarkably reduced using the morphological features and the proposed 4-class classification model.
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http://dx.doi.org/10.1088/1361-6579/ab8cb3DOI Listing
June 2020

Lying Awake at Night: Cardiac Autonomic Activity in Relation to Sleep Onset and Maintenance.

Front Neurosci 2019 15;13:1405. Epub 2020 Jan 15.

Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.

Insomnia, i.e., difficulties initiating and/or maintaining sleep, is one of the most common sleep disorders. To study underlying mechanisms for insomnia, we studied autonomic activity changes around sleep onset in participants without clinical insomnia but with varying problems with initiating or maintaining sleep quantified as increased sleep onset latency (SOL) and wake after sleep onset (WASO), respectively. Polysomnography and electrocardiography were simultaneously recorded in 176 participants during a single night. Cardiac autonomic activity was assessed using frequency domain analysis of RR intervals and results show that the normalized spectral power in the low frequency band ( ) after sleep onset was significantly higher in participants with long SOL compared to participants with short SOL. Furthermore, the normalized spectral power in the high frequency band ( ) was significantly lower in participants with long SOL as compared to participants with short SOL over 3 time periods (first 10 min in bed intending to sleep, 10 min before, and 10 min after sleep onset). These results suggest that participants with long SOL are more aroused in all three examined time periods when compared to participants with short SOL, especially for young adults (20-40 years). As there is no clear consensus on the cutoff for an increased WASO, we used a data-driven approach to explore different cutoffs to define short WASO and long WASO groups. , , and / differed between the long and the short WASO groups. A higher and / and a lower was observed in participants with long WASO for most cutoffs. The highest effect size was found using the cutoff of 66 min. Our findings suggest that autonomic cardiac activity has predictive value with respect to sleep characteristics pertaining to sleep onset and maintenance.
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http://dx.doi.org/10.3389/fnins.2019.01405DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6974549PMC
January 2020

Automatic and Continuous Discomfort Detection for Premature Infants in a NICU Using Video-Based Motion Analysis.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:5995-5999

Frequent pain and discomfort in premature infants can lead to long-term adverse neurodevelopmental outcomes. Video-based monitoring is considered to be a promising contactless method for identification of discomfort moments. In this study, we propose a video-based method for automated detection of infant discomfort. The method is based on analyzing facial and body motion. Therefore, motion trajectories are estimated from frame to frame using optical flow. For each video segment, we further calculate the motion acceleration rate and extract 18 time- and frequency-domain features characterizing motion patterns. A support vector machine (SVM) classifier is then applied to video sequences to recognize infant status of comfort or discomfort. The method is evaluated using 183 video segments for 11 infants from 17 heel prick events. Experimental results show an AUC of 0.94 for discomfort detection and the average accuracy of 0.86 when combining all proposed features, which is promising for clinical use.
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http://dx.doi.org/10.1109/EMBC.2019.8857597DOI Listing
July 2019

Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data.

IEEE J Biomed Health Inform 2020 06 4;24(6):1610-1618. Epub 2019 Nov 4.

Objective: Photoplethysmography (PPG) enables unobtrusive heart rate monitoring, which can be used in wrist-worn applications. Its potential for detecting atrial fibrillation (AF) has been recently presented. Besides AF, another cardiac arrhythmia increasing stroke risk and requiring treatment is atrial flutter (AFL). Currently, the knowledge about AFL detection with PPG is limited. The objective of our study was to develop a model that classifies AF, AFL, and sinus rhythm with or without premature beats from PPG and acceleration data measured at the wrist in daily life.

Methods: A dataset of 40 patients was collected by measuring PPG and accelerometer data, as well as electrocardiogram as a reference, during 24-hour monitoring. The dataset was split into 75%-25% for training and testing a Random Forest (RF) model, which combines features from PPG, inter-pulse intervals (IPI), and accelerometer data, to classify AF, AFL, and other rhythms. The performance was compared to an AF detection algorithm combining traditional IPI features for determining the robustness of the accuracy in presence of AFL.

Results: The RF model classified AF/AFL/other with sensitivity and specificity of 97.6/84.5/98.1% and 98.2/99.7/92.8%, respectively. The results with the IPI-based AF classifier showed that the majority of false detections were caused by AFL.

Conclusion: The PPG signal contains information to classify AFL in the presence of AF, sinus rhythm, or sinus rhythm with premature contractions.

Significance: PPG could indicate presence of AFL, not only AF.
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http://dx.doi.org/10.1109/JBHI.2019.2950574DOI Listing
June 2020

Sleep stage classification from heart-rate variability using long short-term memory neural networks.

Sci Rep 2019 Oct 2;9(1):14149. Epub 2019 Oct 2.

Royal Philips, Research, High Tech Campus 34, 5656 AE, Eindhoven, The Netherlands.

Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen's k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.
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http://dx.doi.org/10.1038/s41598-019-49703-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6775145PMC
October 2019

Automated preterm infant sleep staging using capacitive electrocardiography.

Physiol Meas 2019 06 4;40(5):055003. Epub 2019 Jun 4.

Department of Electrical Engineering, University of Technology Eindhoven, Eindhoven, The Netherlands.

Objective: To date, mainly obtrusive methods (e.g. adhesive electrodes in electroencephalography or electrocardiography) have been necessary to determine the preterm infant sleep states. As any obtrusive measure should be avoided in preterm infants because of their immature skin development, we investigated the possibility of automated sleep staging using electrocardiograph signals from non-adhesive capacitive electrocardiography.

Approach: Capacitive electrocardiography data from eight different patients with a mean gestational age of 30  ±  2.5 weeks are compared to manually annotated reference signals from classic adhesive electrodes. The sleep annotations were performed by two trained observers based on behavioral observations.

Main Results: Based on these annotations, classification performance of the preterm infant active and quiet sleep states, based on capacitive electrocardiography signals, showed a kappa value of 0.56  ±  0.20. Adding wake and caretaking into the classification, a performance of kappa 0.44  ±  0.21 was achieved. In-between sleep state performance showed a classification performance of kappa 0.36  ±  0.12. Lastly, a performance for all sleep states of kappa 0.35  ±  0.17 was attained.

Significance: Capacitive electrocardiography signals can be utilized to classify the central preterm infant sleep states, active and quiet sleep. With further research based on our results, automated classification of sleep states can become an essential instrument in future intensive neonatal care for continuous brain maturation monitoring. In particular, being able to use capacitive electrocardiography for continuous monitoring is a significant contributor to reducing disruption and harm for this extremely fragile patient group.
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http://dx.doi.org/10.1088/1361-6579/ab1224DOI Listing
June 2019

Estimating blood pressure trends and the nocturnal dip from photoplethysmography.

Physiol Meas 2019 02 26;40(2):025006. Epub 2019 Feb 26.

Personal Health, Philips Research, Royal Philips, Eindhoven, The Netherlands. Signal Processing Systems, Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

Objective: Evaluate a method for the estimation of the nocturnal systolic blood pressure (SBP) dip from 24 h blood pressure trends using a wrist-worn photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines.

Approach: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 d during which 5111 reference values for blood pressure (BP) were obtained with a 24 h ambulatory BP monitor and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Long- and short term memory (LSTM) networks, dense networks, random forests and linear regression models were trained and evaluated in their capability of tracking trends in BP, as well as the estimation of the SBP dip.

Main Results: Best performance for estimating the SBP dip were obtained with a deep LSTM neural network with a root mean squared error (RMSE) of 3.12 [Formula: see text] 2.20 [Formula: see text] mmHg and a correlation of 0.69 [Formula: see text]. This dip was derived from trend estimates of BP which had an RMSE of 8.22 [Formula: see text] 1.49 mmHg for systolic and 6.55 [Formula: see text] 1.39 mmHg for diastolic BP (DBP). While other models had similar performance for the tracking of relative BP, they did not perform as well as the LSTM for the SBP dip.

Significance: The work provides first evidence for the unobtrusive estimation of the nocturnal SBP dip, a highly prognostic clinical parameter. It is also the first to evaluate unobtrusive BP measurement in a large data set of unconstrained 24 h measurements in free-living individuals and provides evidence for the utility of LSTM models in this domain.
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http://dx.doi.org/10.1088/1361-6579/ab030eDOI Listing
February 2019

Autonomic cardiac activity in adults with short and long sleep onset latency.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:1448-1451

Autonomic cardiac activity during sleep has been widely studied. Research has mostly focused on cardiac activity between different sleep stages and wakefulness as well as between normal and pathological sleep. This work investigates autonomic activity changes during sleep onset in healthy subjects with long and short sleep onset latency (SOL). Polysomnography (PSG) and electrocardiography (ECG) were simultaneously recorded in 186 healthy subjects during a single night. Autonomic activity was assessed based on frequency domain analysis of RR intervals and results show that the analysis of RR intervals differs significantly between the short SOL and the long SOL groups. We found that the spectral power in the low frequency band (LF) was significantly higher in the long SOL group compared to the short SOL group in the first 10 minutes in bed intended to sleep. There was no significant difference for LF and the spectral power in the high frequency band (HF) 10 minutes before and after sleep onset between the two groups. Only in the short SOL group there was a significant increase in HF from the first 10 minutes in bed intended to sleep to 10 minutes before SO, while LF decreased significantly in both groups. The effect of time (5.5-min bin) on the heart rate variability (HRV) features around sleep onset showed that both LF and HF differed significantly during the period surrounding sleep onset only in the short SOL group.
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http://dx.doi.org/10.1109/EMBC.2018.8512534DOI Listing
July 2018

Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo-Plethysmography at the Wrist.

J Am Heart Assoc 2018 08;7(15):e009351

2 Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands.

Background Long-term continuous cardiac monitoring would aid in the early diagnosis and management of atrial fibrillation ( AF ). This study examined the accuracy of a novel approach for AF detection using photo-plethysmography signals measured from a wrist-based wearable device. Methods and Results ECG and contemporaneous pulse data were collected from 2 cohorts of AF patients: AF patients (n=20) undergoing electrical cardioversion ( ECV ) and AF patients (n=40) that were prescribed for 24 hours ECG Holter in outpatient settings ( HOL ). Photo-plethysmography and acceleration data were collected at the wrist and processed to determine the inter-pulse interval and discard inter-pulse intervals in presence of motion artifacts. A Markov model was deployed to assess the probability of AF given irregular pattern in inter-pulse interval sequences. The AF detection algorithm was evaluated against clinical rhythm annotations of AF based on ECG interpretation. Photo-plethysmography recordings from apparently healthy volunteers (n=120) were used to establish the false positive AF detection rate of the algorithm. A total of 42 and 855 hours (AF: 21 and 323 hours) of photo-plethysmography data were recorded in the ECV and HOL cohorts, respectively. AF was detected with >96% accuracy ( ECV, sensitivity=97%; HOL , sensitivity=93%; both with specificity=100%). Because of motion artifacts, the algorithm did not provide AF classification for 44±16% of the monitoring period in the HOL group. In healthy controls, the algorithm demonstrated a <0.2% false positive AF detection rate. Conclusions A novel AF detection algorithm using pulse data from a wrist-wearable device can accurately discriminate rhythm irregularities caused by AF from normal rhythm.
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http://dx.doi.org/10.1161/JAHA.118.009351DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6201454PMC
August 2018

Comparison between electrocardiogram- and photoplethysmogram-derived features for atrial fibrillation detection in free-living conditions.

Physiol Meas 2018 08 8;39(8):084001. Epub 2018 Aug 8.

Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, Netherlands. Philips Research, Eindhoven, Netherlands.

Objective: Atrial fibrillation (AF) is the most commonly experienced arrhythmia and it increases the risk of stroke and heart failure. The challenge in detecting the presence of AF is the occasional and asymptomatic manifestation of the condition. Long-term monitoring can increase the sensitivity of detecting intermittent AF episodes, however it is either cumbersome or invasive and costly with electrocardiography (ECG). Photoplethysmography (PPG) is an unobtrusive measuring modality enabling heart rate monitoring, and promising results have been presented in detecting AF. However, there is still limited knowledge about the applicability of the PPG solutions in free-living conditions. The aim of this study was to compare the inter-beat interval derived features for AF detection between ECG and wrist-worn PPG in daily life.

Approach: The data consisted of 24 h ECG, PPG, and accelerometer measurements from 27 patients (eight AF, 19 non-AF). In total, seven features (Shannon entropy, root mean square of successive differences (RMSSD), normalized RMSSD, pNN40, pNN70, sample entropy, and coefficient of sample entropy (CosEn)) were compared. Body movement was measured with the accelerometer and used with three different thresholds to exclude PPG segments affected by movement.

Main Results: CosEn resulted as the best performing feature from ECG with Cohens kappa 0.95. When the strictest movement threshold was applied, the same performance was obtained with PPG (kappa  =  0.96). In addition, pNN40 and pNN70 reached similar results with the same threshold (kappa  =  0.95 and 0.94), but were more robust with respect to movement artefacts. The coverage of PPG was 24.0%-57.6% depending on the movement threshold compared to 92.1% of ECG.

Significance: The inter-beat interval features derived from PPG are equivalent to the ones from ECG for AF detection. Movement artefacts substantially worsen PPG-based AF monitoring in free-living conditions, therefore monitoring coverage needs to be carefully selected. Wrist-worn PPG still provides a promising technology for long-term AF monitoring.
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http://dx.doi.org/10.1088/1361-6579/aad2c0DOI Listing
August 2018

Finger and forehead photoplethysmography-derived pulse-pressure variation and the benefits of baseline correction.

J Clin Monit Comput 2019 Feb 11;33(1):65-75. Epub 2018 Apr 11.

Philips Research, Eindhoven, The Netherlands.

To non-invasively predict fluid responsiveness, respiration-induced pulse amplitude variation (PAV) in the photoplethysmographic (PPG) signal has been proposed as an alternative to pulse pressure variation (PPV) in the arterial blood pressure (ABP) signal. However, it is still unclear how the performance of the PPG-derived PAV is site-dependent during surgery. The aim of this study is to compare finger- and forehead-PPG derived PAV in their ability to approach the value and trend of ABP-derived PPV. Furthermore, this study investigates four potential confounding factors, (1) baseline variation, (2) PPV, (3) ratio of respiration and heart rate, and (4) perfusion index, which might affect the agreement between PPV and PAV. In this work, ABP, finger PPG, and forehead PPG were continuously recorded in 29 patients undergoing major surgery in the operating room. A total of 91.2 h data were used for analysis, from which PAV and PPV were calculated and compared. We analyzed the impact of the four factors using a multiple linear regression (MLR) analysis. The results show that compared with the ABP-derived PPV, finger-derived PAV had an agreement of 3.2 ± 5.1%, whereas forehead-PAV had an agreement of 12.0 ± 9.1%. From the MLR analysis, we found that baseline variation was a factor significantly affecting the agreement between PPV and PAV. After correcting for respiration-induced baseline variation, the agreements for finger- and forehead-derived PAV were improved to reach an agreement of - 1.2 ± 3.8% and 3.3 ± 4.8%, respectively. To conclude, finger-derived PAV showed better agreement with ABP-derived PPV compared to forehead-derived PAV. Baseline variation was a factor that significantly affected the agreement between PPV and PAV. By correcting for the baseline variation, improved agreements were obtained for both the finger and forehead, and the difference between these two agreements was diminished. The tracking abilities for both finger- and forehead-derived PAV still warrant improvement for wide use in clinical practice. Overall, our results show that baseline-corrected finger- and forehead-derived PAV may provide a non-invasive alternative for PPV.
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http://dx.doi.org/10.1007/s10877-018-0140-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6314999PMC
February 2019

A comparison of probabilistic classifiers for sleep stage classification.

Physiol Meas 2018 05 15;39(5):055001. Epub 2018 May 15.

Philips Research, High Tech Campus 34, 5656 AE Eindhoven, Netherlands. Department of Electrical Engineering, Eindhoven University of Technology, Postbus 513, 5600MB Eindhoven, Netherlands.

Objective: To compare conditional random fields (CRF), hidden Markov models (HMMs) and Bayesian linear discriminants (LDs) for cardiorespiratory sleep stage classification on a five-class sleep staging task (wake/N1/N2/N3/REM), to explore the benefits of incorporating time information in the classification and to evaluate the feasibility of sleep staging on obstructive sleep apnea (OSA) patients.

Approach: The classifiers with and without time information were evaluated with 10-fold cross-validation on five-, four- (wake/N1  +  N2/N3/REM) and three-class (wake/NREM/REM) classification tasks using a data set comprising 443 night-time polysomnography (PSG) recordings of 231 participants (180 healthy participants, 100 of which had a 'regular' sleep architecture, and 51 participants previously diagnosed with OSA).

Main Results: CRF with time information (CRFt) outperforms all other classifiers on all tasks, achieving a median accuracy and Cohen's κ for all participants of 62.8% and 0.44 for five classes, 68.8% and 0.47 for four classes, and 77.6% and 0.55 for three classes. An advantage was found in training classifiers, specifically for 'regular' and 'OSA' participants, achieving an improvement in classification performance for these groups. For 'regular' participants, CRFt achieved a median accuracy and Cohen's κ of 67.0% and 0.51, 70.8% and 0.53 and 81.3% and 0.62 for five-, four- and three-classes respectively, and for 'OSA' patients, of 59.9% and 0.40, 69.7% and 0.45, and 75.8% and 0.51 for five-, four- and three-classes respectively.

Significance: The results suggest that CRFt is not only better at learning and predicting more complex and irregular sleep architectures, but that it also performs reasonably well in five-class classification-the standard for sleep scoring used in clinical PSG. Additionally, and albeit with a decrease in performance when compared with healthy participants, sleep stage classification in OSA patients using cardiorespiratory features and CRFt seems feasible with reasonable accuracy.
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http://dx.doi.org/10.1088/1361-6579/aabbc2DOI Listing
May 2018

Enhancement of the Comb Filtering Selectivity Using Iterative Moving Average for Periodic Waveform and Harmonic Elimination.

J Healthc Eng 2018 1;2018:7901502. Epub 2018 Feb 1.

Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, Netherlands.

A recurring problem regarding the use of conventional comb filter approaches for elimination of periodic waveforms is the degree of selectivity achieved by the filtering process. Some applications, such as the gradient artefact correction in EEG recordings during coregistered EEG-fMRI, require a highly selective comb filtering that provides effective attenuation in the stopbands and gain close to unity in the pass-bands. In this paper, we present a novel comb filtering implementation whereby the iterative filtering application of FIR moving average-based approaches is exploited in order to enhance the comb filtering selectivity. Our results indicate that the proposed approach can be used to effectively approximate the FIR moving average filter characteristics to those of an ideal filter. A cascaded implementation using the proposed approach shows to further increase the attenuation in the filter stopbands. Moreover, broadening of the bandwidth of the comb filtering stopbands around -3 dB according to the fundamental frequency of the stopband can be achieved by the novel method, which constitutes an important characteristic to account for broadening of the harmonic gradient artefact spectral lines. In parallel, the proposed filtering implementation can also be used to design a novel notch filtering approach with enhanced selectivity as well.
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http://dx.doi.org/10.1155/2018/7901502DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5823410PMC
November 2019

On algorithms for calculating arterial pulse pressure variation during major surgery.

Physiol Meas 2017 Nov 30;38(12):2101-2121. Epub 2017 Nov 30.

Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands. Philips Research, Eindhoven, Netherlands.

Objective: Arterial pulse pressure variation (PPV) is widely used for predicting fluid responsiveness and supporting fluid management in the operating room and intensive care unit. Available PPV algorithms have been typically validated for fluid responsiveness during episodes of hemodynamic stability. Yet, little is known about the performance of PPV algorithms during surgery, where fast changes of the blood pressure may affect the robustness of the presented PPV value. This work provides a comprehensive understanding of how various existing algorithmic designs affect the robustness of the presented PPV value during surgery, and proposes additional processing for the pulse pressure signal before calculating PPV.

Approach: We recorded arterial blood pressure waveforms from 23 patients undergoing major abdominal surgery. To evaluate the performance, we designed three clinically relevant metrics. Main results and Significance: The results show that all algorithms performed well during episodes of hemodynamic stability. Moreover, it is demonstrated that the proposed processing helps improve the robustness of PPV during the entire course of surgery.
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http://dx.doi.org/10.1088/1361-6579/aa95a4DOI Listing
November 2017

Measures of cardiovascular autonomic activity in insomnia disorder: A systematic review.

PLoS One 2017 23;12(10):e0186716. Epub 2017 Oct 23.

Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

Background: Insomnia disorder is a widespread sleep disorder with a prevalence of approximately 10%. Even though the link between insomnia and cardiovascular activity is not exactly clear, it is generally assumed that cardiovascular autonomic modifications could occur as a result of sleeplessness, or, alternatively, that autonomic alterations could be an expression of a hyper-arousal state. This review investigates whether cardiovascular measures are different between insomniacs and controls.

Methods: Electronic databases were systematically searched, and 34 studies were identified. Heart rate variability features, the association of cardiac and EEG activity, physiologic complexity measures, and cardiovascular activity, assessed by measures such as pre-ejection time, blood pressure, and heart rate dynamics were studied. Given the heterogeneity of the studies, a narrative synthesis of the findings was performed.

Results: This review study found overall differences in cardiovascular activity between insomniacs and controls in most of the observational studies (21/26), while the expression of cardiovascular regulation varied between the examined insomniac groups. All the studies that investigated the association of cardiac activity and EEG power reported an altered relation between autonomic activity and EEG parameters in insomniacs.

Conclusion: Autonomic regulation tends to be consistent between insomniacs, as long as they are grouped according to their respective phenotype, as shown in the insomnia subgroup with objectively short sleep duration. Our hypothesis is that these differences in the expression of cardiovascular activity could be explained by the heterogeneity of the disorder. Therefore, the determination of insomnia phenotypes, and the study of cardiovascular measures, rather than heart rate variability alone, will give more insight into the link between insomnia and cardiovascular regulation. This study suggests that cardiovascular activity differs between insomniacs and controls. These new findings are of interest to clinicians and researchers for a more accurate insomnia assessment, and the development of personalized technological solutions in insomnia.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0186716PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5653329PMC
November 2017

Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults.

Sleep 2017 07;40(7)

Philips Group Innovation Research, Eindhoven, The Netherlands.

Study Objectives: To compare the accuracy of automatic sleep staging based on heart rate variability measured from photoplethysmography (PPG) combined with body movements measured with an accelerometer, with polysomnography (PSG) and actigraphy.

Methods: Using wrist-worn PPG to analyze heart rate variability and an accelerometer to measure body movements, sleep stages and sleep statistics were automatically computed from overnight recordings. Sleep-wake, 4-class (wake/N1 + N2/N3/REM) and 3-class (wake/NREM/REM) classifiers were trained on 135 simultaneously recorded PSG and PPG recordings of 101 healthy participants and validated on 80 recordings of 51 healthy middle-aged adults. Epoch-by-epoch agreement and sleep statistics were compared with actigraphy for a subset of the validation set.

Results: The sleep-wake classifier obtained an epoch-by-epoch Cohen's κ between PPG and PSG sleep stages of 0.55 ± 0.14, sensitivity to wake of 58.2 ± 17.3%, and accuracy of 91.5 ± 5.1%. κ and sensitivity were significantly higher than with actigraphy (0.40 ± 0.15 and 45.5 ± 19.3%, respectively). The 3-class classifier achieved a κ of 0.46 ± 0.15 and accuracy of 72.9 ± 8.3%, and the 4-class classifier, a κ of 0.42 ± 0.12 and accuracy of 59.3 ± 8.5%.

Conclusions: The moderate epoch-by-epoch agreement and, in particular, the good agreement in terms of sleep statistics suggest that this technique is promising for long-term sleep monitoring, although more evidence is needed to understand whether it can complement PSG in clinical practice. It also offers an improvement in sleep/wake detection over actigraphy for healthy individuals, although this must be confirmed on a larger, clinical population.
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http://dx.doi.org/10.1093/sleep/zsx097DOI Listing
July 2017

EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

J Neurosci Methods 2017 Oct 19;290:85-94. Epub 2017 Jul 19.

Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Philips Research, Eindhoven, The Netherlands.

Background: The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied.

New Method: A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed.

Results: A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG.

Comparison With Existing Method: A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD/h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD/h of 1.4s).

Conclusions: The proposed VGS-based features can help improve seizure detection for ID patients.
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http://dx.doi.org/10.1016/j.jneumeth.2017.07.013DOI Listing
October 2017

Unobtrusive assessment of neonatal sleep state based on heart rate variability retrieved from electrocardiography used for regular patient monitoring.

Early Hum Dev 2017 10 18;113:104-113. Epub 2017 Jul 18.

Neonatal Intensive Care Unit, Maxima Medical Center, De Run 4600, 5504 DB, Veldhoven, The Netherlands; Faculty of Health, Medicine and Life Science, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, The Netherlands. Electronic address:

As an approach of unobtrusive assessment of neonatal sleep state we aimed at an automated sleep state coding based only on heart rate variability obtained from electrocardiography used for regular patient monitoring. We analyzed active and quiet sleep states of preterm infants between 30 and 37weeks postmenstrual age. To determine the sleep states we used a nonlinear kernel support vector machine for sleep state separation based on known heart rate variability features. We used unweighted and weighted misclassification penalties for the imbalanced distribution between sleep states. The validation was performed with leave-one-out-cross-validation based on the annotations of three independent observers. We analyzed the classifier performance with receiver operating curves leading to a maximum mean value for the area under the curve of 0.87. Using this sleep state separation methods, we show that automated active and quiet sleep state separation based on heart rate variability in preterm infants is feasible.
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http://dx.doi.org/10.1016/j.earlhumdev.2017.07.004DOI Listing
October 2017

Hybrid Optical Unobtrusive Blood Pressure Measurements.

Sensors (Basel) 2017 Jul 1;17(7). Epub 2017 Jul 1.

Philips Research Eindhoven, 5656 AE Eindhoven, The Netherlands.

Blood pressure (BP) is critical in diagnosing certain cardiovascular diseases such as hypertension. Some previous studies have proved that BP can be estimated by pulse transit time (PTT) calculated by a pair of photoplethysmography (PPG) signals at two body sites. Currently, contact PPG (cPPG) and imaging PPG (iPPG) are two feasible ways to obtain PPG signals. In this study, we proposed a hybrid system (called the ICPPG system) employing both methods that can be implemented on a wearable device, facilitating the measurement of BP in an inconspicuous way. The feasibility of the ICPPG system was validated on a dataset with 29 subjects. It has been proved that the ICPPG system is able to estimate PTT values. Moreover, the PTT measured by the new system shows a correlation on average with BP variations for most subjects, which could facilitate a new generation of BP measurement using wearable and mobile devices.
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http://dx.doi.org/10.3390/s17071541DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539707PMC
July 2017

Simulated case management of home telemonitoring to assess the impact of different alert algorithms on work-load and clinical decisions.

BMC Med Inform Decis Mak 2017 01 17;17(1):11. Epub 2017 Jan 17.

Dept. of Health Professional Studies, Faculty of Health & Social Care, University of Hull, Kingston-Upon-Hull, UK.

Background: Home telemonitoring (HTM) of chronic heart failure (HF) promises to improve care by timely indications when a patient's condition is worsening. Simple rules of sudden weight change have been demonstrated to generate many alerts with poor sensitivity. Trend alert algorithms and bio-impedance (a more sensitive marker of fluid change), should produce fewer false alerts and reduce workload. However, comparisons between such approaches on the decisions made and the time spent reviewing alerts has not been studied.

Methods: Using HTM data from an observational trial of 91 HF patients, a simulated telemonitoring station was created and used to present virtual caseloads to clinicians experienced with HF HTM systems. Clinicians were randomised to either a simple (i.e. an increase of 2 kg in the past 3 days) or advanced alert method (either a moving average weight algorithm or bio-impedance cumulative sum algorithm).

Results: In total 16 clinicians reviewed the caseloads, 8 randomised to a simple alert method and 8 to the advanced alert methods. Total time to review the caseloads was lower in the advanced arms than the simple arm (80 ± 42 vs. 149 ± 82 min) but agreements on actions between clinicians were low (Fleiss kappa 0.33 and 0.31) and despite having high sensitivity many alerts in the bio-impedance arm were not considered to need further action.

Conclusion: Advanced alerting algorithms with higher specificity are likely to reduce the time spent by clinicians and increase the percentage of time spent on changes rated as most meaningful. Work is needed to present bio-impedance alerts in a manner which is intuitive for clinicians.
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http://dx.doi.org/10.1186/s12911-016-0398-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5240411PMC
January 2017

Efficient approximation of the Struve functions H occurring in the calculation of sound radiation quantities.

J Acoust Soc Am 2016 12;140(6):4154

Department of Mathematics and Computer Science, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.

The Struve functions H(z), n=0, 1, ...  are approximated in a simple, accurate form that is valid for all z≥0. The authors previously treated the case n = 1 that arises in impedance calculations for the rigid-piston circular radiator mounted in an infinite planar baffle [Aarts and Janssen, J. Acoust. Soc. Am. 113, 2635-2637 (2003)]. The more general Struve functions occur when other acoustical quantities and/or non-rigid pistons are considered. The key step in the paper just cited is to express H(z) as (2/π)-J(z)+(2/π) I(z), where J is the Bessel function of order zero and the first kind and I(z) is the Fourier cosine transform of [(1-t)/(1+t)], 0≤t≤1. The square-root function is optimally approximated by a linear function ĉt+d̂, 0≤t≤1, and the resulting approximated Fourier integral is readily computed explicitly in terms of sin z/z and (1-cos z)/z. The same approach has been used by Maurel, Pagneux, Barra, and Lund [Phys. Rev. B 75, 224112 (2007)] to approximate H(z) for all z≥0. In the present paper, the square-root function is optimally approximated by a piecewise linear function consisting of two linear functions supported by [0,t̂] and [t̂,1] with t̂ the optimal take-over point. It is shown that the optimal two-piece linear function is actually continuous at the take-over point, causing a reduction of the additional complexity in the resulting approximations of H and H. Furthermore, this allows analytic computation of the optimal two-piece linear function. By using the two-piece instead of the one-piece linear approximation, the root mean square approximation error is reduced by roughly a factor of 3 while the maximum approximation error is reduced by a factor of 4.5 for H and of 2.6 for H. Recursion relations satisfied by Struve functions, initialized with the approximations of H and H, yield approximations for higher order Struve functions.
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http://dx.doi.org/10.1121/1.4968792DOI Listing
December 2016

Comparison of cardiac magnetic resonance imaging and bio-impedance spectroscopy for the assessment of fluid displacement induced by external leg compression.

Physiol Meas 2017 01 12;38(1):15-32. Epub 2016 Dec 12.

Department of Electrical Engineering, Eindhoven University of Technology, Den Dolech 2, 5612 AZ, Eindhoven, The Netherlands.

Heart failure is marked by frequent hospital admissions, often as a consequence of pulmonary congestion. Current gold standard techniques for thoracic fluid measurement require invasive heamodynamic access and therefore they are not suitable for continuous monitoring. Changes in thoracic impedance (TI) may enable non-invasive early detection of congestion and prevention of unplanned hospitalizations. However, the usefulness of TI to assess thoracic fluid status is limited by inter-subject variability and by the lack of reliable normalization methods. Indicator dilution methods allow absolute fluid volume estimation; cardiac magnetic resonance (CMR) has been recently proposed to apply indicator dilution methods in a minimally-invasive manner. In this study, we aim to compare bio-impedance spectroscopy (BIS) and CMR for the assessment of thoracic fluid status, and to determine their ability to detect fluid displacement induced by a leg compression procedure in healthy volunteers. A pressure gradient was applied across each subject's legs for 5 min (100-60 mmHg, distal to proximal). Each subject underwent a continuous TI-BIS measurement during the procedure, and repeated CMR-based indicator dilution measurements on a 1.5 T scanner at baseline, during compression, and after pressure release. The Cole-Cole and the local density random walk models were used for parameter extraction from TI-BIS and indicator dilution measurements, respectively. Intra-thoracic blood volume index (ITBI) derived from CMR, and extracellular fluid resistance (R ) from TI-BIS, were considered as thoracic fluid status measures. Eight healthy volunteers were included in this study. An increase in ITBI of 45.2  ±  47.2 ml m was observed after the leg inflation (13.1  ±  15.1% w.r.t. baseline, p  <  0.05), while a decrease of  -0.84  ±  0.39 Ω in R (-1.7  ±  0.9% w.r.t. baseline, p  <  0.05) was observed. ITBV and R normalized by body mass index were strongly inversely correlated (r  =  -0.93, p  <  0.05). In conclusion, an acute fluid displacement to the thoracic circulation was induced in healthy volunteers. Significant changes were observed in the considered thoracic fluid measures derived from BIS and CMR. Good correlation was observed between the two measurement techniques. Further clinical studies will be necessary to prospectively evaluate the value of a combination of the two techniques for prediction of re-hospitalizations after admission for heart failure.
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http://dx.doi.org/10.1088/1361-6579/38/1/15DOI Listing
January 2017

Reduction of false arrhythmia alarms using signal selection and machine learning.

Physiol Meas 2016 08 25;37(8):1204-16. Epub 2016 Jul 25.

Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands.

In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy environment nurses can experience stress and fatigue. In addition, patient safety is compromised because reaction time of the caregivers to true alarms is reduced. The data for the algorithm development consisted of records of electrocardiogram (ECG), arterial blood pressure, and photoplethysmogram signals in which an alarm for either asystole, extreme bradycardia, extreme tachycardia, ventricular fibrillation or flutter, or ventricular tachycardia occurs. First, heart beats are extracted from every signal. Next, the algorithm selects the most reliable signal pair from the available signals by comparing how well the detected beats match between different signals based on [Formula: see text]-score and selecting the best match. From the selected signal pair, arrhythmia specific features, such as heart rate features and signal purity index are computed for the alarm classification. The classification is performed with five separate Random Forest models. In addition, information on the local noise level of the selected ECG lead is added to the classification. The algorithm was trained and evaluated with the PhysioNet/Computing in Cardiology Challenge 2015 data set. In the test set the overall true positive rates were 93 and 95% and true negative rates 80 and 83%, respectively for events with no information and events with information after the alarm. The overall challenge scores were 77.39 and 81.58.
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http://dx.doi.org/10.1088/0967-3334/37/8/1204DOI Listing
August 2016

Gradient Artefact Correction and Evaluation of the EEG Recorded Simultaneously with fMRI Data Using Optimised Moving-Average.

J Med Eng 2016 28;2016:9614323. Epub 2016 Jun 28.

Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, Netherlands; Philips Research Laboratories Eindhoven, Professor Holstlaan 4, 5656 AE Eindhoven, Netherlands.

Over the past years, coregistered EEG-fMRI has emerged as a powerful tool for neurocognitive research and correlated studies, mainly because of the possibility of integrating the high temporal resolution of the EEG with the high spatial resolution of fMRI. However, additional work remains to be done in order to improve the quality of the EEG signal recorded simultaneously with fMRI data, in particular regarding the occurrence of the gradient artefact. We devised and presented in this paper a novel approach for gradient artefact correction based upon optimised moving-average filtering (OMA). OMA makes use of the iterative application of a moving-average filter, which allows estimation and cancellation of the gradient artefact by integration. Additionally, OMA is capable of performing the attenuation of the periodic artefact activity without accurate information about MRI triggers. By using our proposed approach, it is possible to achieve a better balance than the slice-average subtraction as performed by the established AAS method, regarding EEG signal preservation together with effective suppression of the gradient artefact. Since the stochastic nature of the EEG signal complicates the assessment of EEG preservation after application of the gradient artefact correction, we also propose a simple and effective method to account for it.
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http://dx.doi.org/10.1155/2016/9614323DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4942699PMC
July 2016
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