Publications by authors named "Johannes P van Dijk"

35 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

It is All in the Wrist: Wearable Sleep Staging in a Clinical Population versus Reference Polysomnography.

Nat Sci Sleep 2021 28;13:885-897. Epub 2021 Jun 28.

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

Purpose: There is great interest in unobtrusive long-term sleep measurements using wearable devices based on reflective photoplethysmography (PPG). Unfortunately, consumer devices are not validated in patient populations and therefore not suitable for clinical use. Several sleep staging algorithms have been developed and validated based on ECG-signals. However, translation from these techniques to data derived by wearable PPG is not trivial, and requires the differences between sensing modalities to be integrated in the algorithm, or having the model trained directly with data obtained with the target sensor. Either way, validation of PPG-based sleep staging algorithms requires a large dataset containing both gold standard measurements and PPG-sensor in the applicable clinical population. Here, we take these important steps towards unobtrusive, long-term sleep monitoring.

Methods: We developed and trained an algorithm based on wrist-worn PPG and accelerometry. The method was validated against reference polysomnography in an independent clinical population comprising 244 adults and 48 children (age: 3 to 82 years) with a wide variety of sleep disorders.

Results: The classifier achieved substantial agreement on four-class sleep staging with an average Cohen's kappa of 0.62 and accuracy of 76.4%. For children/adolescents, it achieved even higher agreement with an average kappa of 0.66 and accuracy of 77.9%. Performance was significantly higher in non-REM parasomnias (kappa = 0.69, accuracy = 80.1%) and significantly lower in REM parasomnias (kappa = 0.55, accuracy = 72.3%). A weak correlation was found between age and kappa ( = -0.30, p<0.001) and age and accuracy ( = -0.22, p<0.001).

Conclusion: This study shows the feasibility of automatic wearable sleep staging in patients with a broad variety of sleep disorders and a wide age range. Results demonstrate the potential for ambulatory long-term monitoring of clinical populations, which may improve diagnosis, estimation of severity and follow up in both sleep medicine and research.
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http://dx.doi.org/10.2147/NSS.S306808DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253894PMC
June 2021

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram.

Sensors (Basel) 2021 Jan 15;21(2). Epub 2021 Jan 15.

Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.

A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0-20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, -value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.
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http://dx.doi.org/10.3390/s21020573DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7829983PMC
January 2021

Camera-Based Vital Signs Monitoring During Sleep - A Proof of Concept Study.

IEEE J Biomed Health Inform 2021 05 11;25(5):1409-1418. Epub 2021 May 11.

Polysomnography (PSG) is the current gold standard for the diagnosis of sleep disorders. However, this multi-parametric sleep monitoring tool also has some drawbacks, e.g. it limits the patient's mobility during the night and it requires the patient to come to a specialized sleep clinic or hospital to attach the sensors. Unobtrusive techniques for the detection of sleep disorders such as sleep apnea are therefore gaining increasing interest. Remote photoplethysmography using video is a technique which enables contactless detection of hemodynamic information. Promising results in near-infrared have been reported for the monitoring of sleep-relevant physiological parameters pulse rate, respiration and blood oxygen saturation. In this study we validate a contactless monitoring system on eight patients with a high likelihood of relevant obstructive sleep apnea, which are enrolled for a sleep study at a specialized sleep center. The dataset includes 46.5 hours of video recordings, full polysomnography and metadata. The camera can detect pulse and respiratory rate within 2 beats/breaths per minute accuracy 92% and 91% of the time, respectively. Estimated blood oxygen values are within 4 percentage points of the finger-oximeter 89% of the time. These results demonstrate the potential of a camera as a convenient diagnostic tool for sleep apnea, and sleep disorders in general.
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http://dx.doi.org/10.1109/JBHI.2020.3045859DOI Listing
May 2021

Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography.

Sensors (Basel) 2020 Aug 29;20(17). Epub 2020 Aug 29.

Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.

Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e., KR2 and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method.
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http://dx.doi.org/10.3390/s20174890DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506664PMC
August 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

Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population.

Sleep 2020 09;43(9)

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

Study Objectives: To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients.

Methods: We applied a previously designed algorithm for automatic sleep staging using long short-term memory recurrent neural networks to model sleep architecture. The classifier uses 132 HRV features computed from electrocardiography and activity counts from accelerometry. We retrained our algorithm using two public datasets containing both healthy sleepers and sleep disordered patients. We then tested the performance of the algorithm on an independent hold-out validation set of sleep recordings from a wide range of sleep disorders collected in a tertiary sleep medicine center.

Results: The classifier achieved substantial agreement on four-class sleep staging (wake/N1-N2/N3/rapid eye movement [REM]), with an average κ of 0.60 and accuracy of 75.9%. The performance of the sleep staging algorithm was significantly higher in insomnia patients (κ = 0.62, accuracy = 77.3%). Only in REM parasomnias, the performance was significantly lower (κ = 0.47, accuracy = 70.5%). For two-class wake/sleep classification, the classifier achieved a κ of 0.65, with a sensitivity (to wake) of 72.9% and specificity of 94.0%.

Conclusions: This study shows that the combination of HRV, body movements, and a state-of-the-art deep neural network can reach substantial agreement in automatic sleep staging compared with polysomnography, even in patients suffering from a multitude of sleep disorders. The physiological signals required can be obtained in various ways, including non-obtrusive wrist-worn sensors, opening up new avenues for clinical diagnostics.
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http://dx.doi.org/10.1093/sleep/zsaa048DOI Listing
September 2020

Modeling sleep onset misperception in insomnia.

Sleep 2020 08;43(8)

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

Objectives: To extend and validate a previously suggested model of the influence of uninterrupted sleep bouts on sleep onset misperception in a large independent data set.

Methods: Polysomnograms and sleep diaries of 139 insomnia patients and 92 controls were included. We modeled subjective sleep onset as the start of the first uninterrupted sleep fragment longer than Ls minutes, where parameter Ls reflects the minimum length of a sleep fragment required to be perceived as sleep. We compared the so-defined sleep onset latency (SOL) for various values of Ls. Model parameters were compared between groups, and across insomnia subgroups with respect to sleep onset misperception, medication use, age, and sex. Next, we extended the model to incorporate the length of wake fragments. Model performance was assessed by calculating root mean square errors (RMSEs) of the difference between estimated and perceived SOL.

Results: Participants with insomnia needed a median of 34 minutes of undisturbed sleep to perceive sleep onset, while healthy controls needed 22 minutes (Mann-Whitney U = 4426, p < 0.001). Similar statistically significant differences were found between sleep onset misperceivers and non-misperceivers (median 40 vs. 20 minutes, Mann-Whitney U = 984.5, p < 0.001). Model outcomes were similar across other subgroups. Extended models including wake bout lengths resulted in only marginal improvements of model outcome.

Conclusions: Patients with insomnia, particularly sleep misperceivers, need larger continuous sleep bouts to perceive sleep onset. The modeling approach yields a parameter for which we coin the term Sleep Fragment Perception Index, providing a useful measure to further characterize sleep state misperception.
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http://dx.doi.org/10.1093/sleep/zsaa014DOI Listing
August 2020

Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features.

Sci Rep 2019 11 26;9(1):17448. Epub 2019 Nov 26.

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

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder, which results in daytime symptoms, a reduced quality of life as well as long-term negative health consequences. OSA diagnosis and severity rating is typically based on the apnea-hypopnea index (AHI) retrieved from overnight poly(somno)graphy. However, polysomnography is costly, obtrusive and not suitable for long-term recordings. Here, we present a method for unobtrusive estimation of the AHI using ECG-based features to detect OSA-related events. Moreover, adding ECG-based sleep/wake scoring yields a fully automatic method for AHI-estimation. Importantly, our algorithm was developed and validated on a combination of clinical datasets, including datasets selectively including OSA-pathology but also a heterogeneous, "real-world" clinical sleep disordered population (262 participants in the validation set). The algorithm provides a good representation of the current gold standard AHI (0.72 correlation, estimation error of 0.56 ± 14.74 events/h), and can also be employed as a screening tool for a large range of OSA severities (ROC AUC ≥ 0.86, Cohen's kappa ≥ 0.53 and precision ≥70%). The method compares favourably to other OSA monitoring strategies, showing the feasibility of cardiovascular-based surrogates for sleep monitoring to evolve into clinically usable tools.
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http://dx.doi.org/10.1038/s41598-019-53403-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6879766PMC
November 2019

Protocol of the SOMNIA project: an observational study to create a neurophysiological database for advanced clinical sleep monitoring.

BMJ Open 2019 11 25;9(11):e030996. Epub 2019 Nov 25.

Electrical Engineering, Technische Universiteit Eindhoven, Eindhoven, The Netherlands.

Introduction: Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods.

Methods And Analysis: We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm.

Ethics And Dissemination: The study was reviewed by the medical ethical committee of the Maxima Medical Center (Eindhoven, the Netherlands, File no: N16.074). All subjects provide informed consent before participation.The SOMNIA database is built to facilitate future research in sleep medicine. Data from the completed SOMNIA database will be made available for collaboration with researchers outside the institute.
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http://dx.doi.org/10.1136/bmjopen-2019-030996DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886950PMC
November 2019

Assessment of the reliability of the motor unit size index (MUSIX) in single subject "round-robin" and multi-centre settings.

Clin Neurophysiol 2019 05 21;130(5):666-674. Epub 2019 Feb 21.

Neuromuscular Diseases Unit/ALS Clinic, Kantonsspital, St. Gallen, Switzerland.

Objective: The motor unit size index (MUSIX) is incorporated into the motor unit number index (MUNIX). Our objective was to assess the intra-/inter-rater reliability of MUSIX in healthy volunteers across single subject "round robin" and multi-centre settings.

Methods: Data were obtained from (i) a round-robin assessment in which 12 raters (6 with prior experience and 6 without) assessed six muscles (abductor pollicis brevis, abductor digiti minimi, biceps brachii, tibialis anterior, extensor digitorum brevis and abductor hallucis) and (ii) a multi-centre study with 6 centres studying the same muscles in 66 healthy volunteers. Intra/inter-rater data were provided by 5 centres, 1 centre provided only intra-rater data. Intra/inter-rater variability was assessed using the coefficient of variation (COV), Bland-Altman plots, bias and 95% limits of agreement.

Results: In the round-robin assessment intra-rater COVs for MUSIX ranged from 7.8% to 28.4%. Inter-rater variability was between 7.8% and 16.2%. Prior experience did not impact on MUSIX values. In the multi-centre study MUSIX was more consistent than the MUNIX. Abductor hallucis was the least reliable muscle.

Conclusions: The MUSIX is a reliable neurophysiological biomarker of reinnervation.

Significance: MUSIX could provide insights into the pathophysiology of a range of neuromuscular disorders, providing a quantitative biomarker of reinnervation.
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http://dx.doi.org/10.1016/j.clinph.2019.01.020DOI Listing
May 2019

Response to Letter-to-Editor by M. Tenhunen and S. Himanen: "Assessment of respiratory effort during sleep: Esophageal pressure versus noninvasive monitoring techniques".

Sleep Med Rev 2015 Dec 26;24:105. Epub 2015 Sep 26.

Kempenhaeghe Foundation, Sleep Medicine Centre, P.O. Box 61, 5590 AB Heeze, The Netherlands; University of Ghent, Faculty of Medicine and Health Sciences, Department of Internal Medicine, 25 Sint-Pietersnieuwstraat, 9000 Ghent, Belgium.

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http://dx.doi.org/10.1016/j.smrv.2015.09.002DOI Listing
December 2015

Assessment of respiratory effort during sleep: Esophageal pressure versus noninvasive monitoring techniques.

Sleep Med Rev 2015 Dec 27;24:28-36. Epub 2014 Dec 27.

Kempenhaeghe Foundation, Sleep Medicine Centre, P.O. Box 61, 5590 AB Heeze, The Netherlands; University of Ghent, Faculty of Medicine and Health Sciences, Department of Internal Medicine, 25 Sint-Pietersnieuwstraat, 9000 Ghent, Belgium.

Monitoring of respiratory effort is paramount in the clinical diagnostic recording of sleep. Increased respiratory effort is a sign of obstructive sleep-disordered breathing and is associated with arousals from sleep. Respiration is the result of muscle activity that induces negative intrathoracic pressure and expansion of the thoracic and abdominal cavities. Therefore respiratory effort may be recorded from mechanical, electrical and electromechanical signals. Several techniques are available for the recording of respiratory effort. Monitoring of esophageal pressure is still the method of choice, as the pressure signal directly reflects the respiratory muscle force. However, esophageal pressure monitoring is cumbersome and may be replaced with noninvasive techniques. In order to be reliable, these techniques must be validated against the esophageal pressure standard. The present review presents a concise description of the technical principles and, if available, a comparison with esophageal pressure data, based on a systematic literature search. Most data are available on respiratory inductance plethysmography, and confirm that this technique is suitable for routine diagnostic investigation of respiratory effort during sleep. Pulse transit time, diaphragmatic electromyography, snoring loudness, suprasternal pressure monitoring, midsagittal jaw movement and forehead venous pressure monitoring are promising alternative techniques although only limited validation is available.
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http://dx.doi.org/10.1016/j.smrv.2014.12.006DOI Listing
December 2015

The role of central and peripheral muscle fatigue in postcancer fatigue: a randomized controlled trial.

J Pain Symptom Manage 2015 Feb 21;49(2):173-82. Epub 2014 Aug 21.

Department of Medical Oncology, Radboud University Medical Centre, Nijmegen, The Netherlands; Department of Medical Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.

Context: Postcancer fatigue is a frequently occurring problem, impairing quality of life. Little is known about (neuro)physiological factors determining postcancer fatigue. It may be hypothesized that postcancer fatigue is characterized by low peripheral muscle fatigue and high central muscle fatigue.

Objectives: The aims of this study were to examine whether central and peripheral muscle fatigue differ between fatigued and non-fatigued cancer survivors and to examine the effect of cognitive behavioral therapy (CBT) on peripheral and central muscle fatigue of fatigued cancer survivors in a randomized controlled trial.

Methods: Sixteen fatigued patients in the intervention group (CBT) and eight fatigued patients in the waiting list group were successfully assessed at baseline and six months later. Baseline measurements of 20 fatigued patients were compared with 20 non-fatigued patients. A twitch interpolation technique and surface electromyography were applied, respectively, during sustained contraction of the biceps brachii muscle.

Results: Muscle fiber conduction velocity (MFCV) and central activation failure (CAF) were not significantly different between fatigued and non-fatigued patients. Change scores of MFCV and CAF were not significantly different between patients in the CBT and waiting list groups. Patients in the CBT group reported a significantly larger decrease in fatigue scores than patients in the waiting list group.

Conclusion: Postcancer fatigue is neither characterized by abnormally high central muscle fatigue nor by low peripheral muscle fatigue. These findings suggest a difference in the underlying physiological mechanism of postcancer fatigue vs. other fatigue syndromes.
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http://dx.doi.org/10.1016/j.jpainsymman.2014.06.020DOI Listing
February 2015

Quantitative muscle ultrasound and quadriceps strength in patients with post-polio syndrome.

Muscle Nerve 2015 Jan;51(1):24-9

Department of Rehabilitation, Academic Medical Center, Postbus 22660, 1100 DD, Amsterdam, The Netherlands.

Introduction: We investigated whether muscle ultrasound can distinguish muscles affected by post-polio syndrome (PPS) from healthy muscles and whether severity of ultrasound abnormalities is associated with muscle strength.

Methods: Echo intensity, muscle thickness, and isometric strength of the quadriceps muscles were measured in 48 patients with PPS and 12 healthy controls.

Results: Patients with PPS had significantly higher echo intensity and lower muscle thickness than healthy controls. In patients, both echo intensity and muscle thickness were associated independently with muscle strength. A combined measure of echo intensity and muscle thickness was more strongly related to muscle strength than either parameter alone.

Conclusions: Quantitative ultrasound distinguishes healthy muscles from those affected by PPS, and measures of muscle quality and quantity are associated with muscle strength. Hence, ultrasound could be a useful tool for assessing disease severity and monitoring changes resulting from disease progression or clinical intervention in patients with PPS.
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http://dx.doi.org/10.1002/mus.24272DOI Listing
January 2015

Quantitative facial muscle ultrasound: feasibility and reproducibility.

Muscle Nerve 2013 Sep 27;48(3):375-80. Epub 2013 Jul 27.

Department of Neurology and Clinical Neurophysiology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.

Introduction: In this study we describe a protocol for quantitative ultrasound of facial muscles (procerus, zygomaticus major, levator labii superior, depressor anguli oris, mentalis, orbicularis oris pars labialis, orbicularis oris pars marginalis).

Methods: Muscle thickness (MT) and echo intensity (EI) were measured in 12 healthy subjects and a myotonic dystrophy type 1 patient.

Results: MTs ranged from 0.15 to 0.30 mm, except for the procerus muscle (0.06 mm). EIs ranged from 1 to 34, except for the procerus muscle. MT reproducibility was fair for the orbicularis oris pars labialis, excellent for the procerus and levator labii, and good for the other muscles. The myotonic dystrophy type 1 patient showed high EIs, outside the range in healthy subjects in 6 of the 7 muscles. MT was lower than the range seen in healthy subjects in 4 muscles.

Conclusion: Quantitative muscle ultrasound of the facial muscles is feasible and shows moderate to excellent reproducibility.
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http://dx.doi.org/10.1002/mus.23769DOI Listing
September 2013

A new and fast approach towards sEMG decomposition.

Med Biol Eng Comput 2013 May 18;51(5):593-605. Epub 2013 Jan 18.

Department of Electrical Engineering, SCD-SISTA, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.

The decomposition of high-density surface EMG (HD-sEMG) interference patterns into the contribution of motor units is still a challenging task. We introduce a new, fast solution to this problem. The method uses a data-driven approach for selecting a set of electrodes to enable discrimination of present motor unit action potentials (MUAPs). Then, using shapes detected on these channels, the hierarchical clustering algorithm as reported by Quian Quiroga et al. (Neural Comput 16:1661-1687, 2004) is extended for multichannel data in order to obtain the motor unit action potential (MUAP) signatures. After this first step, more motor unit firings are obtained using the extracted signatures by a novel demixing technique. In this demixing stage, we propose a time-efficient solution for the general convolutive system that models the motor unit firings on the HD-sEMG grid. We constrain this system by using the extracted signatures as prior knowledge and reconstruct the firing patterns in a computationally efficient way. The algorithm performance is successfully verified on simulated data containing up to 20 different MUAP signatures. Moreover, we tested the method on real low contraction recordings from the lateral vastus leg muscle by comparing the algorithm's output to the results obtained by manual analysis of the data from two independent trained operators. The proposed method showed to perform about equally successful as the operators.
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http://dx.doi.org/10.1007/s11517-012-1029-yDOI Listing
May 2013

Motor unit number index (MUNIX) versus motor unit number estimation (MUNE): a direct comparison in a longitudinal study of ALS patients.

Clin Neurophysiol 2012 Aug 8;123(8):1644-9. Epub 2012 Feb 8.

Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands.

Objective: To evaluate how the motor unit number index (MUNIX) is related to high-density motor unit number estimation (HD-MUNE) in healthy controls and patients with amyotrophic lateral sclerosis (ALS).

Methods: Both MUNIX and HD-MUNE were performed on the thenar muscles in 18 ALS patients and 24 healthy controls. Patients were measured at baseline, within 2 weeks, and after 4 and 8 months. Clinical evaluation included Medical Research Council (MRC) scale and the ALS functional rating scale (ALSFRS).

Results: There was a significant positive correlation between MUNE and MUNIX values in ALS patients (r=0.49 at baseline; r=0.56 at 4 months; r=0.56 at 8 months, all p<0.05), but not in healthy controls. After 8 months, both MUNE and MUNIX values of the ALS patients decreased significantly more compared to MRC scale, ALS functional rating scale (ALSFRS) and compound muscle action potential (CMAP) (p<0.05). There was no significant difference in relative decline of MUNIX and HD-MUNE values.

Conclusions: In ALS patients, MUNIX and HD-MUNE are significantly correlated. MUNIX has an almost equivalent potential in detecting motor neuron loss compared to HD-MUNE.

Significance: MUNIX could serve as a reliable and sensitive marker for monitoring disease progression in ALS.
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http://dx.doi.org/10.1016/j.clinph.2012.01.004DOI Listing
August 2012

Automated way to obtain motor units' signatures and estimate their firing patterns during voluntary contractions using HD-sEMG.

Annu Int Conf IEEE Eng Med Biol Soc 2011 ;2011:4090-3

Department of Electrical Engineering, Division SCD-SISTA, KatholiekeUniversiteit Leuven, Leuven 3001, Belgium.

A new, automated way to obtain signatures of active motor units (MUs) from high density surface EMG recordings during voluntary contractions is presented. It relies on clustering of repetitive shapes corresponding to different MU action potentials (MUAPs) present. The number of clusters and the mean shapes of the MUAPs as observed on the electrode grid, are estimated in a fast way without user interaction. The algorithm is tested on simulated signals mimicking a small muscle. Our results show that at least 8 MUAPs can be reliably reconstructed and their MU mean firing frequencies can be estimated.
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http://dx.doi.org/10.1109/IEMBS.2011.6091016DOI Listing
July 2012

Quantitative muscle ultrasound is a promising longitudinal follow-up tool in Duchenne muscular dystrophy.

Neuromuscul Disord 2012 Apr 30;22(4):306-17. Epub 2011 Nov 30.

Radboud University Nijmegen Medical Centre, Nijmegen Centre for Evidence Based Practice, Department of Rehabilitation, Nijmegen, The Netherlands.

Responsive outcome measures are needed to follow the disease status of Duchenne muscular dystrophy (DMD) patients, as new therapeutic approaches become available for affected boys. Quantitative muscle ultrasound (QMUS) is potentially an attractive follow up tool for DMD because it reflects the severity of the dystrophic process without the need for invasive procedures, by quantifying echo intensity (i.e., mean grey level of muscle images) and muscle thickness. We performed a longitudinal follow-up of lower and upper extremity QMUS in 18 DMD patients and compared this with physical functioning in 11 of these patients. QMUS could be performed in every patient, and no patient was subjected to more than a total of 20min of ultrasound scanning time for this study. As expected we found a significant increase of echo intensity with age, reflecting increasing dystrophic muscle changes. This increase was related to ambulatory status, functional grading, muscle strength and motor ability. Our study establishes QMUS as a practical and child-friendly tool for the longitudinal follow up of DMD patients.
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http://dx.doi.org/10.1016/j.nmd.2011.10.020DOI Listing
April 2012

Motor Unit Number Index (MUNIX): a novel neurophysiological marker for neuromuscular disorders; test-retest reliability in healthy volunteers.

Clin Neurophysiol 2011 Sep 10;122(9):1867-72. Epub 2011 Mar 10.

Neuromuscular Diseases Unit, Kantonsspital St Gallen, St Gallen, Switzerland.

Objective: To investigate the intra-rater and inter-rater test-retest reliability of the Motor Unit Number Index (MUNIX) in healthy subjects in a multicentre setting.

Methods: Six study centres applied the MUNIX technique in 66 healthy subjects. Five to six muscles (biceps brachii, BB; abductor digiti minimi, ADM; abductor pollicis brevis, APB; tibialis anterior, TA; extensor digitorum brevis, EDB and abductor hallucis, AH) were measured in each volunteer four times by two independent examiners.

Results: The method was easy to perform and well tolerated. The intraclass correlation coefficient (ICC) varied between centres and muscles. Intra-rater reliability was greatest for the AH (ICC 0.83) and EDB (ICC 0.81). Inter-rater reliability was greatest for the AH (ICC 0.69) and ADM muscles (ICC 0.69). The most critical muscle was the APB muscle (ICC 0.52, total variability). This was mostly due to variability in the compound muscle action potential (CMAP) measurements. MUNIX values of the APB, ADM and TA fell into the same range as in other motor unit number estimation (MUNE) studies.

Conclusion: MUNIX measurements in multiple muscles show good inter- and intra-rater reliability in healthy subjects. CMAP amplitude must be controlled to optimize reliability.

Significance: Results suggest that MUNIX could serve as a reliable marker for motor neuron loss in diseases like amyotrophic lateral sclerosis.
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http://dx.doi.org/10.1016/j.clinph.2011.02.017DOI Listing
September 2011

Dynamic imaging of skeletal muscle contraction in three orthogonal directions.

J Appl Physiol (1985) 2010 Sep 8;109(3):906-15. Epub 2010 Jul 8.

Clinical Physics Laboratory-833, Department of Pediatrics, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.

In this study, a multidimensional strain estimation method using biplane ultrasound is presented to assess local relative deformation (i.e., local strain) in three orthogonal directions in skeletal muscles during induced and voluntary contractions. The method was tested in the musculus biceps brachii of five healthy subjects for three different types of muscle contraction: 1) excitation of the muscle with a single electrical pulse via the musculocutaneous nerve, resulting in a so-called "twitch" contraction; 2) a train of five pulses at 10 Hz and 20 Hz, respectively, to obtain a submaximum tetanic contraction; and 3) voluntary contractions at 30, 60, and 100% of maximum contraction force. Results show that biplane ultrasound strain imaging is feasible. The method yielded adequate performance using the radio frequency data in tracking the tissue motion and enabled the measurement of local deformation in both the vertical direction (orthogonal to the arm) and in the horizontal directions (parallel and perpendicular to direction of the arm) in two orthogonal cross sections of the muscle. The twitch experiments appeared to be reproducible in all three directions, and high strains in vertical (25 to 30%) and horizontal (-20% to -10%) directions were measured. Visual inspection of both the ultrasound data, as well as the strain data, revealed a relaxation that was significantly slower than the force decay. The pulse train experiments nicely illustrated the performance of our technique: 1) similar patterns of force and strain waveforms were found; and 2) each stimulation frequency yielded a different strain pattern, e.g., peak vertical strain was 40% during 10-Hz stimulation and 60% during 20-Hz stimulation. The voluntary contraction patterns were found to be both practically feasible and reproducible, which will enable muscles and more natural contraction patterns to be examined without the need of electrical stimulation.
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http://dx.doi.org/10.1152/japplphysiol.00092.2010DOI Listing
September 2010

Monitoring disease progression using high-density motor unit number estimation in amyotrophic lateral sclerosis.

Muscle Nerve 2010 Aug;42(2):239-44

Department of Neurology, Clinical Neurophysiology-920, Radboud University Nijmegen Medical Centre, Donders Institute for Brain, Cognition and Behaviour, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.

In amyotrophic lateral sclerosis (ALS), progressive motor neuron loss causes severe weakness. Functional measurements tend to underestimate the underlying pathology because of collateral reinnervation. A more direct marker of lower motor neuron loss is of significant importance. We evaluated high-density motor unit number estimation (MUNE), as compared with the ALS Functional Rating Scale (ALSFRS) and maximal compound muscle action potential (CMAP) amplitude, for monitoring and classifying disease progression. MUNE showed good reproducibility (intraclass correlation coefficient = 0.86). MUNE showed a significantly greater decrease than the ALSFRS, the Medical Research Council (MRC) scale, and CMAP amplitude. Patients could be stratified into groups with rapidly or slowly progressive disease based on a decrement in MUNE at 4 months from baseline; ALSFRS score at 8 months was significantly lower in the rapidly progressive group. MUNE was sensitive to motor neuron loss early in the disease course when compared to other clinical measures. Stratification of patients based on a decrease in MUNE seems feasible.
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http://dx.doi.org/10.1002/mus.21680DOI Listing
August 2010

Optimal placement of bipolar surface EMG electrodes in the face based on single motor unit analysis.

Psychophysiology 2010 Mar 10;47(2):299-314. Epub 2009 Dec 10.

Department of Orthodontics, Albert Ludwigs University, Freiburg, Germany.

Locations of surface electromyography (sEMG) electrodes in the face are usually chosen on a macro-anatomical basis. In this study we describe optimal placement of bipolar electrodes based on a novel method and present results for lower facial muscles. We performed high-density sEMG recordings in 13 healthy participants. Raw sEMG signals were decomposed into motor unit action potentials (MUAPs). We positioned virtual electrode pairs in the interpolated monopolar MUAPs at different positions along muscle fiber direction and calculated the bipolar potentials. Electrode sites were determined where maximal bipolar amplitude was achieved and were validated. Objective guidelines for sEMG electrode placement improve the signal-to-noise ratio and may contribute to reduce cross talk, which is particularly important in the face. The method may be regarded as an important basis for improving the validity and reproducibility of sEMG in complex muscle areas.
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http://dx.doi.org/10.1111/j.1469-8986.2009.00935.xDOI Listing
March 2010

Muscles alive: ultrasound detects fibrillations.

Clin Neurophysiol 2009 May 7;120(5):932-6. Epub 2009 Apr 7.

Department of Clinical Neurophysiology, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.

Objective: Muscle ultrasound is capable of visualizing muscle movements. Recent improvements in ultrasound technology have raised the question whether it is also possible to detect small-scale spontaneous muscle activity such as denervation. In this study we investigated the ability of dynamic muscle ultrasound to detect fibrillations.

Methods: Eight patients with fibrillations were measured simultaneously by ultrasound and EMG to verify which movements on ultrasound examination corresponded to fibrillation potentials on EMG. The temperature dependency of ultrasound detected fibrillations and the observer agreement was assessed in five healthy subjects with focal denervation induced by botulinum toxin.

Results: Fibrillations appeared on ultrasound examination as small, irregularly oscillating movements within the muscle while the overall shape of the muscle remains undisturbed. Visibility of fibrillations with ultrasound decreased with lower temperatures, with a 32% decrease at 30 degrees C compared to 39 degrees C. The interobserver agreement was substantial with a kappa of 0.65 for experienced observers.

Conclusion: Fibrillations could be visualized with ultrasound. Consistent results could be obtained from trained observers. Care has to be taken to ensure an optimal muscle temperature to avoid false negative results, especially in distal muscles.

Significance: Visualization of fibrillations by muscle ultrasound opens the way for a new diagnostic application of this technique.
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http://dx.doi.org/10.1016/j.clinph.2009.01.016DOI Listing
May 2009

Quantitative gray-scale analysis in skeletal muscle ultrasound: a comparison study of two ultrasound devices.

Muscle Nerve 2009 Jun;39(6):781-6

Department of Clinical Neurophysiology, Donders Centre for Neuroscience, Radboud University Nijmegen, 6500 HB Nijmegen, The Netherlands.

Muscle ultrasound is a useful technique to detect neuromuscular disorders. Quantification of muscle echo intensity (EI) using gray-scale analysis is more reliable and more sensitive compared with visual evaluation of the images. We devised a method to reliably use EI normal values established with one ultrasound device for use with another device. Based on measurements in a dedicated phantom and in 7 healthy subjects, a conversion equation was calculated to convert the mean EI. The reliability of this equation was next evaluated in a follow-up study of 22 healthy children. Mean muscle EI could be reliably converted from one ultrasound device to another. This allows for normal values obtained with one device to be used with other devices, which is an important step forward toward the use of quantitative muscle ultrasound in daily clinical care.
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http://dx.doi.org/10.1002/mus.21285DOI Listing
June 2009

Motor unit tracking with high-density surface EMG.

J Electromyogr Kinesiol 2008 Dec 8;18(6):920-30. Epub 2008 Nov 8.

Department of Clinical Neurophysiology, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.

Following (tracking) individual motor units over time can provide important new insights, both into the relationships among various motor unit (MU) morphological and functional properties and into how these properties are influenced by neuromuscular disorders or interventions. The present study aimed to determine whether high-density surface EMG (HD-sEMG) recordings, which use an array of surface electrodes over a muscle, can increase the yield of MU tracking studies in terms of the number of MUs that can be tracked. For that purpose, four HD-sEMG recording sessions were performed on the thenar muscles of ten healthy subjects. Decomposition of the recorded composite responses yielded a study total of 2849 motor unit action potentials (MUAPs). MUAPs that were found in both of the first two sessions, performed on the same day, were defined as trackable MUAPs. Our results show that 22 (median value; range, 13-34) MUAPs per nerve were trackable, which represented approximately 5% of the total MU population. Of these trackable MUAPs, 16 (11-26) could also be found in one or both of the third and fourth sessions, which were performed between 1 and 13 weeks after the initial studies. Nine (4-18) MUAPs were found in all four sessions. Many of the characteristic MUAP shapes matched well between sessions, even when these sessions were several weeks apart. However, some MUAPs seem very sensitive to changes in arm position or in the muscle's morphology (e.g., to changes in muscle fiber length due to variable degrees of thumb flexion or extension), particularly those from larger and/or superficial MUs. Standardization is, therefore, essential to detect even small MUAP changes, as may occur with pathology or interventions. If this is accomplished, MU tracking with HD-sEMG may prove to be a powerful tool for a promising type of neurophysiological investigation.
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http://dx.doi.org/10.1016/j.jelekin.2008.09.001DOI Listing
December 2008

Effect of small motor unit potentials on the motor unit number estimate.

Muscle Nerve 2008 Jul;38(1):887-92

Department of Clinical Neurophysiology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.

Small surface motor unit potentials (S-MUPs) may have a negative influence on the variability of the motor unit number estimate (MUNE). According to published consensus criteria S-MUPs with a negative peak amplitude smaller than 10 muV should be omitted. The effect of omitting small S-MUPs on the MUNE was evaluated using a simulation model. The model incorporated a healthy and amyotrophic lateral sclerosis (ALS) distribution formed with real S-MUPs. Using a random drawing process the MUNE was calculated with and without small S-MUPs. In the healthy population 27% of all S-MUPs were small. MUNE determined without these S-MUPs was marginally less variable. However, MUNE values dropped about 24% at a sample size of 20. In ALS, only 12% of the total population of 130 S-MUPs were small. MUNE dropped about 12% without the small S-MUPs. By omitting small S-MUPs the differences between the healthy and ALS distributions become smaller. Therefore, incorporating small S-MUPs in the estimate is suggested.
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http://dx.doi.org/10.1002/mus.21003DOI Listing
July 2008

Motor unit number estimation using high-density surface electromyography.

Clin Neurophysiol 2008 Jan 26;119(1):33-42. Epub 2007 Nov 26.

Department of Clinical Neurophysiology, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegan, The Netherlands.

Objective: To present a motor unit number estimation (MUNE) technique that resolves alternation by means of high-density surface EMG.

Methods: High-density surface EMG, using 120 EMG channels simultaneously, is combined with elements of the increment counting technique (ICT) and the multiple-point stimulation technique. Alternation is a major drawback in the ICT. The spatial and temporal information provided by high-density surface EMG support identification and elimination of the effects of alternation. We determined the MUNE and its reproducibility in 14 healthy subjects, using a grid of 8 x 15 small electrodes on the thenar muscles.

Results: Mean MUNE was 271+/-103 (retest: 290+/-109), with a coefficient of variation of 22% and an intra-class correlation of 0.88. On average, 22 motor unit potentials (MUPs) were collected per subject. The representativity of this MUP sample was quantitatively assessed using the spatiotemporal information provided by high-density recordings.

Conclusions: MUNE values are relatively high, because we were able to detect many small MUPs. Reproducibility was similar to that of other MUNE techniques.

Significance: Our technique allows collection of a large MUP sample non-invasively by resolving alternation to a large extent and provides insight into the representativity of this sample. The large sample size is expected to increase MUNE accuracy.
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http://dx.doi.org/10.1016/j.clinph.2007.09.133DOI Listing
January 2008
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