BME2023 Paper Submission & Registration
9th Dutch Bio-Medical Engineering Conference

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10:30   Neurophysiology & Sleep
Chair: Nick Eleveld
15 mins
Contactless radar based vital signs parameter estimation for sleep monitoring
Fokke van Meulen, Juha Kortelainen, Mervi Hirvonen, Hans van Dijk, Johan Plomp, Sebastiaan Overeem
Abstract: Polysomnography (PSG) is widely used in clinical practice as the gold standard in monitoring sleep, but the large amount of wired sensors may influence actual sleep quality and a less representative assessment of sleep as a result. We aim to develop a radar-based method for the unobtrusive assessment of vital signs parameters that are part of the current PSG setup. A 60 GHz Frequency-Modulated Continuous Wave radar system (VTT, Finland) was installed above the head side of a bed in the clinical sleep laboratory of Sleep Medicine Center Kempenhaeghe (Heeze, Netherlands). The radar operates with multiple transmit and receiving antennas to construct a two-dimensional image. The measured signals show micro vibrations at the skin surface with 110 Hz sampling rate. Combined with advanced data processing techniques it allows the detection of heart beats and respiratory movements. Simultaneously recording PSG and radar data allows the validation of vital signs parameters estimated by radar against their gold-standard counterparts. Recruited patients had a variety of (suspected) sleep disorders and were scheduled for a video-polysomnography as part of their diagnostic trajectory. Between September 2021 and February 2022, 43 participants were included in the study. Using the radar-based setup it was possible to detect heart rate with a mean average error of 2.4 beats per minute compared to ECG. The respiration rate shows a mean average error of 0.58 breaths per minute compared to the RIP belts signal. On average the coverage was 98.5% of the nocturnal recording. After reviewing the data, general causes for error were cardiac arrhythmias and motion artefacts. The radar-based setup allows the measurement of heart rate and respiration rate in an accurate and completely unobtrusive manner. In future, surrogate measures for sleep architecture will be obtained, as well as pathological events, such as sleep related breathing and movement events. In contrast with other radar based vital signs monitoring approaches, our setup allows the detection of movements in a two-dimensional plane, and therefore has the potential of detecting larger body movements, such as limb movements and sleeping posture, as well as the ability to separately monitor vital signs of bed partners.
15 mins
Comparison of passive oddball paradigms to study mismatch negativity
Annika de Goede, Femke de Graaf, Robert Doll
Abstract: The oddball paradigm is a classical neuropsychological test to evoke event related potentials. The auditory passive task studies mismatch negativity (MMN) without requiring attention of participants. MMN is thought to reflect an automatic process that detects a difference between an incoming stimulus and the sensory memory trace of preceding stimuli. Traditionally, one specific deviant tone is presented between standard tones with a low probability. Nowadays, variations are described using multiple types of deviant tones. This study aims to 1) compare the traditional one-deviant and three-deviant (Optimal-3) paradigms, and 2) determine the repeatability and effect of deviant type of the Optimal-3 paradigm. Ten healthy participants performed the traditional (once) and Optimal-3 oddball (four times) paradigm. During the traditional task, 600 standard and 150 deviant tones were presented that differed only in frequency. The Optimal-3 task consisted of standard tones (540x) alternated with deviants that differed in either frequency, intensity, or duration (180x per type). MMN amplitude and latency were determined from the difference waveform (interval 100-250 ms post-stimulus) at electrode Cz and analysed using linear mixed models. Compared to the traditional oddball paradigm, the Optimal-3 frequency deviant showed a significantly larger MMN amplitude (p<.01) and shorter latency (p<.001). For the Optimal-3 paradigm, significant differences between deviant types were detected. MMN amplitude was larger for the duration compared to the frequency (p<.01) and intensity (p<.01) deviants, while no significant difference was found between the frequency and intensity deviants. Furthermore, MMN latency was longer for the intensity compared to the frequency (p<.001) and duration (p<.001) deviants, and for the frequency compared to the duration deviant (p<.01). Repeatability of MMN amplitude was good (ICC>0.7) for the frequency and duration deviants, and moderate for the intensity deviant (ICC=0.6). However, the repeatability of the MMN latency was moderate for the frequency deviant (ICC=0.6) and poor (ICC<0.5) for the duration and intensity deviants. The Optimal-3 paradigm seems to be a good alternative for the traditional oddball paradigm, as it evokes a significantly larger MMN amplitude and shorter latency. This is especially the case for the duration deviant compared to the frequency and intensity deviants.
15 mins
Altered nociceptive function in patients with morbid obesity compared to healthy controls
Tom Berfelo, Imre P. Krabbenbos, Boudewijn van den Berg, Eva Kleinveld, Jan R. Buitenweg
Abstract: For improved observation of nociceptive dysfunction, we are developing a novel measurement technique that combines the nociceptive detection threshold (NDT) with brain evoked potentials (EP) using intra-epidermal electrical stimulation. To validate the applicability of the NDT-EP method in patients with higher body mass index (BMI) values, we explored the feasibility of the NDT-EP method in pain-free patients with morbid obesity (MO). Subsequently, we compared the NDT-EP outcomes in MO with those of healthy controls (HC) at the various stimulus types used. Seventeen pain-free MO patients (BMI: 45.9 ± 4.6) and sixteen HC (BMI: 22.0 ± 2.0) were measured at the St. Antonius hospital. Three stimulus types (i.e., single- and double-pulse stimuli with 10 and 40 ms inter-pulse interval) consisting of a total of 450 trials were applied to each subject during two measurements. Subsequently, NDT-EP outcomes related to stimulus properties were calculated using (generalized) linear mixed regression models. The NDT-EP method was completed successfully in all MO patients. NDT results demonstrated that the detection probability was significantly (p=0.014) associated with the effect of MO diagnosis. The detection probability was significantly (p=0.020) decreased by the interaction of diagnosis with the amplitude of the first pulse. Furthermore, EP results showed that the positive peak of the EP amplitude at 485 ms post-stimulus was significantly (p=0.031) decreased by the interaction of diagnosis with the amplitude of a second pulse after 10 ms. The NDT-EP method was feasible to use in pain-free MO patients. Different NDT-EP outcomes were seen in MO compared to HC, which may indicate altered nociceptive function. Therefore, we need future research into clinical features, e.g., BMI, related to nociceptive dysfunction.
15 mins
Safe Neurostimulation Against Pain (SNAP)
Sofia Cecchini, Maurits Konings, Joris Jaspers, Jesse Bosma, Sanne Banning, Oda Heerema, Luuk Evers, Albert van Wijk
Abstract: Chronic neuropathic pain is cumbersome, difficult to treat and it induces a dramatic loss of Quality-of-Life in many patients. It is caused by a primary lesion or dysfunction of the nervous system and it is often treated using analgesic drugs and other conventional medical management that can lead to serious side-effects (dependence, drowsiness) and to which patients are not always responsive. Therefore, many efforts have been taken to find an alternative. Spinal Cord Stimulation (SCS) has been proven to be the most effective in suppressing chronic neuropathic pain: an electric stimulation current is applied in proximity of the vertical dorsal columns of the spinal cord which entails pain mitigation, according to the “gate control theory”. Nevertheless, the current clinical SCS technique uses electrodes on a catheter that needs to be placed within the very vulnerable epidural space. This location is prone to infection, connective tissue formation and catheter migration. To overcome these drawbacks, a new concept (SNAP, Safe Neurostimulation Against Pain) has been formulated, in which the electrodes are placed on a clamp, securely and strongly anchored to the dorsal side of the thoracic vertebrae, and thus outside the risky epidural space. The design comprises a new multi-electrode system on a small, stand-alone, wire-shaped implant that takes advantage of the electrically insulating property of the bone tissue of the spinal cord as a means of projecting the electrical stimulation current into the target area in the spinal cord. The first in-vitro simulations have shown promising results: the applied field appears to target the correct region in the spinal cord without substantial electric current leakages towards outside the foramen vertebrae. Thus, from these preliminary data it seems possible to achieve a sufficient pain mitigation effect using our safe and minimally invasive approach.
15 mins
SOM-CPC: A new clustering method for sleep recordings to facilitate pattern recognition
Iris Huijben, Ruud van Sloun, Sebastiaan Overeem, Merel van Gilst
Abstract: Introduction The expressiveness of a hypnogram in sleep medicine is limited due to the assignment of one AASM sleep stage label per 30-sec sleep epoch. We explored a new pattern-recognition method to further study sleep structure, possibly yielding new insights in disordered sleep. Cluster analysis is a common approach for unsupervised pattern recognition, but classical methods are typically applied on handcrafted features (e.g. spectral power bands), selection of which relies on expert knowledge and often limits an approach to specific applications. We propose SOM-CPC, a method that learns features using Contrastive Predictive Coding (CPC) [1], and subsequently clusters, as well as visualizes, them using a Self-Organizing Map (SOM) [2]. SOM-CPC is sensor-agnostic and takes temporal information into account. Methods Video-polysomnography recordings of 96 healthy subjects were studied (60 F, age: 33±13.6 years) from which we sub-selected F3/F4, C3/C4, O1/O2, Chin1/Chin2 and E1/E2 derivations. We created a hold-out test set of the even channels from n=11 recordings on which conclusions were drawn. The rest of the data were used for training and validating the model. SOM-CPC resulted in a 2-dimensional grid of 100 clusters. For interpretability, each cluster was labelled with the most-frequent sleep stage label and the distribution over time-in-night. Cluster statistics were compared using the non-parametric Mann Whitney U test. Results Labelling each cluster with the most-frequent AASM label of the training set, yielded a Cohen’s kappa of 0.6±0.16 with respect to the expert annotations of the test set. Assigning a distribution over AASM labels to each cluster, revealed non-transitional clusters and transitional clusters that received varying labels. Interestingly, these transitional clusters were positioned at the boundaries of non-transitional clusters on the grid. Adding time-in-night labelling, we found a cluster of early-night (n=147, median epoch: 19) and late-night Wake epochs (n=262, median epoch: 498; U=6.4e3, p=2.15e-29). Conclusions We propose a new approach to cluster raw sleep recordings. Visualizing the grid of clusters labelled with different variables per cluster, allows for recognition of patterns beyond those that can be deducted from the hypnogram. Next, training on data from patients with different sleep disorders may cluster certain patients with specific demographics. [1] Aaron van den Oord, Yazhe Li, and Vinyals Oriol. Representation Learning with Contrastive Predictive Coding. arXiv preprint arXiv:1807.03748, 2019. [2] Teuvo Kohonen. The self-organizing map. Proceedings of the IEEE, 78(9):1464–1480, 1990.
15 mins
Simulating motor neuron degeneration and reinnervation in motor neuron diseases based on surface-electromyography recorded single motor unit potentials
Boudewijn T.H.M. Sleutjes, Diederik J.L. Stikvoort Garcia, H. Stephan Goedee, Leonard H. van den Berg
Abstract: Surface-electromyography (EMG) methods have been shown to provide insights on disease pathology of motor neuron diseases (MND) by detecting two prominent mechanisms: loss of motor units (MUs) and enlarged MUs due to collateral reinnervation (i.e. denervated muscle fibers become reinnervated by still functioning peripheral motor neurons). Due to collateral reinnervation muscle strength may for a certain time be relatively maintained. The interaction of these two mechanisms may vary greatly between patients. By simulating their interaction, we aim to provide insights into their impact on the sensitivity of surface-EMG methods to monitor disease progression in MND. Therefore, we developed a muscle model to simulate progressive MU loss and enlarged MUs due to reinnervation. High-density surface-EMG recorded single MU potentials (MUPs) formed the basic building blocks of the model. From the baseline MU pool innervating a muscle, progressive MU loss was simulated by the one-by-one removal of MUs. These removed MUs underwent reinnervation with scenarios varying from 0% to 100% by neighbouring MUPs depending on the overlap in their topographical fingerprints. We tailored the model to generate compound muscle action potential (CMAP) scans, which is a promising surface-EMG method for monitoring patients with MND. This allowed us to compare simulated and recorded CMAP scans in healthy controls and patients with MND. Simulated baseline maximum CMAP showed values up to 12 mV. During progressive MU loss and reinnervation, the CMAP scan pattern showed a clear transition from a smooth sigmoidal towards a more discrete stepwise pattern, which matched well with experimental observations. Reinnervation was successfully reflected by increases in MU size resulting in enlarged MUs up to 2 mV when only a few MUs are left, which are sizes also occasionally observed during experiments. The muscle model was able to capture on average the pathological MU characteristics observed in patients with MND. Further refinements are possible towards more patient-specific patterns over time. The model could be used as surrogate reference to compare MU number estimate (MUNE) methods without posing unnecessary burden to patients. This may also aid in designing more sensitive biomarkers for monitoring disease progression of MND.

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