Leading Author 11

• Detecting Epileptic Seizures With Multimodal Non-EEG Data From Wearables

  May 2023

  Böttcher, Sebastian

  University of Siegen
  doi:10.25819/ubsi/10357

  Abstract: Epilepsy is one of the most prevalent chronic neurological disorders, affecting millions worldwide throughout all societal groups. Epilepsy manifests in those affected as reoccurring seizures with a wide range of different symptoms at variable intervals and severity. The current gold standard to diagnose and monitor epilepsy is video-electroencephalography. Patients with epilepsy visit monitoring units for a few days, and clinicians provoke seizures through various means, hoping to get enough information for precise diagnosis and treatment. However, this procedure is not viable during the patients' daily lives and over more extended periods. Furthermore, the handwritten diaries that some patients keep have proven unreliable, typically severely under-counting the number of seizures occurring. An alternative for ultra-long-term monitoring is needed to improve current treatments and facilitate the development of new therapy options. This thesis investigates the potential of multimodal non-electroencephalography data recorded from wearable devices as a tool for seizure detection in the context of automated seizure diaries. It furthermore explores a potential application of seizure detection in the context of an automatic alarm system. The work featured in this thesis produces and employs a new data set of wearable biosignal data, recorded at two European epilepsy centers in the context of a European collaborative research project. Over 200 patients with epilepsy were recruited at the two epilepsy monitoring units, and over 300 epileptic seizures of varying types were recorded with a wearable device. Here, the Empatica E4 is used, a research-grade wrist-worn wearable that captures the biosignal modalities of accelerometry (movement), electrodermal activity (electrical skin conductance), and blood volume pulse (optical pulse measurement via photoplethysmography). This data set was the basis for several data analysis studies concerning the evaluation of seizure detection methodologies. This thesis compiles and provides a framework for several contributions of the author concerning the detection of epileptic motor seizures with multimodal non-electroencephalography data from wearables. Specifically, the included studies investigated those seizures with movement manifestations in the limbs and found detection systems based on supervised ensemble machine learning using physiological biosignal data to be viable. One central part of this thesis is focused on convulsive tonic-clonic seizures, severe and dangerous seizures that start in or progress to both hemispheres of the brain. During these seizures, the awareness and consciousness of the affected patient are impaired, and high-amplitude, high-frequency jerks of the limbs and whole body occur. One of the studies presented here assessed an automatic detection methodology based on a combination of accelerometry and electrodermal activity signals. A supervised ensemble machine learning model is trained on expert-labeled data and evaluated on an out-of-sample test set. It performs at least on par with state-of-the-art related work, correctly classifying more than 90 percent of seizure events with false alarm rates of less than 0.5 per day. The suggested methodology performs better than the average monomodal detection system in related work. Convulsive tonic-clonic seizures are typically followed by a period of unconsciousness and immobility, significantly increasing the risk of sudden unexpected death in epilepsy. A further study investigates the utility of wearable biosignal data to detect and gauge this period based on a heuristic detection using accelerometry signals. Contingent on a prior automatic detection of the seizure, the methodology was able to classify all instances of immobility in the data set correctly. Another essential segment of this thesis highlights the detection of focal seizures with data from wearables. Focal seizures typically have very heterogeneous symptoms when regarded across patients. They include body movements of different kinds, responses of the autonomic nervous system, and psychological indications. The research included here analyzed only those focal seizures with specific movements of the limbs. An early exploratory study investigated the impact of the high variance of focal motor seizures on biosignals and the performance of seizure detection based on those signals. An additional study then considered individualized and generic models for detecting focal motor seizures based on the biosignals recorded by the wearable. The study found the optically measured blood volume pulse data to be highly impacted by noise from motion artifacts. Furthermore, generic models performed considerably worse than those specific to an individual patient, with high false alarm rates. Thus, for focal seizure detection, custom-made detection models for individual patients are likely to be the most robust methodology, and are specifically suitable for a subset of patients with epilepsy who experience characteristic seizures. This thesis concludes that while generic seizure detection models may be sufficient for highly convulsive seizures and under in-hospital conditions, they are currently not feasible for detecting focal seizures with fewer or no movements. Conversely, patient-specific detection methodologies are promising for non-convulsive motor seizures. Detection models that individualize over time may eventually become the best option for ultra-long-term seizure detection. Specifically, the included studies investigated detection systems based on supervised ensemble machine learning using physiological biosignal data. Results showed them to be feasible for detecting convulsive and less-convulsive seizures with manifestations including movements of the limbs.

• Data Quality Evaluation in Wearable Monitoring

  December 2022

  Böttcher, Sebastian; Vieluf, Solveig; Bruno, Elisa; Joseph, Boney; Epitashvili, Nino; Biondi, Andrea; Zabler, Nicolas; Glasstetter, Martin; Dümpelmann, Matthias; Van Laerhoven, Kristof; Nasseri, Mona; Brinkman, Benjamin H; Richardson, Mark P; Schulze-Bonhage, Andreas; Loddenkemper, Tobias

  Scientific Reports
  doi:10.1038/s41598-022-25949-x

  Abstract: Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37166 hours from 632 patients in the inpatient and 90776 hours from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring.

• Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients

  April 2022

  Böttcher, Sebastian; Bruno, Elisa; Epitashvili, Nino; Dümpelmann, Matthias; Zabler, Nicolas; Glasstetter, Martin; Ticcinelli, Valentina; Thorpe, Sarah; Lees, Simon; Van Laerhoven, Kristof; Richardson, Mark P.; Schulze-Bonhage, Andreas

  Sensors
  doi:10.3390/s22093318

  Abstract: Focal onset epileptic seizures are highly heterogeneous in their clinical manifestations, and a robust seizure detection across patient cohorts has to date not been achieved. Here, we assess and discuss the potential of supervised machine learning models for the detection of focal onset motor seizures by means of a wrist-worn wearable device, both in a personalized context as well as across patients. Wearable data were recorded in-hospital from patients with epilepsy at two epilepsy centers. Accelerometry, electrodermal activity, and blood volume pulse data were processed and features for each of the biosignal modalities were calculated. Following a leave-one-out approach, a gradient tree boosting machine learning model was optimized and tested in an intra-subject and inter-subject evaluation. In total, 20 seizures from 9 patients were included and we report sensitivities of 67% to 100% and false alarm rates of down to 0.85 per 24 h in the individualized assessment. Conversely, for an inter-subject seizure detection methodology tested on an out-of-sample data set, an optimized model could only achieve a sensitivity of 75% at a false alarm rate of 13.4 per 24 h. We demonstrate that robustly detecting focal onset motor seizures with tonic or clonic movements from wearable data may be possible for individuals, depending on specific seizure manifestations.

• Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation

  November 2021

  Böttcher, Sebastian; Bruno, Elisa; Manyakov, Nikolay V; Epitashvili, Nino; Claes, Kasper; Glasstetter, Martin; Thorpe, Sarah; Lees, Simon; Dümpelmann, Matthias; Van Laerhoven, Kristof; Richardson, Mark P; Schulze-Bonhage, Andreas

  JMIR mHealth and uHealth
  doi:10.2196/27674

  Abstract: Background: Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures. Objective: Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime. Methods: An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation. Results: In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data. Conclusions: We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection.

• Wearable devices for seizure detection: Practical experiences and recommendations from the Wearables for Epilepsy And Research (WEAR) International Study Group

  October 2021

  Bruno, Elisa; Böttcher, Sebastian; Viana, Pedro F; Amengual‐Gual, Marta; Joseph, Boney; Epitashvili, Nino; Dümpelmann, Matthias; Glasstetter, Martin; Biondi, Andrea; Laerhoven, Kristof; Loddenkemper, Tobias; Richardson, Mark P; Schulze‐Bonhage, Andreas; Brinkmann, Benjamin H.

  Epilepsia
  doi:10.1111/epi.17044

  Abstract: The Wearables for Epilepsy And Research (WEAR) International Study Group identified a set of methodology standards to guide research on wearable devices for seizure detection. We formed an international consortium of experts from clinical research, engineering, computer science, and data analytics at the beginning of 2020. The study protocols and practical experience acquired during the development of wearable research studies were discussed and analyzed during bi-weekly virtual meetings to highlight commonalities, strengths, and weaknesses, and to formulate recommendations. Seven major essential components of the experimental design were identified, and recommendations were formulated about: (1) description of study aims, (2) policies and agreements, (3) study population, (4) data collection and technical infrastructure, (5) devices, (6) reporting results, and (7) data sharing. Introducing a framework of methodology standards promotes optimal, accurate, and consistent data collection. It also guarantees that studies are generalizable and comparable, and that results can be replicated, validated, and shared.

• Post-ictal accelerometer silence as a marker of post-ictal immobility

  July 2020

  Bruno, Elisa; Böttcher, Sebastian; Biondi, Andrea; Epitashvili, Nino; Manyakov, Nikolay V.; Lees, Simon; Schulze-Bonhage, Andreas; Richardson, Mark P.

  Epilepsia
  doi:10.1111/epi.16552

  Abstract: Objective: Movement-based wearable sensors are used for detection of convulsive seizures. The identification of the absence of motion following a seizure, known as post-ictal immobility (PI), may represent a potential additional application of wearables. PI has been associated with potentially life-threatening complications and with sudden unexpected death in epilepsy (SUDEP). We aimed to assess whether wearable accelerometers (ACCs) could be used as a digital marker of PI. Method: Devices with embedded ACCs were worn by patients admitted to an epilepsy monitoring unit. Participants presenting with convulsive seizures were included in the study. PI presence and duration were assessed by experts reviewing video recordings. An algorithm for the automatic detection of post-ictal ACC silence and its duration was developed and the linear pairwise relationship between the automatically detected duration of post-ictal ACC silence and the duration of the expert-labeled PI was analyzed. Results: Twenty-two convulsive seizures were recorded from 18 study participants. Twenty were followed by PI and two by agitation. The automated estimation of post-ictal ACC silence identified all the 20 expert-labeled PI. The regression showed that the duration of the post-ictal ACC silence was correlated with the duration of PI (Pearson r =.92; P <.001), with the age of study participants (Pearson r =.78; P <.001), and with the duration of post-ictal generalized electroencephalography suppression (PGES; Pearson r =.4; P =.033). Significance: We highlight a novel application of wearables as a way to record post-ictal manifestations associated with an increased risk of SUDEP. The occurrence of a fatal seizure is unpredictable and the continuous, non-invasive, long-term identification of risk factors associated with each individual seizure may assume a great clinical importance.

• Using multimodal biosignal data from wearables to detect focal motor seizures in individual epilepsy patients

  September 2019

  Böttcher, Sebastian; Manyakov, Nikolay V.; Epitashvili, Nino; Folarin, Amos; Richardson, Mark P.; Dümpelmann, Matthias; Schulze-Bonhage, Andreas; Van Laerhoven, Kristof

  Proceedings of the 6th international Workshop on Sensor-based Activity Recognition and Interaction
  doi:10.1145/3361684.3361687

  Abstract: Epilepsy seizure detection with wearable devices is an emerging research field. As opposed to the gold standard, consisting of simultaneous video and EEG monitoring of patients, wearables have the advantage that they put a lower burden on epilepsy patients. We report on the first stages in a research effort that is dedicated to the development of a multimodal seizure detection system specifically for focal onset epileptic seizures. By in-depth analysis of data from three in-hospital patients with each having six to nine seizures recorded, we show that such seizures can manifest very differently and thus significantly impact classification. Using a Random Forest model on a rich set of features, we have obtained overall precision and recall scores of up to 0.92 and 0.72 respectively. These results show that the approach has validity, but we identify the type of focal seizure to be a critical factor for the classification performance.

• Detecting transitions in manual tasks from wearables: An unsupervised labeling approach

  March 2018

  Böttcher, Sebastian; Scholl, Philipp M.; Van Laerhoven, Kristof

  Informatics
  doi:10.3390/informatics5020016

  Abstract: Authoring protocols for manual tasks such as following recipes, manufacturing processes or laboratory experiments requires significant effort. This paper presents a system that estimates individual procedure transitions from the user's physical movement and gestures recorded with inertial motion sensors. Combined with egocentric or external video recordings, this facilitates efficient review and annotation of video databases. We investigate different clustering algorithms on wearable inertial sensor data recorded on par with video data, to automatically create transition marks between task steps. The goal is to match these marks to the transitions given in a description of the workflow, thus creating navigation cues to browse video repositories of manual work. To evaluate the performance of unsupervised algorithms, the automatically-generated marks are compared to human expert-created labels on two publicly-available datasets. Additionally, we tested the approach on a novel dataset in a manufacturing lab environment, describing an existing sequential manufacturing process. The results from selected clustering methods are also compared to some supervised methods.

• Detecting process transitions from wearable sensors: An unsupervised labeling approach

  September 2017

  Böttcher, Sebastian; Scholl, Philipp M.; Van Laerhoven, Kristof

  ACM International Conference Proceeding Series
  doi:10.1145/3134230.3134233

  Abstract: Authoring protocols for manual tasks such as following recipes, manufacturing processes, or laboratory experiments requires a significant effort. This paper presents a system that estimates individual procedure transitions from the user's physical movement and gestures recorded with inertial motion sensors. Combined with egocentric or external video recordings this facilitates efficient review and annotation of video databases. We investigate different clustering algorithms on wearable inertial sensor data recorded on par with video data, to automatically create transition marks between task steps. The goal is to match these marks to the transitions given in a description of the workflow, thus creating navigation cues to browse video repositories of manual work. To evaluate the performance of unsupervised clustering algorithms, the automatically generated marks are compared to human-expert created labels on publicly available datasets. Additionally, we tested the approach on a novel data set in a manufacturing lab environment, describing an existing sequential manufacturing process.

• Detecting Process Steps by Body Motion Sensors - Supervised versus Unsupervised Approaches

  January 2017

  Böttcher, Sebastian

  Master's Thesis

  Abstract: Activity recognition, the task of detecting distinct, characteristic motions of humans by analyzing a data stream of some kind, is a relatively recent field of research within the area of machine learning. It employs many kinds of classification methods to find recurring segments of data which are relevant to the application in question, and aims to do so with as little interaction from a user as possible. The work presented in this thesis concentrates on a specific group of application scenarios, namely sequential processes and the detection of steps therein. The underlying idea is to build a system that uses motion data from sensors placed on the human body to detect transitions between one step of a process to another, effectively partitioning data into relevant segments with a possible purpose of automatically indexing video archives for easier access. To this end, the thesis surveys state of the art methods and algorithms in activity recognition, both supervised (i.e. user-interactive in the learning phase) and unsupervised, and compiles a commented list of relevant research. To design a system capable of performing activity recognition in relevant example experiments, a practical command-line based tool is extended and employed to represent the activity recognition pipeline. The example experiments are finally used in an evaluation to find the methods and set of pipeline parameters that can achieve the goal of automatic segmentation of process steps most successfully. Five methods are thus evaluated on three different data sets representing real-world scenarios where sequential processes are used, while varying pipeline parameters are applied. The results show that while supervised as well as unsupervised methods can robustly detect process steps, unsupervised methods yield better scores for specific parameter sets over all experiment data sets. This thesis concludes that unsupervised methods are more capable of performing the task than supervised ones while also having no a priori human interaction overhead for labeling, however more in depth experimentation would have to be done to confirm the found optimal parameter combination, possibly focusing on one method alone.

• Techniques for Clearing Cluttered Scenes with Humanoids

  September 2013

  Böttcher, Sebastian

  Bachelor's Thesis

  Abstract: Humanoid robots are capable of performing a variety of tasks that mimic human behaviour in general better than other robots. With four limbs, a body and a head they have the potential to be as mobile and perceptive as a human. In reality however, they still oftentimes lack the precision and versatility to perform even the simplest tasks. In this thesis we consider the task of autonomously clearing, or tidying up, an area cluttered with various objects. We propose techniques that enable a humanoid robot to walk to and pick up distinct object types, carry them to a container and drop them there, all while avoiding other obstacles in the scene. To do this, we use an overlying state machine process to call on specific actions such as path planning, walk motions, pick up and drop motions, and obstacle avoidance actions. This thesis was developed in close collaboration with another Bachelor thesis that provides image processing and state estimation for the robot and its surroundings. The information gained from this work is used to formulate goals for the path planner, i.e., target points for the robot. We present experiments with a Nao humanoid robot, equipped with a depth camera, that evaluate the single components of the system as well as their combination. The robot is able to robustly clear a scene that was cluttered with several objects.

Co-Author 13

• Detection of interictal epileptiform discharges in an extended scalp EEG array and high‐density EEG – A prospective multicenter study

  March 2022

  Heers, Marcel; Böttcher, Sebastian; Kalina, Adam; Katletz, Stefan; Altenmüller, Dirk‐Matthias; Baroumand, Amir G.; Strobbe, Gregor; van Mierlo, Pieter; von Oertzen, Tim J.; Marusic, Petr; Schulze‐Bonhage, Andreas; Beniczky, Sándor; Dümpelmann, Matthias

  Epilepsia
  doi:10.1111/epi.17246

  Abstract: High counts of averaged interictal epileptiform discharges (IEDs) are key components of accurate interictal electric source imaging (ESI) in patients with focal epilepsy. Automated detections may be time-efficient, but they need to identify the correct IED types. Thus we compared semiautomated and automated detection of IED types in long-term video-EEG (electroencephalography) monitoring (LTM) using an extended scalp EEG array and short-term high-density EEG (hdEEG) with visual detection of IED types and the seizure-onset zone (SOZ). We prospectively recruited consecutive patients from four epilepsy centers who underwent both LTM with 40-electrode scalp EEG and short-term hdEEG with 256 electrodes. Only patients with a single circumscribed SOZ in LTM were included. In LTM and hdEEG, IED types were identified visually, semiautomatically and automatically. Concordances of semiautomated and automated detections in LTM and hdEEG, as well as visual detections in hdEEG, were compared against visually detected IED types and the SOZ in LTM. Fifty-two of 62 patients with LTM and hdEEG were included. The most frequent IED types per patient, detected semiautomatically and automatically in LTM and visually in hdEEG, were significantly concordant with the most frequently visually identified IED type in LTM and the SOZ. Semiautomated and automated detections of IED types in hdEEG were significantly concordant with visually identified IED types in LTM, only when IED types with more than 50 detected single IEDs were selected. The threshold of 50 detected IED in hdEEG was reached in half of the patients. For all IED types per patient, agreement between visual and semiautomated detections in LTM was high. Semiautomated and automated detections of IED types in LTM show significant agreement with visually detected IED types and the SOZ. In short-term hdEEG, semiautomated detections of IED types are concordant with visually detected IED types and the SOZ in LTM if high IED counts were detected.

• Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor

  September 2021

  Glasstetter, Martin; Böttcher, Sebastian; Zabler, Nicolas; Epitashvili, Nino; Dümpelmann, Matthias; Richardson, Mark P; Schulze-Bonhage, Andreas

  Sensors
  doi:10.3390/s21186017

  Abstract: Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.

• Automatisierte Anfallsdetektion mit Wearables: Welche Technologien für welche Biosignale?

  August 2021

  Schulze-Bonhage, Andreas; Böttcher, Sebastian; Zabler, Nicolas; Glasstetter, Martin; Dümpelmann, Matthias

  Zeitschrift für Epileptologie
  doi:10.1007/s10309-021-00421-4

  Abstract: Epileptic seizures can have motor, autonomic, cognitive or emotional manifestations. Devices for mobile monitoring can record these using sensors which capture specific biosignatures of these signs and classify them based thereon for seizure detection, seizure prediction and for a risk assessment. Presently the performance of wearables to detect seizures is best for motor seizures with tonic and clonic components; however, other seizure types are candidates for an objective seizure detection as well. The combination with ultralong electroencephalography (EEG) recordings widens the spectrum of seizure types to be captured and opens up a perspective for a qualitatively new era of treatment monitoring.

• Teaching Embedded Systems by Constructing an Escape Room

  March 2021

  Pfeifer, Marc; Völker, Benjamin; Böttcher, Sebastian; Köhler, Sven; Scholl, Philipp M.

  Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
  doi:10.1145/3408877.3432485

  Abstract: Embedded systems are the foundation of many of today's consumer products and industrial applications - and they are increasingly connected. To teach this topic we created a course with the overarching goal of designing and constructing an automated escape room. This provided motivation and opportunity for students to learn the engineering and soft skills required for building networked embedded systems. The game was open for faculty members and friends of the students after the course concluded. By dividing the building process into multiple tasks, such as individual puzzles, the presented concept encourages inter- and intra-group work, including conceptualizing, designing and developing reliable, connected embedded systems. In this paper we first present the motivation, context, and pedagogical approach of the course, then describe the course structure and conclude with experiences from constructing an escape room with multiple groups of students.

• Remote assessment of disease and relapse in epilepsy: Protocol for a multicenter prospective cohort study

  December 2020

  Bruno, Elisa; Biondi, Andrea; Böttcher, Sebastian; Vértes, Gergely; Dobson, Richard; Folarin, Amos; Ranjan, Yatharth; Rashid, Zulqarnain; Manyakov, Nikolay; Rintala, Aki; Myin-Germeys, Inez; Simblett, Sara; Wykes, Til; Stoneman, Amanda; Little, Ann; Thorpe, Sarah; Lees, Simon; Schulze-Bonhage, Andreas; Richardson, Mark

  JMIR Research Protocols
  doi:10.2196/21840

  Abstract: Background: In recent years, a growing body of literature has highlighted the role of wearable and mobile remote measurement technology (RMT) applied to seizure detection in hospital settings, whereas more limited evidence has been produced in the community setting. In clinical practice, seizure assessment typically relies on self-report, which is known to be highly unreliable. Moreover, most people with epilepsy self-identify factors that lead to increased seizure likelihood, including mood, behavior, sleep pattern, and cognitive alterations, all of which are amenable to measurement via multiparametric RMT. Objective: The primary aim of this multicenter prospective cohort study is to assess the usability, feasibility, and acceptability of RMT in the community setting. In addition, this study aims to determine whether multiparametric RMT collected in populations with epilepsy can prospectively estimate variations in seizure occurrence and other outcomes, including seizure frequency, quality of life, and comorbidities. Methods: People with a diagnosis of pharmacoresistant epilepsy will be recruited in London, United Kingdom, and Freiburg, Germany. Participants will be asked to wear a wrist-worn device and download ad hoc apps developed on their smartphones. The apps will be used to collect data related to sleep, physical activity, stress, mood, social interaction, speech patterns, and cognitive function, both passively from existing smartphone sensors (passive remote measurement technology [pRMT]) and actively via questionnaires, tasks, and assessments (active remote measurement technology [aRMT]). Data will be collected continuously for 6 months and streamed to the Remote Assessment of Disease and Relapse-base (RADAR-base) server. Results: The RADAR Central Nervous System project received funding in 2015 from the Innovative Medicines Initiative 2 Joint Undertaking under Grant Agreement No. 115902. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations. Ethical approval was obtained in London from the Bromley Research Ethics Committee (research ethics committee reference: 19/LO/1884) in January 2020. The first participant was enrolled on September 30, 2020. Data will be collected until September 30, 2021. The results are expected to be published at the beginning of 2022. Conclusions: RADAR Epilepsy aims at developing a framework of continuous data collection intended to identify ictal and preictal states through the use of aRMT and pRMT in the real-life environment. The study was specifically designed to evaluate the clinical usefulness of the data collected via new technologies and compliance, technology acceptability, and usability for patients. These are key aspects to successful adoption and implementation of RMT as a new way to measure and manage long-term disorders.

• Day and night comfort and stability on the body of four wearable devices for seizure detection: A direct user-experience

  November 2020

  Bruno, Elisa; Biondi, Andrea; Böttcher, Sebastian; Lees, Simon; Schulze-Bonhage, Andreas; Richardson, Mark P.

  Epilepsy & Behavior
  doi:10.1016/j.yebeh.2020.107478

  Abstract: Purpose: Wearable devices are progressively becoming an available tool for continuous seizure detection. Motivation to use wearables is not only driven by the accuracy and reliability of the performance but also by the form factor, comfort, and stability on the body. We collected direct feedback and device placement-related issues experienced by a cohort of people with epilepsy (PWE) to investigate to what extent available devices are nonintrusive, comfortable, and stable on the body. Methods: Four models of wearable devices (E4 wrist band, Everion upper arm band, IMEC upper arm band, and Epilog scalp patch electrodes) were worn by PWE who were admitted to two epilepsy monitoring units (EMUs) in London and Freiburg. Participants were periodically reviewed, and accidental displacements of the devices were annotated. Participants' experience was assessed using the Technology Acceptance Model Fast Form (TAM-FF) plus two additional questions on comfort. A thematic analysis was also performed on the free text of the questionnaire. Results: One hundred and fifteen participants were enrolled. The devices had a good stability on the body including during seizures. Overall, all the devices were considered comfortable to be worn, including during sleep. However, devices containing wires and patches demonstrated a lesser degree of stability on the body and were judged less positively. Participants age was correlated with TAM-FF mean scores, and older participants judged the devices less favorably compared with younger participants. Discussion: Removable but securely fitted, wireless, and comfortable designs were considered more appropriate for a continuous monitoring aimed at seizure detection. Some caution may be required when patch electrodes and electrodes glued to the skin or to the scalp are used, as those evaluated in the present study demonstrated a lower level of acceptability and a lower degree of stability to the body, especially at night. These factors could limit a continuous monitoring decreasing the device performance for nocturnal, unsupervised seizures which are at higher risk of lethality.

• Signal quality and patient experience with wearable devices for epilepsy management

  November 2020

  Nasseri, Mona; Nurse, Ewan; Glasstetter, Martin; Böttcher, Sebastian; Gregg, Nicholas M.; Laks Nandakumar, Aiswarya; Joseph, Boney; Pal Attia, Tal; Viana, Pedro F.; Bruno, Elisa; Biondi, Andrea; Cook, Mark; Worrell, Gregory A.; Schulze‐Bonhage, Andreas; Dümpelmann, Matthias; Freestone, Dean R.; Richardson, Mark P.; Brinkmann, Benjamin H.

  Epilepsia
  doi:10.1111/epi.16527

  Abstract: Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor-quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in-hospital or in-home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high-quality, marginal-quality, or poor-quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good-quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good-quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good-, marginal-, and poor-quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist-worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high-quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.

• Mobile seizure monitoring in epilepsy patients

  December 2019

  Schulze-Bonhage, A.; Böttcher, S.; Glasstetter, M.; Epitashvili, N.; Bruno, E.; Richardson, M.; v. Laerhoven, K.; Dümpelmann, M.

  Nervenarzt
  doi:10.1007/s00115-019-00822-x

  Abstract: Wearables are receiving much attention from both epilepsy patients and treating physicians, for monitoring of seizure frequency and warning of seizures. They are also of interest for the detection of seizure-associated risks of patients, for differential diagnosis of rare seizure types and prediction of seizure-prone periods. Accelerometry, electromyography (EMG), heart rate and further autonomic parameters are recorded to capture clinical seizure manifestations. Currently, a clinical use to document nocturnal motor seizures is feasible. In this review the available devices, data on the performance in the documentation of seizures, current options for clinical use and developments in data analysis are presented and critically discussed.

• RADAR-base: Open source mobile health platform for collecting, monitoring, and analyzing data using sensors, wearables, and mobile devices

  August 2019

  Ranjan, Yatharth; Rashid, Zulqarnain; Stewart, Callum; Conde, Pauline; Begale, Mark; Verbeeck, Denny; Boettcher, Sebastian; Hyve, The; Dobson, Richard; Folarin, Amos

  Journal of Medical Internet Research
  doi:10.2196/11734

  Abstract: Background: With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. Objective: Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. Methods: RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. Results: General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. Conclusions: RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

• Poster: RADAR-base: A novel open source m-health platform

  October 2018

  Ranjan, Yatharth; Kerz, Maximilian; Rashid, Zulqarnain; Böttcher, Sebastian; Dobson, Richard J.B; Folarin, Amos A.

  UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers
  doi:10.1145/3267305.3267579

  Abstract: Smartphones with embedded and connected sensors are playing vital role in healthcare through various apps and mHealth platforms. RADAR-base is a modern mHealth data collection platform built around Confluent and Apache Kafka. RADAR-base enables study design and set up, active and passive remote data collection. It provides secure data transmission, and scalable solutions for data storage, management and access. The application is used presently in RADAR-CNS study to collect data from patients suffering from Multiples Sclerosis, Depression and Epilepsy. Beyond RADAR-CNS, RADAR-base is being deployed across a number of other funded research programmes.

• Poster: RADAR-base: Epilepsy case study

  October 2018

  Rashid, Zulqarnain; Stewart, Callum L.; Ranjan, Yatharth; Dobson, Richard J.B; Böttcher, Sebastian; Folarin, Amos A.

  UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers
  doi:10.1145/3267305.3267578

  Abstract: The traditional hospital set-up is not appropriate for long-term epilepsy seizure detection in naturalistic ambulatory settings. To explore the feasibility of seizure detection in such a setting, an in-hospital study was conducted to evaluate three wearable devices and a data collection platform for ambulatory seizure detection. The platform collects and processes data for study administrators, clinicians and data scientists, who use it to create models to detect seizures. For that purpose, all data collected from the wearable devices is additionally synchronized with the hospital EEG and video, with gold-standard seizure labels provided by trained clinicians. Data collected by wearable devices shows potential for seizure detection in out-of-hospital based and ambulatory settings.

• Advanced projects and applications for embedded systems engineering on E2LP Platform

  January 2016

  Grgić, Dario; Böttcher, Sebastian; Pfeifer, Marc; Scherle, Johannes; Völker, Benjamin; Burchard, Jan; Sester, Sebastian; Reindl, Leonhard M.

  Advances in Intelligent Systems and Computing
  doi:10.1007/978-3-319-27540-6_8

  Abstract: The E2LP-Platform is capable to impart many different fields of learning content. Starting from simple, short exercises also complex projects can be realized covering up to several weeks of workload. This work presents a documentation of four students projects developed and performed at University of Freiburg in the Advanced Embedded System Laboratory. This laboratory contains a dynamic classroom approach in which the required laboratory hardware is mobile. Exploring real-wold challenges and problems motivates the participants to acquire a deeper knowledge.

• Mobile manipulation in cluttered environments with humanoids: Integrated perception, task planning, and action execution

  November 2015

  Hornung, Armin; Böttcher, Sebastian; Schlagenhauf, Jonas; Dornhege, Christian; Hertle, Andreas; Bennewitz, Maren

  IEEE-RAS International Conference on Humanoid Robots
  doi:10.1109/HUMANOIDS.2014.7041451

  Abstract: To autonomously carry out complex mobile manipulation tasks, a robot control system has to integrate several components for perception, world modeling, action planning and replanning, navigation, and manipulation. In this paper, we present a modular framework that is based on the Temporal Fast Downward Planner and supports external modules to control the robot. This allows to tightly integrate individual sub-systems with the high-level symbolic planner and enables a humanoid robot to solve challenging mobile manipulation tasks. In the work presented here, we address mobile manipulation with humanoids in cluttered environments, particularly the task of collecting objects and delivering them to designated places in a home-like environment while clearing obstacles out of the way. We implemented our system for a Nao humanoid tidying up a room, i.e., the robot has to collect items scattered on the floor, move obstacles out of its way, and deliver the objects to designated target locations. Despite the limited sensing and motion capabilities of the low-cost platform, the experiments show that our approach results in reliable task execution by applying monitoring actions to verify object and robot states.