This research focuses on creating composite biomarkers that can classify diagnoses, estimate symptom severity, and detect treatment effects using data from wearable sensors and smartphone... Show moreThis research focuses on creating composite biomarkers that can classify diagnoses, estimate symptom severity, and detect treatment effects using data from wearable sensors and smartphone applications. The thesis consists of an introduction to machine learning techniques and their use in developing biomarkers for the central nervous system; a narrative review of the relevant literature; and detailed studies on the application of these techniques in various health conditions. Specifically, the research includes observational and cross-sectional studies on facioscapulohumeral muscular dystrophy (FSHD) and major depressive disorder (MDD), demonstrating how smartphone and wearable sensor data can be used to monitor disease severity and progression. Additionally, the research identified the use of a tablet-based finger tapping task to monitor the real-time effects of antiparkinson's drugs on Parkinson's symptom severity. Key findings highlight the potential of mHealth biomarkers to provide continuous, real-time monitoring of patients, which can enhance the accuracy of clinical assessments and potentially reduce the burden on patients and healthcare systems. The thesis also addresses the challenges of variability in mHealth device data and emphasizes the need for robust validation and standardization to ensure the reliability of these biomarkers in clinical settings. Show less
Epileptic seizures are associated with changes in autonomic function. Ictal asystole, when it leads to syncope, can cause severe traumatic falls. We discovered a new indirect method, based on video... Show moreEpileptic seizures are associated with changes in autonomic function. Ictal asystole, when it leads to syncope, can cause severe traumatic falls. We discovered a new indirect method, based on video, EEG and ECG, to disentangle if vasodilatation was the dominant mechanism behind the syncope. In this group of patients, pacemaker implantation is less helpful. Autonomic manifestations of epilepsy can also help to detect seizures. Our literature review discovered that combining different modalities in one detection device provides higher sensitivity, and personalization of detection algorithms can decrease false alarm rate. We validated a wearable multimodal detection system (NightWatch) on children at home. NightWatch showed high sensitivity for the detection of potentially dangerous nocturnal seizures, reduces caregiver stress and saved costs from a societal perspective. Validation of an automated video detection system showed that this could provide a good alternative for children who cannot tolerate a wearable device.From different qualitative user studies, we concluded that caregivers’ needs for seizure detection vary greatly. Also, the success of device implementation is highly dependent on the protective behavior parents developed towards their child with epilepsy. This emphasizes the importance of tailored and user-centered approaches for seizure detection. Show less
Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older... Show moreThrough the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being. Show less
Paraschiakos, S.; Sa, C.R. de; Okai, J.; Slagboom, P.E.; Beekman, M.; Knobbe, A. 2022
Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older... Show moreThrough the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being. Show less
Westrhenen, A. van; Souhoka, T.; Ballieux, M.E.; Thijs, R.D. 2021
Introduction: User preferences for seizure detection devices (SDDs) have been previously assessed using surveys and interviews, but these have not addressed the latent needs and wishes. Context... Show moreIntroduction: User preferences for seizure detection devices (SDDs) have been previously assessed using surveys and interviews, but these have not addressed the latent needs and wishes. Context mapping is an approach in which designers explore users' dreams and fears to anticipate potential future experiences and optimize the product design.Methods: A generative group session was held using the context mapping approach. Two types of nocturnal SDD users were included: three professional caregivers at a residential care facility and two informal caregivers of children with refractory epilepsy and learning disabilities. Participants were invited to share their personal SDD experiences and briefed to make their needs and wishes explicit. The audiotaped session was transcribed and analyzed together with the collected material using inductive content analysis. The qualitative data was classified by coding the content, grouping codes into categories and themes, and combining those into general statements (abstraction).Results: "Trust" emerged as the most important theme, entangling various emotional and practical factors that influence caregiver's trust in a device. Caregivers expressed several factors that could help to gain their trust in an SDD, including integration of different modalities, insight on all parameters overnight, personal adjustment of the algorithm, recommendation by a neurologist, and a set-up period. Needs regarding alerting seemed to differ between the two types of caregivers in our study: professional caregivers preferred to be alerted only for potentially dangerous seizures, whereas informal caregivers emphasized the urge to be alerted for every event, thus indicating the need for personal adjustment of SDD settings.Conclusion: In this explorative study, we identified several key elements for nocturnal SDD implementation including the importance of gaining trust and the possibility to adjust SDD settings for different types of caregivers. (C) 2020 The Author(s). Published by Elsevier Inc. Show less
This thesis describes several aspects of diagnostic and therapeutic possibilities of mitochondrial function in clinical pharmacological. During several clinical studies in healthy volunteers, pre... Show moreThis thesis describes several aspects of diagnostic and therapeutic possibilities of mitochondrial function in clinical pharmacological. During several clinical studies in healthy volunteers, pre-frail elderly and Huntington’s disease patients, we used phosphorous magnetic resonance spectroscopy as the main method to measure mitochondrial function in vivo. Other in vivo methods included Near Infrared Spectroscopy and the novel Protoporphorin-9 Triplet State Lifetime Technique, besides in vitro ELISA methods to measure activity of separate complexes of the mitochondrial electron transport chain. Using these modalities, we showed mitochondrial dysfunction in pre-frail elderly, emphasizing the importance of an active lifestyle in the prevention of sarcopenia and frailty. Using the mitotoxicity of simvastatin, and its reversibility by ubiquinol, we validated the first proof-of-pharmacology model in healthy volunteers to evaluate efficacy of novel mitochondrial function improving compounds. We also described the importance of measuring (mitochondrial) oxygen consumption as a means to measure mitotoxicity of commonly described medications. Lastly, we evaluated the safety and efficacy of the novel compound SBT-020 in a placebo-controlled, double-blinded, randomized controlled trial in mild to moderate Huntington’s disease patients and compared central to peripheral mitochondrial function for the first time, gaining inside into therapeutic possibilities in this complex and devastating disease. Show less
Westrhenen, A. van; Souhoka, T.; Ballieux, M.E.; Thijs, R.D. 2021
Introduction: User preferences for seizure detection devices (SDDs) have been previously assessed using surveys and interviews, but these have not addressed the latent needs and wishes. Context... Show moreIntroduction: User preferences for seizure detection devices (SDDs) have been previously assessed using surveys and interviews, but these have not addressed the latent needs and wishes. Context mapping is an approach in which designers explore users' dreams and fears to anticipate potential future experiences and optimize the product design.Methods: A generative group session was held using the context mapping approach. Two types of nocturnal SDD users were included: three professional caregivers at a residential care facility and two informal caregivers of children with refractory epilepsy and learning disabilities. Participants were invited to share their personal SDD experiences and briefed to make their needs and wishes explicit. The audiotaped session was transcribed and analyzed together with the collected material using inductive content analysis. The qualitative data was classified by coding the content, grouping codes into categories and themes, and combining those into general statements (abstraction).Results: "Trust" emerged as the most important theme, entangling various emotional and practical factors that influence caregiver's trust in a device. Caregivers expressed several factors that could help to gain their trust in an SDD, including integration of different modalities, insight on all parameters overnight, personal adjustment of the algorithm, recommendation by a neurologist, and a set-up period. Needs regarding alerting seemed to differ between the two types of caregivers in our study: professional caregivers preferred to be alerted only for potentially dangerous seizures, whereas informal caregivers emphasized the urge to be alerted for every event, thus indicating the need for personal adjustment of SDD settings.Conclusion: In this explorative study, we identified several key elements for nocturnal SDD implementation including the importance of gaining trust and the possibility to adjust SDD settings for different types of caregivers. (C) 2020 The Author(s). Published by Elsevier Inc. Show less
Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the... Show moreMeasuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students’ physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps. Show less
Paraschiakos, S.; Cachucho, R.E.; Moed, M.; Heemst, D. van; Mooijaart, S.P.; Slagboom, E.P.; ... ; Beekman, M. 2020
A population group that is often overlooked in the recent revolution of self-tracking is the group of older people. This growing proportion of the general population is often faced with increasing... Show moreA population group that is often overlooked in the recent revolution of self-tracking is the group of older people. This growing proportion of the general population is often faced with increasing health issues and discomfort. In order to come up with lifestyle advice towards the elderly, we need the ability to quantify their lifestyle, before and after an intervention. This research focuses on the task of activity recognition (AR) from accelerometer data. With that aim, we collect a substantial labelled dataset of older individuals wearing multiple devices simultaneously and performing a strict protocol of 16 activities (the GOTOV dataset, 𝑁=28N=28). Using this dataset, we trained Random Forest AR models, under varying sensor set-ups and levels of activity description granularity. The model that combines ankle and wrist accelerometers (GENEActiv) produced the best results (accuracy >80%>80%) for 16-class classification. At the same time, when additional physiological information is used, the accuracy increased (>85%>85%). To further investigate the role of granularity in our predictions, we developed the LARA algorithm, which uses a hierarchical ontology that captures prior biological knowledge to increase or decrease the level of activity granularity (merge classes). As a result, a 12-class model in which the different paces of walking were merged showed a performance above 93%93%. Testing this 12-class model in labelled free-living pilot data, the mean balanced accuracy appeared to be reasonably high, while using the LARA algorithm, we show that a 7-class model (lying down, sitting, standing, household, walking, cycling, jumping) was optimal for accuracy and granularity. Finally, we demonstrate the use of the latter model in unlabelled free-living data from a larger lifestyle intervention study. In this paper, we make the validation data as well as the derived prediction models available to the community. Show less
