2023

Food Choices after Cognitive Load: An Affective Computing Approach. Kappattanavar, Arpita Mallikarjuna; Hecker, Pascal; Moontaha, Sidratul; Steckhan, Nico; Arnrich, Bert in Sensors (2023). 23(14)
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
Psychology and nutritional science research has highlighted the impact of negative emotions and cognitive load on calorie consumption behaviour using subjective questionnaires. Isolated studies in other domains objectively assess cognitive load without considering its effects on eating behaviour. This study aims to explore the potential for developing an integrated eating behaviour assistant system that incorporates cognitive load factors. Two experimental sessions were conducted using custom-developed experimentation software to induce different stimuli. During these sessions, we collected 30 h of physiological, food consumption, and affective states questionnaires data to automatically detect cognitive load and analyse its effect on food choice. Utilising grid search optimisation and leave-one-subject-out cross-validation, a support vector machine model achieved a mean classification accuracy of 85.12% for the two cognitive load tasks using eight relevant features. Statistical analysis was performed on calorie consumption and questionnaire data. Furthermore, 75% of the subjects with higher negative affect significantly increased consumption of specific foods after high-cognitive-load tasks. These findings offer insights into the intricate relationship between cognitive load, affective states, and food choice, paving the way for an eating behaviour assistant system to manage food choices during cognitive load. Future research should enhance system capabilities and explore real-world applications.
@article{kappattanavar2023choices, abstract = {Psychology and nutritional science research has highlighted the impact of negative emotions and cognitive load on calorie consumption behaviour using subjective questionnaires. Isolated studies in other domains objectively assess cognitive load without considering its effects on eating behaviour. This study aims to explore the potential for developing an integrated eating behaviour assistant system that incorporates cognitive load factors. Two experimental sessions were conducted using custom-developed experimentation software to induce different stimuli. During these sessions, we collected 30 h of physiological, food consumption, and affective states questionnaires data to automatically detect cognitive load and analyse its effect on food choice. Utilising grid search optimisation and leave-one-subject-out cross-validation, a support vector machine model achieved a mean classification accuracy of 85.12% for the two cognitive load tasks using eight relevant features. Statistical analysis was performed on calorie consumption and questionnaire data. Furthermore, 75% of the subjects with higher negative affect significantly increased consumption of specific foods after high-cognitive-load tasks. These findings offer insights into the intricate relationship between cognitive load, affective states, and food choice, paving the way for an eating behaviour assistant system to manage food choices during cognitive load. Future research should enhance system capabilities and explore real-world applications.}, author = {Kappattanavar, Arpita Mallikarjuna and Hecker, Pascal and Moontaha, Sidratul and Steckhan, Nico and Arnrich, Bert}, journal = {Sensors}, keywords = {arpitakappattanavar bertarnrich nicosteckhan pascalhecker sidratulmoontaha}, number = 14, title = {Food Choices after Cognitive Load: An Affective Computing Approach}, volume = 23, year = 2023 }
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URNhttp://dx.doi.org/10.3390/s23146597
AbstractPsychology and nutritional science research has highlighted the impact of negative emotions and cognitive load on calorie consumption behaviour using subjective questionnaires. Isolated studies in other domains objectively assess cognitive load without considering its effects on eating behaviour. This study aims to explore the potential for developing an integrated eating behaviour assistant system that incorporates cognitive load factors. Two experimental sessions were conducted using custom-developed experimentation software to induce different stimuli. During these sessions, we collected 30 h of physiological, food consumption, and affective states questionnaires data to automatically detect cognitive load and analyse its effect on food choice. Utilising grid search optimisation and leave-one-subject-out cross-validation, a support vector machine model achieved a mean classification accuracy of 85.12% for the two cognitive load tasks using eight relevant features. Statistical analysis was performed on calorie consumption and questionnaire data. Furthermore, 75% of the subjects with higher negative affect significantly increased consumption of specific foods after high-cognitive-load tasks. These findings offer insights into the intricate relationship between cognitive load, affective states, and food choice, paving the way for an eating behaviour assistant system to manage food choices during cognitive load. Future research should enhance system capabilities and explore real-world applications.

Design, Implementation and Evaluation of an Air Quality Sonification System. Terboven, Carla; Panourgia, Eleni-Ira; Arnrich, Bert; Steckhan, Nico (2023).
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@misc{icad, author = {Terboven, Carla and Panourgia, Eleni-Ira and Arnrich, Bert and Steckhan, Nico}, keywords = {bertarnrich nicosteckhan}, title = {Design, Implementation and Evaluation of an Air Quality Sonification System}, year = 2023 }
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2022

Effects of a Supplement Containing a Cranberry Extract on Recurrent Urinary Tract Infections and Intestinal Microbiota: A Prospective, Uncontrolled Exploratory Study. Jeitler, Michael; Michalsen, Andreas; Schwiertz, Andreas; Kessler, Christian S.; Koppold-Liebscher, Daniela; Grasme, Julia; Kandil, Farid I.; Steckhan, Nico in Journal of Integrative and Complementary Medicine (2022). 28(5) 399–406.
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@article{Jeitler2022, author = {Jeitler, Michael and Michalsen, Andreas and Schwiertz, Andreas and Kessler, Christian S. and Koppold-Liebscher, Daniela and Grasme, Julia and Kandil, Farid I. and Steckhan, Nico}, journal = {Journal of Integrative and Complementary Medicine}, keywords = {bertarnrich nicosteckhan}, month = {may}, number = 5, pages = {399–406}, publisher = {Mary Ann Liebert Inc}, title = {Effects of a Supplement Containing a Cranberry Extract on Recurrent Urinary Tract Infections and Intestinal Microbiota: A Prospective, Uncontrolled Exploratory Study}, volume = 28, year = 2022 }
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URNhttp://dx.doi.org/10.1089/jicm.2021.0300

Mental and Behavioural Responses to Bahá’’i Fasting: Looking behind the Scenes of a Religiously Motivated Intermittent Fast Using a Mixed Methods Approach. Ring, Raphaela M.; Eisenmann, Clemens; Kandil, Farid I.; Steckhan, Nico; Demmrich, Sarah; Klatte, Caroline; Kessler, Christian S.; Jeitler, Michael; Boschmann, Michael; Michalsen, Andreas; Blakeslee, Sarah B.; Stöckigt, Barbara; Stritter, Wiebke; Koppold-Liebscher, Daniela A. in Nutrients (2022). 14(5) 1038.
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@article{Ring2022, author = {Ring, Raphaela M. and Eisenmann, Clemens and Kandil, Farid I. and Steckhan, Nico and Demmrich, Sarah and Klatte, Caroline and Kessler, Christian S. and Jeitler, Michael and Boschmann, Michael and Michalsen, Andreas and Blakeslee, Sarah B. and St{ö}ckigt, Barbara and Stritter, Wiebke and Koppold-Liebscher, Daniela A.}, journal = {Nutrients}, keywords = {bertarnrich nicosteckhan}, month = {feb}, number = 5, pages = 1038, publisher = {{MDPI} {AG}}, title = {Mental and Behavioural Responses to Bah{{á}}'{\'{\i}} Fasting: Looking behind the Scenes of a Religiously Motivated Intermittent Fast Using a Mixed Methods Approach}, volume = 14, year = 2022 }
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URNhttp://dx.doi.org/10.3390/nu14051038

Voice Analysis for Neurological Disorder Recognition - A Systematic Review and Perspective on Emerging Trends. Hecker, Pascal; Steckhan, Nico; Eyben, Florian; Schuller, Björn W.; Arnrich, Bert in Frontiers in Digital Health (2022). 4
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Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.
@article{Hecker2022, abstract = {Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.}, author = {Hecker, Pascal and Steckhan, Nico and Eyben, Florian and Schuller, Bj{ö}rn W. and Arnrich, Bert}, journal = {Frontiers in Digital Health}, keywords = {bertarnrich nicosteckhan pascalhecker}, month = {jul}, publisher = {Frontiers Media {SA}}, title = {Voice Analysis for Neurological Disorder Recognition - A Systematic Review and Perspective on Emerging Trends}, volume = 4, year = 2022 }
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URNhttp://dx.doi.org/10.3389/fdgth.2022.842301
AbstractQuantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.

2021

Effects of Early vs. Late Time-Restricted Eating on Cardiometabolic Health, Inflammation, and Sleep in Overweight and Obese Women: A Study Protocol for the ChronoFast Trial. Peters, Beeke; Koppold-Liebscher, Daniela A.; Schuppelius, Bettina; Steckhan, Nico; Pfeiffer, Andreas F. H.; Kramer, Achim; Michalsen, Andreas; Pivovarova-Ramich, Olga in Frontiers in Nutrition (2021). 8
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@article{Peters2021, author = {Peters, Beeke and Koppold-Liebscher, Daniela A. and Schuppelius, Bettina and Steckhan, Nico and Pfeiffer, Andreas F. H. and Kramer, Achim and Michalsen, Andreas and Pivovarova-Ramich, Olga}, journal = {Frontiers in Nutrition}, keywords = {bertarnrich nicosteckhan}, month = {nov}, publisher = {Frontiers Media {SA}}, title = {Effects of Early vs. Late Time-Restricted Eating on Cardiometabolic Health, Inflammation, and Sleep in Overweight and Obese Women: A Study Protocol for the {ChronoFast} Trial}, volume = 8, year = 2021 }
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URNhttp://dx.doi.org/10.3389/fnut.2021.765543

2020

HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population. Morassi Sasso, Ariane; Datta, Suparno; Jeitler, Michael; Steckhan, Nico; Kessler, Christian S.; Michalsen, Andreas; Arnrich, Bert; Böttinger, Erwin M. Michalowski, R. Moskovitch (eds.) (2020). (Vol. 12299)
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
The state of the art for monitoring hypertension relies on measuring blood pressure (BP) using uncomfortable cuff-based devices. Hence, for increased adherence in monitoring, a better way of measuring BP is needed. That could be achieved through comfortable wearables that contain photoplethysmography (PPG) sensors. There have been several studies showing the possibility of statistically estimating systolic and diastolic BP (SBP/DBP) from PPG signals. However, they are either based on measurements of healthy subjects or on patients on (ICUs). Thus, there is a lack of studies with patients out of the normal range of BP and with daily life monitoring out of the ICUs. To address this, we created a dataset (HYPE) composed of data from hypertensive subjects that executed a stress test and had 24-h monitoring. We then trained and compared machine learning (ML) models to predict BP. We evaluated handcrafted feature extraction approaches vs image representation ones and compared different ML algorithms for both. Moreover, in order to evaluate the models in a different scenario, we used an openly available set from a stress test with healthy subjects (EVAL). The best results for our HYPE dataset were in the stress test and had a mean absolute error (MAE) in mmHg of 8.79 (±3.17) for SBP and 6.37 (±2.62) for DBP; for our EVAL dataset it was 14.74 (±4.06) and 7.12 (±2.32) respectively. Although having tested a range of signal processing and ML techniques, we were not able to reproduce the small error ranges claimed in the literature. The mixed results suggest a need for more comparative studies with subjects out of the intensive care and across all ranges of blood pressure. Until then, the clinical relevance of PPG-based predictions in daily life should remain an open question.
@inproceedings{MorassiSasso2020, abstract = {The state of the art for monitoring hypertension relies on measuring blood pressure (BP) using uncomfortable cuff-based devices. Hence, for increased adherence in monitoring, a better way of measuring BP is needed. That could be achieved through comfortable wearables that contain photoplethysmography (PPG) sensors. There have been several studies showing the possibility of statistically estimating systolic and diastolic BP (SBP/DBP) from PPG signals. However, they are either based on measurements of healthy subjects or on patients on (ICUs). Thus, there is a lack of studies with patients out of the normal range of BP and with daily life monitoring out of the ICUs. To address this, we created a dataset (HYPE) composed of data from hypertensive subjects that executed a stress test and had 24-h monitoring. We then trained and compared machine learning (ML) models to predict BP. We evaluated handcrafted feature extraction approaches vs image representation ones and compared different ML algorithms for both. Moreover, in order to evaluate the models in a different scenario, we used an openly available set from a stress test with healthy subjects (EVAL). The best results for our HYPE dataset were in the stress test and had a mean absolute error (MAE) in mmHg of 8.79 (±3.17) for SBP and 6.37 (±2.62) for DBP; for our EVAL dataset it was 14.74 (±4.06) and 7.12 (±2.32) respectively. Although having tested a range of signal processing and ML techniques, we were not able to reproduce the small error ranges claimed in the literature. The mixed results suggest a need for more comparative studies with subjects out of the intensive care and across all ranges of blood pressure. Until then, the clinical relevance of PPG-based predictions in daily life should remain an open question.}, author = {Morassi Sasso, Ariane and Datta, Suparno and Jeitler, Michael and Steckhan, Nico and Kessler, Christian S. and Michalsen, Andreas and Arnrich, Bert and Böttinger, Erwin}, booktitle = {Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science}, editor = {Michalowski, M. and Moskovitch, R.}, keywords = {bertarnrich nicosteckhan}, publisher = {Springer}, title = {HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population}, volume = 12299, year = 2020 }
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HerausgeberMichalowski, M. and Moskovitch, R.
URNhttp://dx.doi.org/https://doi.org/10.1007/978-3-030-59137-3_29
AbstractThe state of the art for monitoring hypertension relies on measuring blood pressure (BP) using uncomfortable cuff-based devices. Hence, for increased adherence in monitoring, a better way of measuring BP is needed. That could be achieved through comfortable wearables that contain photoplethysmography (PPG) sensors. There have been several studies showing the possibility of statistically estimating systolic and diastolic BP (SBP/DBP) from PPG signals. However, they are either based on measurements of healthy subjects or on patients on (ICUs). Thus, there is a lack of studies with patients out of the normal range of BP and with daily life monitoring out of the ICUs. To address this, we created a dataset (HYPE) composed of data from hypertensive subjects that executed a stress test and had 24-h monitoring. We then trained and compared machine learning (ML) models to predict BP. We evaluated handcrafted feature extraction approaches vs image representation ones and compared different ML algorithms for both. Moreover, in order to evaluate the models in a different scenario, we used an openly available set from a stress test with healthy subjects (EVAL). The best results for our HYPE dataset were in the stress test and had a mean absolute error (MAE) in mmHg of 8.79 (±3.17) for SBP and 6.37 (±2.62) for DBP; for our EVAL dataset it was 14.74 (±4.06) and 7.12 (±2.32) respectively. Although having tested a range of signal processing and ML techniques, we were not able to reproduce the small error ranges claimed in the literature. The mixed results suggest a need for more comparative studies with subjects out of the intensive care and across all ranges of blood pressure. Until then, the clinical relevance of PPG-based predictions in daily life should remain an open question.

Federated Learning in a Medical Context: A Systematic Literature Review. Pfitzner, Bjarne; Steckhan, Nico; Arnrich, Bert in ACM Transactions on Internet Technology (TOIT) Special Issue on Security and Privacy of Medical Data for Smart Healthcare (2020).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. On the other hand, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.
@article{pfitzner2020federated, abstract = {Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. On the other hand, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.}, author = {Pfitzner, Bjarne and Steckhan, Nico and Arnrich, Bert}, journal = {ACM Transactions on Internet Technology (TOIT) Special Issue on Security and Privacy of Medical Data for Smart Healthcare}, keywords = {bertarnrich bjarnepfitzner nicosteckhan}, title = {Federated Learning in a Medical Context: A Systematic Literature Review}, year = 2020 }
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URNhttp://dx.doi.org/10.1145/3412357
AbstractData privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. On the other hand, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

Self-prediction of seizures in drug resistance epilepsy using digital phenotyping: a concept study. Moontaha, Sidratul; Steckhan, Nico; Kappattanavar, Arpita; Surges, Rainer; Arnrich, Bert (2020). (Vol. 14)
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
 
Drug-resistance is a prevalent condition in children and adult patients with epilepsy. The quality of life of these patients is profoundly affected by the unpredictability of seizure occurrence. Some of these patients are capable of reporting self-prediction of their seizures by observing their affectivity. Some patients report no signs of feeling premonitory symptoms, prodromes, or aura. In this paper, we propose a concept study that will provide objective information to self-predict seizures for both the patient groups. We will develop a model using digital phenotyping which takes both ecological momentary assessment and data from sensor technology into consideration. This method will be able to provide a feedback of their premonitory symptoms so that a pre-emptive therapy can be associated to reduce seizure frequency or eliminate seizure occurrence.
@inproceedings{moontaha2020selfprediction, abstract = {Drug-resistance is a prevalent condition in children and adult patients with epilepsy. The quality of life of these patients is profoundly affected by the unpredictability of seizure occurrence. Some of these patients are capable of reporting self-prediction of their seizures by observing their affectivity. Some patients report no signs of feeling premonitory symptoms, prodromes, or aura. In this paper, we propose a concept study that will provide objective information to self-predict seizures for both the patient groups. We will develop a model using digital phenotyping which takes both ecological momentary assessment and data from sensor technology into consideration. This method will be able to provide a feedback of their premonitory symptoms so that a pre-emptive therapy can be associated to reduce seizure frequency or eliminate seizure occurrence.}, author = {Moontaha, Sidratul and Steckhan, Nico and Kappattanavar, Arpita and Surges, Rainer and Arnrich, Bert}, booktitle = {14th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters}, journal = {Pervasive Health}, keywords = {arpitakappattanavar bertarnrich nicosteckhan sidratulmoontaha}, month = {oct}, title = {Self-prediction of seizures in drug resistance epilepsy using digital phenotyping: a concept study}, volume = 14, year = 2020 }
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AbstractDrug-resistance is a prevalent condition in children and adult patients with epilepsy. The quality of life of these patients is profoundly affected by the unpredictability of seizure occurrence. Some of these patients are capable of reporting self-prediction of their seizures by observing their affectivity. Some patients report no signs of feeling premonitory symptoms, prodromes, or aura. In this paper, we propose a concept study that will provide objective information to self-predict seizures for both the patient groups. We will develop a model using digital phenotyping which takes both ecological momentary assessment and data from sensor technology into consideration. This method will be able to provide a feedback of their premonitory symptoms so that a pre-emptive therapy can be associated to reduce seizure frequency or eliminate seizure occurrence.

2019

Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions. Morassi Sasso, Ariane; Datta, Suparno; Pfitzner, Bjarne; Zhou, Lin; Steckhan, Nico; Boettinger, Erwin; Arnrich, Bert (2019).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
Hypertension is one of the most prevalent chronic diseases worldwide. Early diagnosis of this condition can prevent the incidence of stroke and also, cardiovascular diseases (CVDs) such as myocardial infarction and heart failure. Lifestyle interventions, such as intermittent fasting (IF), aim to lower blood pressure (BP) levels and increase the health of patients with cardiometabolic conditions. However, for monitoring BP, we still rely on a cuff that slows the �ow of blood, which is both uncomfortable and makes continuous monitoring implausible. Recent research has shown that BP can be estimated using comfortable sensors such as the photoplethysmography (PPG) and the electrocardiography (ECG). Features that can be used for the estimation of BP are systolic upstroke time (SUT) and diastolic time (DT) extracted from the PPG signal, and pulse arrival and transit time (PAT/PTT) derived from the combination of ECG and PPG signals. In this paper we present: (1) a study design to collect continuous physiological signals, before and after a 7-days intermittent fasting (IF) intervention from both cardiometabolic and non-hypertensive patients using wearable devices and (2) initial results for predicting continuous blood pressure from the PPG and ECG signals using statistical and machine learning methods.
@inproceedings{MorassiSasso2019, abstract = {Hypertension is one of the most prevalent chronic diseases worldwide. Early diagnosis of this condition can prevent the incidence of stroke and also, cardiovascular diseases (CVDs) such as myocardial infarction and heart failure. Lifestyle interventions, such as intermittent fasting (IF), aim to lower blood pressure (BP) levels and increase the health of patients with cardiometabolic conditions. However, for monitoring BP, we still rely on a cuff that slows the �ow of blood, which is both uncomfortable and makes continuous monitoring implausible. Recent research has shown that BP can be estimated using comfortable sensors such as the photoplethysmography (PPG) and the electrocardiography (ECG). Features that can be used for the estimation of BP are systolic upstroke time (SUT) and diastolic time (DT) extracted from the PPG signal, and pulse arrival and transit time (PAT/PTT) derived from the combination of ECG and PPG signals. In this paper we present: (1) a study design to collect continuous physiological signals, before and after a 7-days intermittent fasting (IF) intervention from both cardiometabolic and non-hypertensive patients using wearable devices and (2) initial results for predicting continuous blood pressure from the PPG and ECG signals using statistical and machine learning methods.}, address = {Trento}, author = {Morassi Sasso, Ariane and Datta, Suparno and Pfitzner, Bjarne and Zhou, Lin and Steckhan, Nico and Boettinger, Erwin and Arnrich, Bert}, booktitle = {Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters}, keywords = {bertarnrich bjarnepfitzner linzhou nicosteckhan}, publisher = {EAI}, title = {Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions}, year = 2019 }
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URNhttp://dx.doi.org/10.4108/eai.20-5-2019.2283509
AbstractHypertension is one of the most prevalent chronic diseases worldwide. Early diagnosis of this condition can prevent the incidence of stroke and also, cardiovascular diseases (CVDs) such as myocardial infarction and heart failure. Lifestyle interventions, such as intermittent fasting (IF), aim to lower blood pressure (BP) levels and increase the health of patients with cardiometabolic conditions. However, for monitoring BP, we still rely on a cuff that slows the �ow of blood, which is both uncomfortable and makes continuous monitoring implausible. Recent research has shown that BP can be estimated using comfortable sensors such as the photoplethysmography (PPG) and the electrocardiography (ECG). Features that can be used for the estimation of BP are systolic upstroke time (SUT) and diastolic time (DT) extracted from the PPG signal, and pulse arrival and transit time (PAT/PTT) derived from the combination of ECG and PPG signals. In this paper we present: (1) a study design to collect continuous physiological signals, before and after a 7-days intermittent fasting (IF) intervention from both cardiometabolic and non-hypertensive patients using wearable devices and (2) initial results for predicting continuous blood pressure from the PPG and ECG signals using statistical and machine learning methods.

Integrative Network Analysis Of Microbiome-Immune Axis In Metabolic Syndrome Patients During A Fasting Intervention. Avery, Ellen G; Balogh, András; Bartolomaeus, Hendrik; Löber, Ulrike; Steckhan, Nico; Markó, Lajos; Wilck, Nicola; Hamad, Ibrahim; Šušnjar, Urša; Mähler, Anja; Hohmann, Christoph; Lesker, Till R; Strowig, Till; Dechend, Ralf; Bzdok, Danilo; Kleinewietfeld, Markus; Andreas, Michalsen; Müller, Dominik N; Forslund, Sofia K in Hypertension (2019). 74(Suppl_1)
[ BibTeX ] [ DOI ] [ Details ]
 
@article{noauthororeditor2019integrative, author = {Avery, Ellen G and Balogh, András and Bartolomaeus, Hendrik and Löber, Ulrike and Steckhan, Nico and Markó, Lajos and Wilck, Nicola and Hamad, Ibrahim and Šušnjar, Urša and Mähler, Anja and Hohmann, Christoph and Lesker, Till R and Strowig, Till and Dechend, Ralf and Bzdok, Danilo and Kleinewietfeld, Markus and Andreas, Michalsen and Müller, Dominik N and Forslund, Sofia K}, journal = {Hypertension}, keywords = {nicosteckhan}, number = {Suppl_1}, title = {Integrative Network Analysis Of Microbiome-Immune Axis In Metabolic Syndrome Patients During A Fasting Intervention}, volume = 74, year = 2019 }
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URNhttp://dx.doi.org/https://doi.org/10.1161/hyp.74.suppl_1.p2072

Digital Transformation of Complementary and Alternative Medicine (Accepted Presubmission). Steckhan, Nico; Arnrich, Bert in Complementary Medicine Research (2019).
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@article{steckhandigital, author = {Steckhan, Nico and Arnrich, Bert}, journal = {Complementary Medicine Research}, keywords = {bertarnrich nicosteckhan}, title = {Digital Transformation of Complementary and Alternative Medicine (Accepted Presubmission)}, year = 2019 }
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