2020

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  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, Moskovitch, R. (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.
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  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 ] [ 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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  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.
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  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

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  Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions.Morassi Sasso, Ariane; Datta, Suparno; Pfitzner, Bjarne; Zhou, Lin; Steckhan, Nico; Boettinger, Erwin; Arnrich, Bert (2019).
  [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  @inproceedings{MorassiSasso2019, 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
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  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
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  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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