The open D1NAMO dataset: A multi-modal dataset for research on non-invasive type 1 diabetes management

Abstract The usage of wearable devices has gained popularity in the latest years, especially for health-care and well being. Recently there has been an increasing interest in using these devices to improve the management of chronic diseases such as diabetes. The quality of data acquired through wearable sensors is generally lower than what medical-grade devices provide, and existing datasets have mainly been acquired in highly controlled clinical conditions. In the context of the D1NAMO project — aiming to detect glycemic events through non-invasive ECG pattern analysis — we elaborated a dataset that can be used to help developing health-care systems based on wearable devices in non-clinical conditions. This paper describes this dataset, which was acquired on 20 healthy subjects and 9 patients with type-1 diabetes. The acquisition has been made in real-life conditions with the Zephyr BioHarness 3 wearable device. The dataset consists of ECG, breathing, and accelerometer signals, as well as glucose measurements and annotated food pictures. We open this dataset to the scientific community in order to allow the development and evaluation of diabetes management algorithms.

[1]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[2]  T. Laitinen,et al.  Electrocardiographic Alterations during Hyperinsulinemic Hypoglycemia in Healthy Subjects , 2008, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[3]  G Reach,et al.  Which threshold to detect hypoglycemia? Value of receiver-operator curve analysis to find a compromise between sensitivity and specificity. , 2001, Diabetes care.

[4]  Keiji Yanai,et al.  Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation , 2014, ECCV Workshops.

[5]  N. Harris,et al.  Influence of autonomic neuropathy on QTc interval lengthening during hypoglycemia in type 1 diabetes. , 2004, Diabetes.

[6]  Héctor Pomares,et al.  mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications , 2014, IWAAL.

[7]  R. Saatchi,et al.  A knowledge-based electrocardiogram-monitoring system for detection of the onset of nocturnal hypoglycaemia in type 1 diabetic patients , 2006, 2006 Computers in Cardiology.

[8]  N. Busch,et al.  Food-pics: an image database for experimental research on eating and appetite , 2014, Front. Psychol..

[9]  Paolo Sernani,et al.  Exploring the ambient assisted living domain: a systematic review , 2017, J. Ambient Intell. Humaniz. Comput..

[10]  R O Potts,et al.  Detection of hypoglycemia with the GlucoWatch biographer. , 2001, Diabetes care.

[11]  Lena Mamykina,et al.  No longer wearing: investigating the abandonment of personal health-tracking technologies on craigslist , 2015, UbiComp.

[12]  Aldo Franco Dragoni,et al.  Event Calculus Agent Minds Applied to Diabetes Monitoring , 2017, AAMAS Workshops.

[13]  L Heinemann,et al.  Electrocardiographic changes during insulin-induced hypoglycemia in healthy subjects. , 1998, Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme.

[14]  Miguel Altuve,et al.  A new on-line electrocardiographic records database and computer routines for data analysis , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[16]  Nuryani Nuryani,et al.  Electrocardiographic Signals and Swarm-Based Support Vector Machine for Hypoglycemia Detection , 2011, Annals of Biomedical Engineering.

[17]  S MacRury,et al.  The use of technology to promote physical activity in Type 2 diabetes management: a systematic review , 2013, Diabetic medicine : a journal of the British Diabetic Association.

[18]  U. Ekelund,et al.  International children's accelerometry database (ICAD): Design and methods , 2011, BMC public health.

[19]  T. H. Kyaw,et al.  Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database* , 2011, Critical care medicine.

[20]  Jefferson Luiz Brum Marques,et al.  Altered ventricular repolarization during hypoglycaemia in patients with diabetes , 1997, Diabetic medicine : a journal of the British Diabetic Association.

[21]  Edward A. Witt,et al.  Physical Activity and Health-Related Quality of Life Among Adults with Type 2 Diabetes: Results from Wearable Fitness Trackers , 2016 .

[22]  Zhigang Zhu,et al.  Current and Emerging Technology for Continuous Glucose Monitoring , 2017, Sensors.

[23]  J. Randløv,et al.  QT interval prolongation during spontaneous episodes of hypoglycaemia in type 1 diabetes: the impact of heart rate correction , 2010, Diabetologia.

[24]  Aldin Malkoc,et al.  Enhancing Glycemic Control via Detection of Insulin Using Electrochemical Impedance Spectroscopy , 2017, Journal of diabetes science and technology.

[25]  R. Saatchi,et al.  Classification of SAECG by autoregressive modelling and neural networks , 2004, 2004 IEEE Africon. 7th Africon Conference in Africa (IEEE Cat. No.04CH37590).

[26]  Anders Pors,et al.  Critical-depth Raman spectroscopy enables home-use non-invasive glucose monitoring , 2018, PloS one.

[27]  R. Guy,et al.  Non-invasive, transdermal, path-selective and specific glucose monitoring via a graphene-based platform , 2018, Nature Nanotechnology.

[28]  Matthieu Guillaumin,et al.  Food-101 - Mining Discriminative Components with Random Forests , 2014, ECCV.

[29]  B. Eckert,et al.  Hypoglycaemia leads to an increased QT interval in normal men. , 1998, Clinical physiology.

[30]  Keiji Yanai,et al.  Recognition of Multiple-Food Images by Detecting Candidate Regions , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[31]  Davide Anguita,et al.  A Public Domain Dataset for Human Activity Recognition using Smartphones , 2013, ESANN.

[32]  Karl Aberer,et al.  Detection of Hypoglycemic Events through Wearable Sensors , 2016, SEMPER@ESWC.