Bio-medical analysis framework

Monitoring of ECG signal is used in medicine for multiple purposes. Measurements can be taken at any stage of patient's medical care, either as a preventative, diagnostical or recovery monitoring. Current wearable technology enables users and doctors to produce so far unprecedented amount of information. Processing of such measurements is usually a laborious and time consuming manual task. Automatic processing of such measurements is neither well defined nor thoroughly tested. In this work we focused on the needs of both, health care professionals, and IT engineers developing software for processing of long term multi-sensor measurements. Taking into account future expandability of multi-sensor gadgets, we propose a new framework, which is able to show the data measured by wearable ECG monitor, process it, and compare algorithms for automatic processing. We determined possible signal sources along with their values, units and time continuity. We propose suitable file formats for storage of such measurements, keeping in mind future expandability, size demands and usage of the formats. Suggested framework can therefore be used to display, automatically process and store discrete and continuous biomedical signals beside ECG, producing additional value to gathered measurements.

[1]  Jure Slak,et al.  Detection of heart rate variability from a wearable differential ECG device , 2016, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[2]  G.B. Moody,et al.  PhysioNet: a Web-based resource for the study of physiologic signals , 2001, IEEE Engineering in Medicine and Biology Magazine.

[3]  Wilfred Ng,et al.  Comparative Analysis of XML Compression Technologies , 2006, World Wide Web.

[4]  Michael M Laks,et al.  New devices for very long-term ECG monitoring. , 2012, Cardiology journal.

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

[6]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[7]  E. Topol,et al.  Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring. , 2014, The American journal of medicine.

[8]  N J HOLTER.  New Method for Heart Studies , 1961, Science.

[9]  Miha Mohorcic,et al.  Heart rate analysis with NevroEkg , 2016, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[10]  Chris D. Nugent,et al.  A review of ECG storage formats , 2011, Int. J. Medical Informatics.

[11]  Emil Jovanov,et al.  Real Time Holter Monitoring of Biomedical Signals , 1999 .

[12]  Theodor Landis,et al.  Usefulness of Ambulatory 7-Day ECG Monitoring for the Detection of Atrial Fibrillation and Flutter After Acute Stroke and Transient Ischemic Attack , 2004, Stroke.

[13]  Matjaz Depolli,et al.  Robust beat detection on noisy differential ECG , 2016, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).