Design and evaluation of a wireless decision-support system for heart rate variability study in haemodialysis follow-up procedures

In this paper a new wireless decision-support system for haemodialysis patients using heart rate variability (HRV) is presented. The telemedicine system provides connectivity to three participant sites: the general practitioner or nurse at the point of care in the dialysis unit, the remote information and processing server and the cardiologist. At the clinical point of care, the nurse acquires the electrocardiogram (ECG) by using a tailored mobile telecardiology system as well as other relevant physiological information during the clinical procedure, and sends it to the information server. The received information is stored in a secure file server, linked to the patient database and the ECG signal is automatically analyzed by using advanced signal processing tools in the processing server, where a complete clinical results report is generated. The cardiologist can then be linked by means of a web browser to the information server to analyze these results for further clinical diagnosis support. The system has been applied to study HRV in patients undergoing haemodialysis. The clinical report consisted of trends for time- and frequency-domain HRV indexes and other supplementary information automatically calculated, which show the response of the electrical activity of the heart to the dialysis process and that can be helpful for the follow-up of these patients. The telecardiology framework has been successfully evaluated both by the patients and the hospital personnel showing a high compliance with the system. The design and implementation of the telecardiology system have followed the most recent advances in web technologies, biomedical information and storage standards and signal processing techniques. The presented system can be used as a telemedicine tool for clinical diagnosis support and could also be used in other clinical settings.

[1]  IstepanianR. S.H.,et al.  Guest Editorial Introduction to the Special Section on M-Health , 2004 .

[2]  P. Laguna,et al.  Coronary artery disease diagnosis based on exercise electrocardiogram indexes from repolarisation, depolarisation and heart rate variability , 2003, Medical and Biological Engineering and Computing.

[3]  Mika P. Tarvainen,et al.  Software for advanced HRV analysis , 2004, Comput. Methods Programs Biomed..

[4]  Vaidotas Marozas,et al.  Web-based health services and clinical decision support. , 2004, Studies in health technology and informatics.

[5]  R. Istepanian,et al.  M-Health: Emerging Mobile Health Systems , 2006 .

[6]  Emil Jovanov,et al.  Guest Editorial Introduction to the Special Section on M-Health: Beyond Seamless Mobility and Global Wireless Health-Care Connectivity , 2004, IEEE Transactions on Information Technology in Biomedicine.

[7]  R. Istepanian,et al.  Mobile e-health: the unwired evolution of telemedicine. , 2003, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[8]  P Lander,et al.  Temporal evolution of traditional versus transformed ECG-based indexes in patients with induced myocardial ischemia. , 2000, Journal of electrocardiology.

[9]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[10]  José García,et al.  Remote processing server for ECG-based clinical diagnosis support , 2002, IEEE Transactions on Information Technology in Biomedicine.

[11]  T. Iwasaka,et al.  Determinants of heart rate variability in chronic hemodialysis patients. , 1998, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[12]  Álvaro Alesanco Iglesias,et al.  Enhanced real-time ECG coder for packetized telecardiology applications , 2006, IEEE Transactions on Information Technology in Biomedicine.

[13]  Pablo Laguna,et al.  Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal , 2003, IEEE Transactions on Biomedical Engineering.

[14]  N. Ohte,et al.  Prognostic value of heart rate variability in patients with end-stage renal disease on chronic haemodialysis. , 2003, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[16]  Pablo Laguna,et al.  Bioelectrical Signal Processing in Cardiac and Neurological Applications , 2005 .

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

[18]  K. Arheart,et al.  Heart rate variability and mortality in patients with end stage renal disease. , 2005, Nephrology nursing journal : journal of the American Nephrology Nurses' Association.