A Heart Rate Variability Analysis System Based on LeanCloud and Echarts

The electrocardiogram (ECG) has become a standard tool to measure and record the electrical activities of the heart because of its low-cost and non-invasive characteristic. Applications running on smart phones or tablets have been widely used for collecting and processing ECG signals, with the prevalence and massive availability of health related mobile applications. However, these applications have the disadvantage of relative small mass storage and relative poor computing ability, especially in Human-Computer interaction user experience. To achieve rapid Heart Rate Variability (HRV) analysis based on big physiological data, a novel HRV analysis system has been developed based on LeanCloud cloud platform and web application. The cloud platform can store and process mass raw ECG signals. Computing intensive signal processing tasks were implemented in Python code. The HRV analysis results displayed on the web achieve good interactivity by using Echarts. The preliminary results demonstrate that this system have strong computing ability and good Human-Computer interaction user experience.

[1]  A. Porta,et al.  Power spectrum analysis of heart rate variability to assess the changes in sympathovagal balance during graded orthostatic tilt. , 1994, Circulation.

[2]  G. Billman Heart Rate Variability – A Historical Perspective , 2011, Front. Physio..

[3]  Qiao Li,et al.  Open source Java-based ECG analysis software and Android app for Atrial Fibrillation screening , 2013, Computing in Cardiology 2013.

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

[5]  Steve Wheeler,et al.  How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX , 2011, Biomedical engineering online.

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

[7]  Cheng Shi,et al.  Apnea MedAssist II: A smart phone based system for sleep apnea assessment , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

[8]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[9]  Jorge A. Gálvez,et al.  Perioperative Smartphone Apps and Devices for Patient-Centered Care , 2015, Journal of Medical Systems.

[10]  Sheikh Iqbal Ahamed,et al.  PriGen: A Generic Framework to Preserve Privacy of Healthcare Data in the Cloud , 2013, ICOST.

[11]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[13]  P. Welch The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[14]  A. Doruk,et al.  Autonomic Nervous System Imbalance in Young Adults with Developmental Stuttering , 2008 .

[15]  C. Pizzi,et al.  Heart rate variability today. , 2012, Progress in cardiovascular diseases.