Name
Abstract | ||
|---|---|---|
As technology evolves, its consumers gain considerable advantages to bring prosperity for all humankind. It is so in medical environment. Even though there always has been standards in hospital or other related medical sites, it is possible for people with the help of technology to study about simple theory of pathology and find another mechanism to meet those standards, i.e., to create new method for healing illnesses. For consumer use, creating the new method can be of a significant benefit for the case of maximizing usability and minimizing cost, and inventing Electrocardiography method is a good example. It is considered difficult for laymen if they want to assess their own heart condition following the rule defined, as so the cost they should afford. Motivated by this problem, this paper investigates detrended fluctuation analysis (DFA) to measure heart electric signal. DFA scales the autocorrelation of nonstationary signal, which can be found in human heartbeat in the form of Electrocardiogram wave. DFA was implemented in a developed Electrocardiogram (ECG) device, created with a low-cost Raspberry pi 2 as the core and some other sensors. As for home environment, smoking habit was considered as the performance metric. The experimental result reveals a cardiac disparity between smoker and nonsmoker, showing a new way of determining smoking habit effect using DFA. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CCNC.2017.7983093 | 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC) |
Keywords | Field | DocType |
detrended fluctuation analysis,ECG device,home environment,medical environment,hospital,pathology,electrocardiography method,heart condition,DFA,heart electric signal,nonstationary signal autocorrelation,human heartbeat,electrocardiogram wave,Raspberry pi 2,performance metric,cardiac disparity,smoking habit effect | Time series,Heartbeat,Algorithm design,Computer science,Raspberry pi,Performance metric,Usability,Detrended fluctuation analysis,Artificial intelligence,Machine learning,Autocorrelation,Distributed computing | Conference |
ISSN | ISBN | Citations |
2331-9852 | 978-1-5090-6197-6 | 1 |
PageRank | References | Authors |
0.36 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alif Akbar Pranata | 1 | 2 | 0.73 |
| Gereziher W. Adhane | 2 | 1 | 0.69 |
| Dong Seong Kim | 3 | 866 | 93.34 |