| Title |
Normalizing Electrocardiograms of Both Healthy Persons and Cardiovascular Disease Patients for Biometric Authentication
|
|---|---|
| Published in |
PLOS ONE, August 2013
|
| DOI | 10.1371/journal.pone.0071523 |
| Pubmed ID | |
| Authors |
Meixue Yang, Bin Liu, Miaomiao Zhao, Fan Li, Guoqing Wang, Fengfeng Zhou |
| Abstract |
Although electrocardiogram (ECG) fluctuates over time and physical activity, some of its intrinsic measurements serve well as biometric features. Considering its constant availability and difficulty in being faked, the ECG signal is becoming a promising factor for biometric authentication. The majority of the currently available algorithms only work well on healthy participants. A novel normalization and interpolation algorithm is proposed to convert an ECG signal into multiple template cycles, which are comparable between any two ECGs, no matter the sampling rates or health status. The overall accuracies reach 100% and 90.11% for healthy participants and cardiovascular disease (CVD) patients, respectively. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 28% |
| Student > Bachelor | 4 | 16% |
| Researcher | 3 | 12% |
| Student > Doctoral Student | 2 | 8% |
| Student > Master | 2 | 8% |
| Other | 3 | 12% |
| Unknown | 4 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 8 | 32% |
| Computer Science | 5 | 20% |
| Medicine and Dentistry | 2 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 5 | 20% |