Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach
Electrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining rando...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/17/2/410 |
| _version_ | 1798034617148637184 |
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| author | Robin Tan Marek Perkowski |
| author_facet | Robin Tan Marek Perkowski |
| author_sort | Robin Tan |
| collection | DOAJ |
| description | Electrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining random forest and wavelet distance measure through a probabilistic threshold schema, to improve the effectiveness and robustness of a biometric recognition system using ECG data acquired from a biosensor integrated into mobile devices. The proposed algorithm is evaluated using a mixed dataset from 184 subjects under different health conditions. The proposed two-stage classifier achieves a total of 99.52% subject verification accuracy, better than the 98.33% accuracy from random forest alone and 96.31% accuracy from wavelet distance measure algorithm alone. These results demonstrate the superiority of the proposed algorithm for biometric identification, hence supporting its practicality in areas such as cloud data security, cyber-security or remote healthcare systems. |
| first_indexed | 2024-04-11T20:46:42Z |
| format | Article |
| id | doaj.art-d378c43b56894800a17099e99691b8d9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:46:42Z |
| publishDate | 2017-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d378c43b56894800a17099e99691b8d92022-12-22T04:04:00ZengMDPI AGSensors1424-82202017-02-0117241010.3390/s17020410s17020410Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier ApproachRobin Tan0Marek Perkowski1Department of Electrical and Computer Engineering, Portland State University, Portland, OR 97201, USADepartment of Electrical and Computer Engineering, Portland State University, Portland, OR 97201, USAElectrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining random forest and wavelet distance measure through a probabilistic threshold schema, to improve the effectiveness and robustness of a biometric recognition system using ECG data acquired from a biosensor integrated into mobile devices. The proposed algorithm is evaluated using a mixed dataset from 184 subjects under different health conditions. The proposed two-stage classifier achieves a total of 99.52% subject verification accuracy, better than the 98.33% accuracy from random forest alone and 96.31% accuracy from wavelet distance measure algorithm alone. These results demonstrate the superiority of the proposed algorithm for biometric identification, hence supporting its practicality in areas such as cloud data security, cyber-security or remote healthcare systems.http://www.mdpi.com/1424-8220/17/2/410electrocardiogram (ECG)biometric recognitionrandom forestwavelet distance measuredata security |
| spellingShingle | Robin Tan Marek Perkowski Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach Sensors electrocardiogram (ECG) biometric recognition random forest wavelet distance measure data security |
| title | Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach |
| title_full | Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach |
| title_fullStr | Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach |
| title_full_unstemmed | Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach |
| title_short | Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach |
| title_sort | toward improving electrocardiogram ecg biometric verification using mobile sensors a two stage classifier approach |
| topic | electrocardiogram (ECG) biometric recognition random forest wavelet distance measure data security |
| url | http://www.mdpi.com/1424-8220/17/2/410 |
| work_keys_str_mv | AT robintan towardimprovingelectrocardiogramecgbiometricverificationusingmobilesensorsatwostageclassifierapproach AT marekperkowski towardimprovingelectrocardiogramecgbiometricverificationusingmobilesensorsatwostageclassifierapproach |