2020
DOI: 10.3390/s20102920
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Data Improvement Model Based on ECG Biometric for User Authentication and Identification

Abstract: The rapid spread of wearable technologies has motivated the collection of a variety of signals, such as pulse rate, electrocardiogram (ECG), electroencephalogram (EEG), and others. As those devices are used to do so many tasks and store a significant amount of personal data, the concern of how our data can be exposed starts to gain attention as the wearable devices can become an attack vector or a security breach. In this context, biometric also has expanded its use to meet new security requirements of authent… Show more

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Cited by 28 publications
(27 citation statements)
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“…In this paper, the designed system authenticates the user using biometric information such as the user's electrocardiogram. The average recognition rate of personal identification using electrocardiogram is 92% to 99%, [30].…”
Section: Figure 8 Ecg Waveformmentioning
confidence: 99%
“…In this paper, the designed system authenticates the user using biometric information such as the user's electrocardiogram. The average recognition rate of personal identification using electrocardiogram is 92% to 99%, [30].…”
Section: Figure 8 Ecg Waveformmentioning
confidence: 99%
“…raditional biometric signal recognition systems mainly use fingerprints, human faces, iris and other physiological characteristics for recognition [1][2][3]. Despitehe advantages of a higher recognition rate, faster recognition and higher measurability, these physiological characteristicslso have some disadvantages, such as being easy to copy and forge [4,5].…”
Section: Introductionmentioning
confidence: 99%
“…Commonly used methods of ECG identification include linear classifier [25] and random forest [26]. These methods may not be robust enough in practice, and the identification effect depends heavily on signal quality.…”
Section: Introductionmentioning
confidence: 99%