Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches

Tseng, Kuo-Kun; Lo, Jiao; Chen, Chih‐Cheng; Tu, Shu-Yi; Yang, Cheng-Fu

doi:10.3390/s18124138

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Moreover, breathing rate [4], heart rate [4], blood pressure, oxygen saturation [5], bioimpedance, electromyogram (EMG), and electrocardiogram (ECG) [6][7][8]. are some of the bio-signal traits that are often acquired utilising wearable and smart textile sensors.…”

Section: Introductionmentioning

confidence: 99%

ECG in Real World Scenario: Time Variability in Biometric Using Wearable Smart Textile Shirts

Muhammad Muizz Mohd Nawawi,

Khairul Azami Sidek,

Amelia Wong Azman

2024

ARASET

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Biomedical signals, such as an electrocardiogram (ECG), have been included in wearable platforms for biometric reasons due to the rapid expansion of apps and technology capable of gathering this physiological data. Most studies using ECG biometrics are conducted in clinical settings, which is impractical for wearable ECG-based biometric applications. Therefore, this study aims to determine the reliability of ECG signals obtained from the commercially available Hexoskin Proshirt and HeartIn Fit shirt, which may be worn for biometric verification in real-world scenarios. ECG data from 22 participants were collected over a span period of more than 30 days. The raw ECG signal is first pre-processed in the time domain using noise-removal Butterworth filters, and then a successful QRS segmented feature extraction method is used. Not to mention, 300 datasets were used to test the recommended recognition method using a Quadratic Support Vector Machine (QSVM). In comparison, around 854 datasets were prepared for training and validation of the classifier. The findings showed that the proposed method provided a considerable accuracy above 99.63 % with a FAR of 0.14 %, an FRR of 2.86 %, and a TPR of 97.14 %. Thus, the study supports using ECG biometrics for verification in real-world settings by employing a smart textile shirt with varying temporal variability.

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Section: Introductionmentioning

confidence: 99%

ECG in Real World Scenario: Time Variability in Biometric Using Wearable Smart Textile Shirts

Muhammad Muizz Mohd Nawawi,

Khairul Azami Sidek,

Amelia Wong Azman

2024

ARASET

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“…Nowadays, the advances of wearable textile technology growth have reached the stage of a smart capability that can acquire the biosignal of its wearer seamlessly [1][2][3]. Some of the bio-signal traits that typically acquire using wearable and smart textile sensors are breathing rate, heart rate, blood pressure, oxygen saturation, bioimpedance, electromyogram (EMG), and electrocardiogram (ECG) [4][5][6][7][8]. In addition, wearable devices that do not require any invasive procedures to record human ECG over an extended period and do not interfere with the user's routine have garnered interest and sparked a consumer market demand for a shirt-like wearable ECG device with embedded measuring electrodes and lead [6,9].…”

Section: Introductionmentioning

confidence: 99%

Biometric Analysis on Smart Textile Garment in Real Life Scenario

Muhammad Muizz Mohd Nawawi,

Khairul Azami Sidek,

Amelia Wong Azman

2023

ARASET

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The rapid proliferation of wearable applications and technologies capable of acquiring biomedical signals has prompted the incorporation of biomedical signals, such as the electrocardiogram (ECG), for biometric purposes in wearable platforms. Most ECG biometric research utilises medical-grade sensors in clinical settings, which is unrealistic for wearable ECG-based biometric applications in the real world. Therefore, this research aims to examine the ECG biometric on smart textile garments in real life, collected from commercially available wearable Hexoskin Proshirt and HeartIn Fit shirts. ECG data were obtained from 22 participants who took part in this study. The raw ECG signal is initially pre-processed using noise-removal Butterworth filters in the time domain, followed by an effective QRS segmented feature extraction technique. Finally, around 2076 datasets were created for training and validation, while the remaining 501 datasets were employed to test the suggested recognition approach with 29 Machine Learning Classifiers. Subsequently, Quadratic SVM has the highest accuracy at 96.8% for ECG biometrics, followed by Narrow Neural Network with 95.8% and Wide Neural Network with 95.4%. Further improvement to the QSVM parameter improved the accuracy to 97.4% with an error rate of 2.6%, followed by a sensitivity of 97.4% with a precision of 97.7% and a false rejection rate of 2.6%. Thus, the results of this study further validate the feasibility of applying ECG biometrics for recognition in real-life scenarios utilising a smart textile shirt with different configurations and brand is possible.

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Review on Data Acquisition of Electrocardiogram Biometric Recognition in Wearable Smart Textile Shirts

Nawawi

Sidek

Dafhalla

et al. 2021

J. Phys.: Conf. Ser.

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Electrocardiogram (ECG) wearable smart textile shirt has widely been investigated due to its high flexibility, reusability, comfort, and the possibility of being used for home-based, real-life activities and real-time measurement. ECG smart textile shirt is an embedded textile sensor inside a cloth that can capture ECG data in more convenient ways and ease user-friendly, especially for continuous and long-term ECG data acquisition outside the laboratory environment. However, the current challenge with ECG smart textile shirt is the reliability and quality of data acquired by the wearable smart textile. This review will mainly focus on the research strategies in the early stages regarding data acquisitions in ECG smart textile shirt. It will introduce researchers’ data acquisition methods in the biometric recognition system using wearable ECG smart textile. The Scopus and Mendeley databases review may help future researchers consider different parameters, which affect the reliability and data quality when selecting data acquisitions strategies in a biometric recognition system using wearable ECG smart textile shirt.

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Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches

Cited by 3 publications

References 32 publications

ECG in Real World Scenario: Time Variability in Biometric Using Wearable Smart Textile Shirts

ECG in Real World Scenario: Time Variability in Biometric Using Wearable Smart Textile Shirts

Biometric Analysis on Smart Textile Garment in Real Life Scenario

Review on Data Acquisition of Electrocardiogram Biometric Recognition in Wearable Smart Textile Shirts

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