Human identification with electrocardiogram

Tseng, Kuo-Kun; Fu, Lean; Liu, Linlin; Lee, Dachao; Wang, Chao; Li, Lingao; Meng, Yao

doi:10.1080/17517575.2018.1450526

Cited by 12 publications

(11 citation statements)

References 29 publications

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“…The concept of our proposed algorithm, RBP, for ECG disease is related to the work of Yang [ 31 ] and Kuma [ 32 ], although we expand it to a different field of application. The basic RBP algorithm [ 33 ] in our design can be roughly divided into two necessary steps that will be illustrated as follows.…”

Section: Methodsmentioning

confidence: 99%

Healthcare knowledge of relationship between time series electrocardiogram and cigarette smoking using clinical records

Tseng

Tang

et al. 2020

BMC Med Inform Decis Mak

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Background: In the few studies of clinical experience available, cigarette smoking may be associated with ischemic heart disease and acute coronary events, which can be reflected in the electrocardiogram (ECG). However, there is no formal proof of a significant relationship between cigarette smoking and electrocardiogram results. In this study, we therefore investigate and prove the relationship between electrocardiogram and smoking using unsupervised neural network techniques. Methods: In this research, a combination of two techniques of pattern recognition; feature extraction and clustering neural networks, is specifically investigated during the diagnostic classification of cigarette smoking based on different electrocardiogram feature extraction methods, such as the reduced binary pattern (RBP) and Wavelet features. In this diagnostic system, several neural network models have been obtained from the different training subsets by clustering analysis. Unsupervised neural network of clustering cigarette smoking was then implemented based on the self-organizing map (SOM) with the best performance. Results: Two ECG datasets were investigated and analysed in this prospective study. One is the public PTB diagnostic ECG databset with 290 samples (age 17-87, mean 57.2; 209 men and 81 women; 73 smoking and 133 non-smoking). The other ECG database is from Taichung Veterans General Hospital (TVGH) and includes 480 samples (240 smoking, and 240 non-smoking). The diagnostic accuracy regarding smoking and non-smoking in the PTB dataset reaches 80.58% based on the RBP feature, and 75.63% in the second dataset based on Wavelet feature. Conclusions: The electrocardiogram diagnostic system performs satisfactorily in the cigarette smoking habit analysis task, and demonstrates that cigarette smoking is significantly associated with the electrocardiogram.

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Section: Methodsmentioning

confidence: 99%

Healthcare knowledge of relationship between time series electrocardiogram and cigarette smoking using clinical records

Tseng

Tang

et al. 2020

BMC Med Inform Decis Mak

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“…In a waveform-based feature study (Tseng et al, 2018), a total of 24 features are extracted from five classes: position (P, Q, R, S, T), amplitude (PQ, RQ, TQ, RT, PS, RP, TS, RS, PT, QS), duration (QS, PR, QR, ST, QT), slope (RS, ST and QR), and area (area of the QRS triangle). These features form a feature vector S. Before extracting the features mentioned above, we need to first accurately locate R, P, Q, T, and S points, which is a key issue for waveform feature extraction.…”

Section: Waveform Feature Extractionmentioning

confidence: 99%

“…However, we suspect ECG can provide more information on aging diseases and chronic habits. This article is dedicated to finding the relationships between ECGs and common diseases and habits using k-means clustering (Alsabti et al, 1997;Qin et al, 2018) based on ECG features [reduced binary pattern (RBP) (Tseng et al, 2015), waveform (Tseng et al, 2018), and wavelet (Saechia et al, 2005;Chan et al, 2008;Chiu et al, 2009)], expecting that we can extract accurate feature information from ECG signals for further research of automatic diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Clustering Analysis of Aging Diseases and Chronic Habits With Multivariate Time Series Electrocardiogram and Medical Records

Tseng

Tang

et al. 2020

Front. Aging Neurosci.

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Background: With recent technology, multivariate time-series electrocardiogram (ECG) analysis has played an important role in diagnosing cardiovascular diseases. However, discovering the association of wide range aging disease and chronic habit with ECG analysis still has room to be explored. This article mainly analyzes the possible relationship between common aging diseases or chorionic habits of medical record and ECG, such as diabetes, obesity, and hypertension, or the habit of smoking. Methods: In the research, we first conducted different ECG features, such as those of reduced binary pattern, waveform, and wavelet and then performed a k-means clustering analysis on the correlation between ECGs and the aforementioned diseases and habits, from which it is expected to find a firm association between them and the best characteristics that can be used for future research. Results: In summary, we discovered a weak and strong evidence between ECG and medical records. For strong evidence, most patients with diabetes are always assigned into a specified group no matter the number of classes in the k-means clustering, which means we can find their association between them. For weak evidence, smokers, obesity, and hypertension have less unique ECG feature vector, enabling clustering them into specific groups, so the ECGs might be used to identify smokers, obesity, and hypertension. It is also interesting that we found obesity and hypertension, which are thought to be related to cardiovascular system. However, they are not highly correlated in our clustering analysis, which might indirectly tell us that the impact of obesity and hypertension to our body is various. In addition, the clustering effect of waveform feature is better than the other two methods.

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“…Spacesuits are costly high-tech products which help astronauts adapt to the space environment and accomplish a variety of space activities. Embedding ECG signal acquisition devices into spacesuits allows for the physical condition of astronauts to be checked [44], including health status and individual identification. A spacesuit embedded with ECG signal acquisition devices is shown in Fig.…”

Section: Application and Hardware A Applicationmentioning

confidence: 99%

Robust Multistage ECG Identification for Astronaut Spacesuits With IoT Applications

et al. 2019

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Today, the Internet of Things (IOT) concept is gaining much attention and popularity; The related technologies as spacesuits and embedded ECG acquisition device is already existed. However, there are important issues to be resolved when an application is in a space environment. The ECG signal may be measured by different mobile conditions when embedded in spacesuits, requiring a more robust algorithm to remove exercise and noise issues. Thus, we propose a more complete architecture with a new storing polymorphic average template (SPAT) and a multistage identification algorithm (MIA) to improve the robustness of ECG identification in motion. In addition, we select better combinations of de-noising and feature extractions to create a better and more complete architecture. According to our experimental results, our proposed architecture offers better performance than previous adaptive boosting (AdaBoost) methods; thus, it is also suitable for application in astronaut spacesuits.

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Human identification with electrocardiogram

Cited by 12 publications

References 29 publications

Healthcare knowledge of relationship between time series electrocardiogram and cigarette smoking using clinical records

Healthcare knowledge of relationship between time series electrocardiogram and cigarette smoking using clinical records

Clustering Analysis of Aging Diseases and Chronic Habits With Multivariate Time Series Electrocardiogram and Medical Records

Robust Multistage ECG Identification for Astronaut Spacesuits With IoT Applications

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