scholarly journals Utilizing ECG Waveform Features as New Biometric Authentication Method

2018 ◽  
Vol 8 (2) ◽  
pp. 658 ◽  
Author(s):  
Ahmed Younes Shdefat ◽  
Moon-Il Joo ◽  
Sung-Hoon Choi ◽  
Hee-Cheol Kim
Keyword(s):  
Template Matching ◽  
The Body ◽  
Classification Task ◽  
Ecg Signal ◽  
Recording Device ◽  
Svm Algorithm ◽  
Linear Svm ◽  
Electrocardiogram Ecg ◽  
Matching Techniques ◽  
Electrical Activities

<p>In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.</p>

Introduction to Heart

2014 ◽  
pp. 96-112
Keyword(s):  
The Body ◽  
Ecg Signal ◽  
Electrocardiogram Signals ◽  
Signal Characteristics ◽  
Improve Measurement ◽  
Computer Based ◽  
The Common ◽  
Diagnosis Tool ◽  
Heart Disorders ◽  
Electrocardiogram Ecg

The heart is an important organ in the human body, for pumping the blood throughout the body. An electrocardiogram (ECG) is a diagnosis tool that reports the electrical operation of the heart, recorded by skin electrodes at specific locations on the body. The introduction of computer-based methods for the purpose of quantifying different ECG signal characteristics is the result of a desire to improve measurement accuracy as well as reproducibility. In this chapter, the author explains the basic definitions in heart studies and the electrocardiogram signals. In addition, the importance of interpretation and measuring the effective features in heart signals to detect the heart disorders is described. Finally, some of the common disorders of heart are briefly explained.

scholarly journals Detection and Classification of ECG Signal through Machine learning

2019 ◽  
Vol 8 (10) ◽  
pp. 3221-3227
Keyword(s):  
The Body ◽  
Wave Form ◽  
Ecg Signal ◽  
Software Application ◽  
Ecg Signals ◽  
Temporal Features ◽  
Associate Anomaly ◽  
The Individual ◽  
Electrocardiogram Ecg

The electrical activity which might be acquired by inserting the probes on the body exterior that is originated within the individual muscle cells of the heart and is summed to indicate an indication wave form referred to as the EKG (ECG). Cardiac Arrhythmia is an associate anomaly within the heart which may be diagnosed with the usage of signals generated by Electrocardiogram (ECG). For the classification of ECG signals a software application model was developed and has been investigated with the usage of the MIT-BIH database. The version is based on some existing algorithms from literature, entails the extraction of a few temporal features of an ECG signal and simulating it with a trained FFNN. The software version may be employed for the detection of coronary heart illnesses in patients. The neural network’s structure and weights are optimized using Particle Swarm Optimization (PSO). The FFNN trained with set of rules by PSO increase its accuracy. The overall accuracy and sensitivity of the algorithm is about 93.687 % and 92%.

WBAN Based Long Term ECG Monitoring

2013 ◽  
Vol 1 (3) ◽  
pp. 20-37 ◽  
Author(s):  
Marius Rosu ◽  
Sever Pasca
Keyword(s):  
Low Power ◽  
Sensor Nodes ◽  
Ecg Signal ◽  
Area Network ◽  
Wireless Sensor Nodes ◽  
Medication Process ◽  
Remote Healthcare ◽  
Electrocardiogram Ecg ◽  
Supply Information

Healthcare solutions using anytime, and anywhere remote healthcare surveillance devices, have become a major challenge. The patients with chronic diseases who need only therapeutic supervision are not advised to occupy a hospital bed. Using Wearable Wireless Body/Personal Area Network (WWBAN), intelligent monitoring of heart can supply information about medical conditions. Electrocardiogram (ECG) is the core reference in the diagnosis and medication process. An approach on healthcare solution WBAN based, for real-time ECG signal monitoring and long-term recording will be presented. Low-power wireless sensor nodes with local processing and encoding capabilities in order to achieve maximum mobility and flexibility are our main goal. ZigBee wireless technology will be used for transmission. Sensor device will be programmed to process locally the ECG signal and to raise an alert. Low-power and miniaturization are essential physical requirements.

scholarly journals Two Electrode ECG And EOG System For Monitoring Applications

2019 ◽  
Vol 8 (11) ◽  
pp. 262-265
Keyword(s):  
Reference Electrode ◽  
Cost Effective ◽  
Electrode System ◽  
Ecg Signal ◽  
Biomedical Signal ◽  
Ecg Signals ◽  
Arm Position ◽  
The Stability ◽  
Electrocardiogram Ecg ◽  
Total Gain

Heart and Eye are two vital organs in the human system. By knowing the Electrocardiogram (ECG) and Electro-oculogram (EOG), one will be able to tell the stability of the heart and eye respectively. In this project, we have developed a circuit to pick the ECG and EOG signal using two wet electrodes. Here no reference electrode is used. EOG and ECG signals have been acquired from ten healthy subjects. The ECG signal is obtained from two positions, namely wrist and arm position respectively. The picked-up biomedical signal is recorded and heart rate information is extracted from ECG signal using the biomedical workbench. The result found to be promising and acquired stable EOG and ECG signal from the subjects. The total gain required for the arm position is higher than the wrist position for the ECG signal. The total gain necessary for the EOG signal is higher than the ECG signal since the ECG signal is in the range of millivolts whereas EOG signal in the range of microvolts. This two-electrode system is stable, cost-effective and portable while still maintaining high common-mode rejection ratio (CMRR).

scholarly journals SVM ALGORITHM FOR PREDICTING RICE YIELDS

2020 ◽  
Vol 13 (2) ◽  
pp. 50-54
Author(s):  
Nur Nafi'iyah
Keyword(s):  
Agricultural Land ◽  
Rice Yield ◽  
Weather Conditions ◽  
Original Data ◽  
Research Process ◽  
Svm Algorithm ◽  
Food Shortages ◽  
Rice Yields ◽  
The Government ◽  
Linear Svm

Agriculture in Indonesia is highly dependent on reservoir irrigation water sources and rain. Because some agricultural land in Indonesia is rainfed. Plants in Indonesia rely on water from rain and irrigation. Weather conditions greatly affect the number of farmers' harvest. Farmers often experience crop failures due to changing weather. From data from the Central Statistics Agency, it is stated that the number of rice yields in 2019 decreased by 7.76% compared to 2018. In order to avoid rice imports and rice food shortages, a breakthrough is needed that can help the government in making policies. One of the breakthroughs is creating a rice yield prediction system. The research process consisted of collecting data via the web: https://www.pertanian.go.id/home/?show=page&act=view&id=61. The data shows the variables of province, year, land area, production. The total number of data is 170 rows, with a division of 130 lines for training, and 40 for testing. Furthermore, the data is processed and processed and normalized. The results of data processing are then trained and predicted with a linear SVM kernel. The results of SVM prediction with original data without normalization of MAPE 6635.53%, and RMSE 1094810.74. The results of SVM prediction with normalized data first, the MAPE value was 9427.714%, and RMSE 0.017.

scholarly journals Very early identification of a bimodal frictional behavior during the post-seismic phase of the 2015 M<sub><i>w</i></sub>8.3 Illapel, Chile, earthquake

2021 ◽  
Author(s):  
Cedric Twardzik ◽  
Mathilde Vergnolle ◽  
Anthony Sladen ◽  
Louisa L. H. Tsang
Keyword(s):  
Template Matching ◽  
State Of The Art ◽  
Data Driven ◽  
Spatiotemporal Evolution ◽  
Seismic Slip ◽  
The North ◽  
Chile Earthquake ◽  
Gnss Data ◽  
Matching Techniques

Abstract. It is well-established that the post-seismic slip results from the combined contribution of seismic slip and aseismic slip. However, the partitioning between these two modes of slip remains unclear due to the difficulty to infer detailed and robust descriptions of how both evolve in space and time. This is particularly true just after a mainshock when both processes are expected to be the strongest. Using state-of-the-art sub-daily processing of GNSS data, along with dense catalogs of aftershocks obtained from template-matching techniques, we unravel the spatiotemporal evolution of post-seismic slip and aftershocks over the first 12 hours following the 2015 Mw8.3 Illapel, Chile, earthquake. We show that the very early post-seismic activity occurs over two regions with distinct behaviors. To the north, post-seismic slip appears to be purely aseismic and precedes the occurrence of late aftershocks. To the south, aftershocks are the primary cause of the post-seismic slip. We suggest that this difference in behavior could be inferred only few hours after the mainshock, and thus could contribute to a more data-driven forecasts of long-term aftershocks.

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