Securing the Biometric through ECG using Machine Learning Techniques

In the current era, biometrics is widely used for maintaining the security. To extract the information from the biomedical signals, biomedical signal processing is needed. One of the significant tools used for the diagnostic is electrocardiogram (ECG). The main reason behind this is the certain uniqueness in the ECG signals of the individual.  In this paper, the focus will be on distinguishing the individual on the basis of ECG signals using feature extraction approaches and the machine learning algorithms. Other than preprocessing approach, the discrete cosine transform is applied to perform the extraction. The classification between the signals of the individuals is carried out using the Support Vector Machine and K-Nearest Neighbor machine learning techniques.  The classification accuracy achieved through SVM is 87% and K-NN has achieved a classification accuracy of 96.6% with k=3. The work has shown how machine learning can be used to classify the ECG signal

Clinical assessment of bone quality at implant site using CBCT and hounsfield unit

Objectives: The current research was done to assess the bone quality at implant site using CBCT. Materials and Methods: The present study was conducted on 50 partially edentulous patients of both genders. All subjects had their chests scanned using a Kodac machine set to 120 kVp, 12 mA, and a 17-second exposure time. Using Hounsfield units, bone quality was classified as D1, D2, D3, D4, and D5 (HU). Result: Out of 50 patients, 27 were males and 23 were females. The average HU was 786.1 at the anterior maxilla, 1174.3 at the anterior mandible, 332.1 at the posterior maxilla, and 742.4 at the posterior mandible. The variation was considerable (P-0.01). Conclusion: The anterior mandible, anterior maxilla, posterior mandible, and posterior maxilla were found to have the highest densities. Based on Hounsfield units, CBCT is helpful in determining the bone density at the implant site

Outcome of implant diameter and length on the distribution of stress with immediate loaded implants: A 3D finite element analysis

Objectives: To assess the outcome of implant diameter and length on THE distribution of stress using a three-dimensional (3D) finite elements (FE) analysis, with immediate loading implants. Materials and Methods: This study made use of a 3D FE model of an implant encased in a chunk of bone. The LEADER/ITALIA-Fix type implant was created specifically for immediate loading. To create a solid model of the implant and bone and to carry out the FE analysis, the ANSYS V.12 programme was used. Results: The findings indicated that the neck of dental implants is the area of highest stress for all implant diameters and lengths, with an increase in implant length from 10 mm to 12 mm resulting in a slight raise in stress at the interface of implant-bone, and an increase in diameter from 3.75 mm to 4.25 mm having no appreciable impact on the value of stresses around dental implants. Conclusion: It was concluded that an increase in length has a negative effect on stress, while a diameter increase has no discernible impact on stress values