A Review on Machine Learning Techniques for Neurological Disorders Estimation by Analyzing EEG Waves

With the fast improvement of neuroimaging data acquisition strategies, there has been a significant growth in learning neurological disorders among data mining and machine learning communities. Neurological disorders are the ones that impact the central nervous system (including the human brain) and also include over 600 disorders ranging from brain aneurysm to epilepsy. Every year, based on World Health Organization (WHO), neurological disorders affect much more than one billion people worldwide and count for up to seven million deaths. Hence, useful investigation of neurological disorders is actually of great value. The vast majority of datasets useful for diagnosis of neurological disorders like electroencephalogram (EEG) are actually complicated and poses challenges that are many for data mining and machine learning algorithms due to their increased dimensionality, non stationarity, and non linearity. Hence, an better feature representation is actually key to an effective suite of data mining and machine learning algorithms in the examination of neurological disorders. With this exploration, we use a well defined EEG dataset to train as well as test out models. A preprocessing stage is actually used to extend, arrange and manipulate the framework of free data sets to the needs of ours for better training and tests results. Several techniques are used by us to enhance system accuracy. This particular paper concentrates on dealing with above pointed out difficulties and appropriately analyzes different EEG signals that would in turn help us to boost the procedure of feature extraction and enhance the accuracy in classification. Along with acknowledging above issues, this particular paper proposes a framework that would be useful in determining man stress level and also as a result, differentiate a stressed or normal person/subject

Evaluate The Quality Of Marked Decrypted Image Quantitatively To Encrypted Images Using Rdh

Encryption is an effectual and popular means as it converts the original and significant content to incomprehensible one. Even though few RDH methods in encrypted images have been published yet there are some talented applications if RDH can be applied to encrypted images. Hwang et al. supported a reputation-based trust-management system enhanced with data colouring a way of embedding data into covers and software watermarking in which data encryption and colouring offer potential for upholding the content owner’s privacy and data integrity. Apparently the cloud service provider has no right to commence everlasting distortion during data colouring into encrypted data. Therefore a reversible data colouring technique based on encrypted data is preferred. Suppose a medical image database is stored in a data centre and a server in the data centre can implant notations into an encrypted version of a medical image through a RDH technique. With the notations the server can handle the image or confirm its integrity without having the knowledge of the original content and thus the patient’s privacy is protected. On the other hand a doctor having the cryptographic key can decrypt and reinstate the image in a reversible manner for the reason of additional diagnosing

Coronary plaque and clinical characteristics of South Asian (Indian) patients with acute coronary syndromes : an optical coherence tomography study

Background: South Asians, and Indians in particular, are known to have a higher incidence of premature atherosclerosis and acute coronary syndromes (ACS) with worse clinical outcomes, compared to populations with different ethnic backgrounds. However, the underlying pathobiology accounting for these differences has not been fully elucidated. Methods: ACS patients who had culprit lesion optical coherence tomography (OCT) imaging were enrolled. Culprit plaque characteristics were evaluated using OCT. Results: Among 1315 patients, 100 were South Asian, 1009 were East Asian, and 206 were White. South Asian patients were younger (South Asians vs. East Asians vs. Whites: 51.6 ± 13.4 vs. 65.4 ± 11.9 vs. 62.7 ± 11.7; p < 0.001) and more frequently presented with ST-segment elevation myocardial infarction (STEMI) (77.0% vs. 56.4% vs. 35.4%; p < 0.001). On OCT analysis after propensity group matching, plaque erosion was more frequent (57.0% vs. 38.0% vs. 50.0%; p = 0.003), the lipid index was significantly greater (2281.6 [1570.8–3160.6] vs. 1624.3 [940.9–2352.4] vs. 1303.8 [1090.0–1757.7]; p < 0.001), and the prevalence of layered plaque was significantly higher in the South Asian group than in the other two groups (52.0% vs. 30.0% vs. 34.0%; p = 0.003). Conclusions: Compared to East Asians and Whites, South Asians with ACS were younger and more frequently presented with STEMI. Plaque erosion was the predominant pathology for ACS in South Asians and their culprit lesions had more features of plaque vulnerability. Clinical Trial Registration: http://www.clinicaltrials.gov, NCT03479723</p