An Effective Deep Learning Model for Health Monitoring and Detection of COVID-19 Infected Patients: An End-to-End Solution.

The COVID-19 infection is the greatest danger to humankind right now because of the devastation it causes to the lives of its victims. It is important that infected people be tested in a timely manner in order to halt the spread of the disease. Physical approaches are time-consuming, expensive, and tedious. As a result, there is a pressing need for a cost-effective and efficient automated tool. A convolutional neural network is presented in this paper for analysing X-ray pictures of patients' chests. For the analysis of COVID-19 infections, this study investigates the most suitable pretrained deep learning models, which can be integrated with mobile or online apps and support the mobility of diagnostic instruments in the form of a portable tool. Patients can use the smartphone app to find the nearest healthcare testing facility, book an appointment, and get instantaneous results, while healthcare professionals can keep track of the details thanks to the web and mobile applications built for this study. Medical practitioners can apply the COVID-19 detection model for chest frontal X-ray pictures with ease. A user-friendly interface is created to make our end-to-end solution paradigm work. Based on the data, it appears that the model could be useful in the real world.

Acquisition and analysis of brainstem auditory evoked responses of normal and diseased subjects by spectral estimation.

Human brainstem auditory evoked responses (BAERs) are sensory evoked potentials that can be recorded within a few milliseconds following a transient acoustic stimulus (click signal). This paper suggests a novel technique to clearly demarcate normals and patients with complaints of vertigo and deafness by computing hitherto unused power spectral parameters from the BAER signals recorded on them. The BAER spectrum of normal subjects contains three main frequency components, i.e. low-, mid- and high-frequency components around 100, 500 and 1000 Hz, respectively, which is not so in the case of diseased subjects. The spectral parameters, i.e. the mean power frequency, median frequency, the ratios of the integrated power at dominant frequencies to that of the total power in spectrum and change in spectral power (CP) between these dominant frequency components are used to classify the recorded BAER signals into those of normals and the patients, and aid the clinician in quick and better diagnosis. The ranges of CP are estimated for the different groups and appear to be the most dominant parameter in the classification of the BAER signals.