Hester Espensen (beatpizza81)

Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database. Our DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed promising results on all 12-lead inputs. Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers. We performed two dilic arrhythmia database comprising more than 10,000 records. We constructed an efficient DNN model for automated detection of arrhythmia using these records. Cardiovascular diseases are critical diseases and need to be diagnosedas early as possible. There is a lack of medical professionals in remote areas to diagnose these diseases. Artificial intelligence-based automatic diagnostic tools can help to diagnose cardiac diseases. This work presents an automatic classification method using machine learning to diagnose multiple cardiac diseases from phonocardiogram signals. The proposed system involves a convolutional neural network (CNN) model because of its high accuracy and robustness to automatically diagnose the cardiac disorders from the heart sounds. To improve the accuracy in a noisy environment and make the method robust, the proposed method has used data augmentation techniques for training and multi-classification of multiple cardiac diseases. The model has been validated both heart sound data and augmented data using n-fold cross-validation. Results of all fold have been shown reported in this work. The model has achieved accuracy on the test set up to 98.60% to diagnose multiple cardiac diseases. The proposed model can be ported to any computing devices like computers, single board computing processors, android handheld devices etc. To make a stand-alone diagnostic tool that may be of help in remote primary health care centres. The proposed method is non-invasive, efficient, robust, and has low time complexity making it suitable for real-time applications. The proposed model can be ported to any computing devices like computers, single board computing processors, android handheld devices etc. To make a stand-alone diagnostic tool that may be of help in remote primary health care centres. The proposed method is non-invasive, efficient, robust, and has low time complexity making it suitable for real-time applications.Rhodopsin S334ter-3 retinal degeneration rats have been widely used to investigate degenerative diseases of the retina. In this model, morphological and electrophysiological changes have been observed in the retina, superior colliculus and primary visual cortex (V1). However, no study so far has examined rhodopsin S334ter-3 rats with regards to their contrast response in V1 - a fundamental property of visual information processing. In this study, experimental rats (S334ter-3) carried one copy of the mutant transgene. We compared responses to spatio-temporal variations in luminance contrast in the primary visual cortex of these rats with those in Long-Evans (LE) rats to elucidate the degeneration-specific activity changes in this part of the visual pathway. We measured extracellular responses to different stimulus contrasts at the preferred parameters of each recorded cell under classical re