Makine öğrenme algoritmaları kalp ritminin sınıflandırılması

The electrical activity of the heart is recorded via an electrocardiogram signal. Electrodes on the skin detect minor voltage changes induced by depolarization and repolarization of the heart muscle. The electric potential between two electrodes can be measured. An ECG lead is a pair of electrodes that are connected together. Each lead offers a unique perspective on a heart's electrical activity.The EEG signals have a significant potential of discovering various neurological illnesses in an early stage. We have proven why it is superior than MRI. Being affordable and efficient, it can be employed for various investigations even with a reduced budget as compared to MRI. Many Deep Learning algorithms have been implemented on EEG recordings in order to classify different neurological illnesses and brain interface applications. In this research we have done an exhaustive literature review on application of various neural networks on EEG data and how utilizing these neural networks one was able to categorize numerous neurological illnesses including Seizure, AD and Depression. The pre-processing phase considerably influences the performance of the model. In this study, we train different machine learning algorithms models on ECG datasets and compare their performance with each other. We found that at least in our experiment, both hand-crafted features and the feature extractors each have both their favourable and bad characteristics.