A Heart Movement Detector based on Machine Learning to analyze the motion and rhythms of the human heartbeat

This paper has the sole purpose of emphasizing the fact that the movements of the human heart hold an important position in the field of heart ailment diagnosis. Our study brings out a new approach to the analysis of heart movements using machine learning. We have used the deep learning algorithms ResNet and BiLSTM for the classification and segmentation of the images from the video inputs and Explainable AI techniques LIME and SHAP have been applied in order to increase the interpretability and predictability of the model. During the training and testing phases, the downstream values of the tasks had been modified into the pre-trained set which was projected to self-supervised learning through the integration of SimCLR layers. The values thus obtained after the analysis of the pre-trained set helped us to evaluate the working of the model while predicting the next outcomes from a sequence of inputs. After the successful integration of these results with the current sequence of inputs, we have observed that the results generated by the model showed an increased learning rate of le-4 and the predictive outcomes becoming better with time. The highest accuracy value has been observed to be 92.5%. The values of the precision, recall, and f1 score also showed increasing trends with the passage of epochs and the final result suggested that the model is sufficient to provide proper medical assistance in terms of automation of the heart movement detection and it would be helpful for the early diagnosis of heart ailments.