An implantable cardioverter-defibrillator (ICD) is a device that must detect VT and VF arrhythmias on time and treat them. In this project, three CNN networks are designed to introduce the practical methods of using deep learning in heart electrophysiology signals processing. This project presents two speedy intelligent detection methods of ventricular fibrillation and ventricular tachycardia arrhythmias for ICD devices. It also provides another quick, innovative diagnosis method for use in intelligent electrocardiograph devices to detect abnormal cardiac signals. The first network is 1D-CNN for smart electrocardiographic devices to detect abnormal ECG signals. Dataset MIT-BIH has been used to train this network. This network with the most optimal number of parameters due to high detection speed has a high accuracy of 91%. The second and third networks are 2D-CNNs for use in implantable defibrillators. For the second network, a data set of 20 patients with cardiac arrhythmia and 20 patients without cardiac arrhythmia in an 8-month period of ICD check-up has been prepared. The third network is trained using the Spontaneous Ventricular Tachyarrhythmia Database. The second and third networks are designed to detect EGM signals in VF and VT modes with the optimal number of parameters and 100% accuracy in the second network and 90% in the third network. All three designed networks are in an optimal condition regarding the number of parameters and layers, so they have optimal speed and energy consumption.

The essential step of successful brachytherapy would be precise applicator/needles trajectory detection, which is an open problem yet. This study proposes a two-phase deep learning-based method to automate the localization of high-dose-rate (HDR) prostate brachytherapy catheters through the patient's CT images. The whole process is divided into two phases using two different deep neural networks. First, brachytherapy needles segmentation is accomplished through a pix2pix Generative Adversarial Neural Network (pix2pix GAN). Second, a generic Object Tracking Using Regression Networks (GOTURN) was used to predict the needle trajectories. These models were trained and tested on a clinical prostate brachytherapy dataset. Among the total 25 patients, 5 patients that consisted of 592 slices was dedicated to testing sets, and the rest were used as train/validation set. The total number of needles in these slices of CT images was 8764, of which the employed pix2pix network is able to segment 98.72% (8652 of total). Dice Similarity Coefficient (DSC) and IoU (Intersection over Union) between the network output and the ground truth were 0.95 and 0.90, respectively. Moreover, the F1-score, Recall, and Precision results were 0.95, 0.93, and 0.97, respectively. Regarding the location of the shafts, the proposed model has an error of 0.41 mm. The current study proposed a novel methodology to automatically localize and reconstruct the prostate HDR brachytherapy interstitial needles through the 3D CT images. The presented method can be utilized as a computer-aided module in clinical applications to automatically detect and delineate the multi-catheters, potentially enhancing the treatment quality.

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