Conventional lead implantation requires fluoroscopic guidance. This may be problematic in certain patient groups such as pregnant patients. We report a case of an implantable cardioverter defibrillator implantation without fluoroscopy in a pregnant patient.

Significance: Pulmonary vein isolation with catheter-based radiofrequency ablation (RFA) is carried out frequently to treat atrial fibrillation. However, RFA lesion creation is only guided by indirect information (e.g., temperature, impedance, and contact force), which may result in poor lesion quality (e.g., nontransmural) and can lead to reoccurrence or complications.Aim: The feasibility of guiding intracardiac RFA with an integrated polarization-sensitive optical coherence tomography (PSOCT)-RFA catheter in the right atria (RA) of living swine is demonstrated.Approach: In total, 12 sparse lesions were created in the RA of three living swine using an integrated PSOCT-RFA catheter with standard ablation protocol. PSOCT images were displayed in real time to guide catheter-tissue apposition. After experiments, post-processed PSOCT images were analyzed to assess lesion quality and were compared with triphenyltetrazolium chloride (TTC) lesion quality analysis.Results: Five successful lesions identified with PSOCT images were all confirmed by TTC analysis. In two ablations, PSOCT imaging detected gas bubble formation, indicating overtreatment. Unsuccessful lesions observed with PSOCT imaging were confirmed by TTC analysis. Conclusions:The results demonstrate that the PSOCT-RFA catheter provides real-time feedback to guide catheter-tissue apposition, monitor lesion quality, and possibly help avoid complications due to overtreatment, which may enable more effective and safer RFA treatment.

No abstract

Congenital Long QT Syndrome (LQTS) is a genetic disease and associated with significant arrhythmias and sudden cardiac death. We introduce a noninva-sive procedure in which Discrete Wavelet Trans-form (DWT) is used to extract features from elec-trocardiogram (ECG) time-series data first, then the extracted features data is classified as either abnormal or unaffected using Support Vector Machines (SVM). A total of 26 genetically identified patients with LQTS and 19 healthy controls were studied. Due to the limited number of samples, model selection was done by training 44 instances and testing it on remaining one in each run. The proposed method shows reasonably high average accuracy in LQTS diagnosis when combined with best parameter selection process in the classifica-tion stage. An accuracy of 80%is achieved when Sigmoid kernel is used in v-SVM with parameters v = 0.58 and r = 0.5. The corresponding SVM model showed a classification rate of 21/26 for LQTS pa-tients and 15/19 for controls. Since the diagnosis of LQTS can be challenging, the proposed method is promising and can be a potential tool in the correct diagnosis. The method may be improved further if larger data sets can be obtained and used