Cardiac autonomic neuropathy in patients with diabetes and no symptoms of coronary artery disease: comparison of 123I-metaiodobenzylguanidine myocardial scintigraphy and heart rate variability

PURPOSE The purpose of this study was to evaluate the prevalence of cardiac autonomic neuropathy (CAN) in a cohort of patients with type 2 diabetes, truly asymptomatic for coronary artery disease (CAD), using heart rate variability (HRV) and (123)I-metaiodobenzylguanidine ((123)I-mIBG) myocardial scintigraphy. METHODS The study group comprised 88 patients with type 2 diabetes prospectively recruited from an outpatient diabetes clinic. In all patients myocardial perfusion scintigraphy, CAN by HRV and (123)I-mIBG myocardial scintigraphy were performed. Two or more abnormal tests were defined as CAN-positive (ECG-based CAN) and one or fewer as CAN-negative. CAN assessed by (123)I-mIBG scintigraphy was defined as abnormal if the heart-to-mediastinum ratio was 25%, or the total defect score was >13. RESULTS The prevalence of CAN in patients asymptomatic for CAD with type 2 diabetes and normal myocardial perfusion assessed by HRV and (123)I-mIBG scintigraphy was respectively, 27% and 58%. Furthermore, in almost half of patients with normal HRV, (123)I-mIBG scintigraphy showed CAN. CONCLUSION The current study revealed a high prevalence of CAN in patients with type 2 diabetes. Secondly, disagreement between HRV and (123)I-mIBG scintigraphy for the assessment of CAN was observed.Cardiovascular Aspects of Radiolog

Embedding-based subsequence matching in time-series databases

We propose an embedding-based framework for subsequence matching in time-series databases that improves the efficiency of processing subsequence matching queries under the Dynamic Time Warping (DTW) distance measure. This framework partially reduces subsequence matching to vector matching, using an embedding that maps each query sequence to a vector and each database time series into a sequence of vectors. The database embedding is computed offline, as a preprocessing step. At runtime, given a query object, an embedding of that object is computed online. Relatively few areas of interest are efficiently identified in the database sequences by comparing the embedding of the query with the database vectors. Those areas of interest are then fully explored using the exact DTW-based subsequence matching algorithm. We apply the proposed framework to define two specific methods. The first method focuses on time-series subsequence matching under unconstrained Dynamic Time Warping. The second method targets subsequence matching under constrained Dynamic Time Warping (cDTW), where warping paths are not allowed to stray too much off the diagonal. In our experiments, good trade-offs between retrieval accuracy and retrieval efficiency are obtained for both methods, and the results are competitive with respect to current state-of-the-art methods