New Vectorcardiographic non-planarity measure of T-wave loop improves separation between healthy subjects and myocardial infarction patients

Changes in cardiac repolarisation during spontaneous nocturnal hypoglycaemia in subjects with type 1 diabetes: a preliminary report

AIMS

Experimental studies have revealed that hypoglycaemia can result in morphological changes in electrocardiographic repolarisation in subjects with type 1 diabetes. However, the influence of spontaneous nocturnal hypoglycaemia on repolarisation morphology in a 'real life' situation is not clear.

METHODS

Adults with type 1 diabetes (n = 11) underwent continuous glucose monitoring with a subcutaneous sensor and digital 12-lead ECG recording for three nights. T-wave morphology was analysed with custom-made software during both hypoglycaemia (glucose <3.5 mmol/l at least 20 min) from ten consecutive heart beats in the middle of the deepest hypoglycaemia and from a control nonhypoglycaemic period (glucose ≥5.0 mmol/l) from the same recording.

RESULTS

In the comparison of 10 hypoglycaemia-control pairs, heart rate (65 ± 12 beats/min during normoglycaemia versus 85 ± 19 beats/min during hypoglycaemia, p = 0.028) increased and the QT_c interval (439 ± 5 vs. 373 ± 5 ms, respectively, p = 0.025) decreased significantly during hypoglycaemia. The spatial QRS-T angle (TCRT) was reduced, and the roughness of the T-wave loop (T-E) increased significantly (p = 0.037 for both) in the patients during hypoglycaemia.

CONCLUSIONS

In adults with type 1 diabetes, spontaneous nocturnal hypoglycaemia results in morphological changes and increased heterogeneity of global cardiac repolarisation. These changes may contribute to the risk of 'dead in bed' syndrome encountered in young individuals with type 1 diabetes.

Detection of myocardial scar from the VCG using a supervised learning approach

This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the investigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue.