Serial ECG Analysis: Absolute Rather Than Signed Changes in the Spatial QRS-T Angle Should Be Used to Detect Emerging Cardiac Pathology

Background. Larger one-time values of spatial QRS-T angle (SA) are associated with risk. However, experience how serial changes in SA (ΔSA) should be interpreted is lacking. Even within normal limits, any ΔSA likely signifies electrical remodeling. This study aimed to assess the impact of choosing either ΔSA or |ΔSA| as one of a set of serial ECG difference features that constitute the input for our deep learning serial-ECG classifier (DLSEC). Methods. DLSEC was trained and tested to detect emerging pathology in two serial ECG databases: a heart failure database and an acute ischemia database. Either ΔSA or |ΔSA| were among 13 features of serial-ECG differences. DLSEC was dynamically generated during learning, and testing area under the curve (AUC) of the receiver operating characteristic was computed. Results. The DLSECs performed well in emerging heart failure as well as in acute ischemia: testing AUCs were 72% and 84% for the heart failure database and 77% and 83% for the ischemia database, for ΔSA or |ΔSA| among the features, respectively. Conclusion. |ΔSA| among the features was superior to ΔSA in discriminating cases and controls. Our study supports the concept that any ΔSA, irrespective of its sign, indicates a worsening clinical condition. Further corroboration requires studies in other clinical situations.