![]() DLSEC was dynamically generated during learning, and testing area under the curve (AUC) of the receiver operating characteristic was computed. Either ΔSA or |ΔSA| were among 13 features of serial-ECG differences. DLSEC was trained and tested to detect emerging pathology in two serial ECG databases: a heart failure database and an acute ischemia database. ![]() 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). Even within normal limits, any ΔSA likely signifies electrical remodeling. However, experience how serial changes in SA (ΔSA) should be interpreted is lacking. Larger one-time values of spatial QRS-T angle (SA) are associated with risk.
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