Arjuna Scagnetto | Searcharxiv

arXiv2026

ECG Biometrics with ArcFace-Inception: External Validation on MIMIC and HEEDB

ECG biometrics has been studied mainly on small cohorts and short inter-session intervals, leaving open how identification behaves under large galleries, external domain shift, and multi-year temporal gaps. We evaluated a 1D Inception-v1 model trained with ArcFace on an internal clinical corpus of 164,440 12-lead ECGs from 53,079 patients and tested it on larger cohorts derived from MIMIC-IV-ECG and HEEDB. The study used a unified closed-set leave-one-out protocol with Rank@K and TAR@FAR metrics, together with scale, temporal-stress, reranking, and confidence analyses. Under general comparability, the system achieved Rank@1 of 0.9506 on ASUGI-DB, 0.8291 on MIMIC-GC, and 0.6884 on HEEDB-GC. In the temporal stress test at constant gallery size, Rank@1 declined from 0.7853 to 0.6433 on MIMIC and from 0.6864 to 0.5560 on HEEDB from 1 to 5 years. Scale analysis on HEEDB showed monotonic degradation as gallery size increased and recovery as more examinations per patient became available. On HEEDB-RR, post-hoc reranking further improved retrieval, with AS-norm reaching Rank@1 = 0.8005 from a 0.7765 baseline. ECG identity information therefore remains measurable under externally validated large-scale closed-set conditions, but its operational quality is strongly affected by domain heterogeneity, longitudinal drift, gallery size, and second-stage score processing.

cs.LG↗

arXiv2023

How much of the past matters? Using dynamic survival models for the monitoring of potassium in heart failure patients using electronic health records

Statistical methods to study the association between a longitudinal biomarker and the risk of death are very relevant for the long-term care of subjects affected by chronic illnesses, such as potassium in heart failure patients. Particularly in the presence of comorbidities or pharmacological treatments, sudden crises can cause potassium to undergo very abrupt yet transient changes. In the context of the monitoring of potassium, there is a need for a dynamic model that can be used in clinical practice to assess the risk of death related to an observed patient's potassium trajectory. We considered different dynamic survival approaches, starting from the simple approach considering the most recent measurement, to the joint model. We then propose a novel method based on wavelet filtering and landmarking to retrieve the prognostic role of past short-term potassium shifts. We argue that while taking into account past information is important, not all past information is equally informative. State-of-the-art dynamic survival models are prone to give more importance to the mean long-term value of potassium. However, our findings suggest that it is essential to take into account also recent potassium instability to capture all the relevant prognostic information. The data used comes from over 2000 subjects, with a total of over 80 000 repeated potassium measurements collected through Administrative Health Records and Outpatient and Inpatient Clinic E-charts. A novel dynamic survival approach is proposed in this work for the monitoring of potassium in heart failure. The proposed wavelet landmark method shows promising results revealing the prognostic role of past short-term changes, according to their different duration, and achieving higher performances in predicting the survival probability of individuals.

stat.ME↗