{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T11:04:34Z","timestamp":1784027074515,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:00:00Z","timestamp":1783814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electrocardiography (ECG)-only workload-state classification offers a lower-burden physiological sensing route than denser multimodal, multi-sensor physiological, or neuroimaging setups for controlled laparoscopic training research. This study evaluated whether ECG-only representations can classify condition-derived cognitive workload states during a controlled laparoscopic peg transfer task performed under Control and auditory N-back conditions (N0, N1, and N2). Twenty surgical trainees from an advanced surgical-skills course completed the task protocol, and 17 participants entered ECG modelling after ECG quality-control exclusions. The retained ECG modelling dataset comprised 268 task blocks (Control\/N0\/N1\/N2: 68\/68\/68\/64), evaluated as held-out task-block predictions in a known-participant four-fold leave-one-round-out (LOTO) evaluation. Branch-specific raw ECG windows, recurrence-plot sequences, and heart-rate\/time-domain heart-rate-variability inputs are detailed in the Methods, and model metrics were computed after reduction to task-block predictions. The primary endpoint was Surgery Task Load Index (SURG-TLX)-aligned low\/high workload, defined as Control plus N0 versus N1 plus N2. Four-class and three-level endpoints were retained as secondary views. Raw ECG, recurrence-plot (RP)-derived, hybrid score-level fusion, and conventional heart-rate\/time-domain heart-rate-variability Random Forest (HRV-RF) models were compared using a locked evaluation protocol, leakage-aware train-fold-only preprocessing, participant-clustered confidence intervals, and planned paired tests for the primary endpoint. On the primary low\/high endpoint, raw ECG achieved the highest macro-F1\/balanced accuracy (0.865\/0.865), followed by the hybrid branch (0.847\/0.847) and HRV-RF (0.648\/0.649). Raw ECG and hybrid were supported over RP-derived and HRV-RF under the planned paired tests. On selected secondary endpoints, hybrid achieved higher macro-F1 values than raw ECG, consistent with possible endpoint-dependent RP-derived complementarity rather than a general hybrid advantage. These findings support ECG-only block-level workload-state classification in this controlled training setting. The evidence is retrospective and based on held-out rounds from known participants rather than subject-independent or deployment validation.<\/jats:p>","DOI":"10.3390\/s26144427","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:53:58Z","timestamp":1783932838000},"page":"4427","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ECG-Only Cognitive Workload State Classification in Laparoscopic Training Using Raw and Recurrence-Plot Representations"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5177-4029","authenticated-orcid":false,"given":"Kaizhe","family":"Jin","sequence":"first","affiliation":[{"name":"Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, London SW7 2AZ, UK"},{"name":"Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9466-8604","authenticated-orcid":false,"given":"Adrian","family":"Rubio-Solis","sequence":"additional","affiliation":[{"name":"Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, London SW7 2AZ, UK"},{"name":"Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0211-9358","authenticated-orcid":false,"given":"Ravi","family":"Naik","sequence":"additional","affiliation":[{"name":"Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, London SW7 2AZ, UK"},{"name":"Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3725-5843","authenticated-orcid":false,"given":"George","family":"Mylonas","sequence":"additional","affiliation":[{"name":"Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, London SW7 2AZ, UK"},{"name":"Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1002\/bjs.10795","article-title":"Systematic review of measurement tools to assess surgeons\u2019 intraoperative cognitive workload","volume":"105","author":"Dias","year":"2018","journal-title":"Br. 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