{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T15:31:52Z","timestamp":1747150312706},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685335","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,22]]},"abstract":"<jats:p>Feature attribution methods stand as a popular approach for explaining the decisions made by convolutional neural networks. Given their nature as local explainability tools, these methods fall short in providing a systematic evaluation of their global meaningfulness. This limitation often gives rise to confirmation bias, where explanations are crafted after the fact. Consequently, we conducted a systematic investigation of feature attribution methods within the realm of electrocardiogram time series, focusing on R-peak, T-wave, and P-wave. Using a simulated dataset with modifications limited to the R-peak and T-wave, we evaluated the performance of various feature attribution techniques across two CNN architectures and explainability frameworks. Extending our analysis to real-world data revealed that, while feature attribution maps effectively highlight significant regions, their clarity is lacking, even under the simulated ideal conditions, resulting in blurry representations.<\/jats:p>","DOI":"10.3233\/shti240489","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:26:50Z","timestamp":1724405210000},"source":"Crossref","is-referenced-by-count":1,"title":["Assessing the Reliability of Machine Learning Explanations in ECG Analysis Through Feature Attribution"],"prefix":"10.3233","author":[{"given":"Lucas","family":"Plagwitz","sequence":"first","affiliation":[{"name":"Institute of Medical Informatics, University M\u00fcnster, Germany"},{"name":"Interdisciplinary Center for Clinical Research (IZKF), M\u00fcnster, Germany"}]},{"given":"Lucas","family":"Bickmann","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University M\u00fcnster, Germany"}]},{"given":"Antonius","family":"B\u00fcscher","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University M\u00fcnster, Germany"},{"name":"Interdisciplinary Center for Clinical Research (IZKF), M\u00fcnster, Germany"},{"name":"Clinic for Cardiology II: Electrophysiology, University Hospital M\u00fcnster, Germany"}]},{"given":"Julian","family":"Varghese","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University M\u00fcnster, Germany"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240489","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:26:51Z","timestamp":1724405211000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240489"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240489","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}