{"title":"通过可解释深度学习识别缺血后室性心动过速电生理学研究中的致心律失常部位","authors":"","doi":"10.1016/j.bspc.2024.106844","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and objective</h3><p>Abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs) are frequently considered as markers of arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) during electroanatomic mapping (EAM) procedures. Their detection is strongly operator-dependent and time-consuming. This work explores the adoption of explainable deep learning to support the discrimination between physiological EGMs and AVPs.</p></div><div><h3>Methods</h3><p>Three convolutional neural networks were trained to discriminate the target signals based on their time–frequency representations by synchrosqueezed wavelet transform. The efficacy of the method was assessed on 2561 real bipolar EGMs collected from nine post-ischemic VT patients.</p></div><div><h3>Results</h3><p>The proposed approach achieved high performance, with accuracy levels reaching up to 89%. It also demonstrated coherent localization of the arrhythmogenic sites with respect to conventional voltage and local activation time maps. Moreover, by using saliency maps, AVPs discriminant signatures were highlighted at high frequencies (i.e., in the 103–125 Hz band, which was generally relevant for every network), in line with prior evidence.</p></div><div><h3>Conclusion</h3><p>For the first time, deep learning has been successfully applied and robustly evaluated in the field. The proposed approach paves the way to the development of effective AI-driven systems. These systems will enable a faster, trustworthy and operator-independent identification of AVPs in VT EAM procedures. Furthermore, even without injecting prior knowledge in the adopted models, the analysis of saliency maps revealed that CNNs are prone to autonomously select time–frequency ranges of the EGMs in agreement with the current knowledge.</p></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1746809424009029/pdfft?md5=586f3001745dc1f4ce9c0a47c52e28fe&pid=1-s2.0-S1746809424009029-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Arrhythmogenic sites identification in post-ischemic ventricular tachycardia electrophysiological studies by explainable deep learning\",\"authors\":\"\",\"doi\":\"10.1016/j.bspc.2024.106844\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background and objective</h3><p>Abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs) are frequently considered as markers of arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) during electroanatomic mapping (EAM) procedures. Their detection is strongly operator-dependent and time-consuming. This work explores the adoption of explainable deep learning to support the discrimination between physiological EGMs and AVPs.</p></div><div><h3>Methods</h3><p>Three convolutional neural networks were trained to discriminate the target signals based on their time–frequency representations by synchrosqueezed wavelet transform. The efficacy of the method was assessed on 2561 real bipolar EGMs collected from nine post-ischemic VT patients.</p></div><div><h3>Results</h3><p>The proposed approach achieved high performance, with accuracy levels reaching up to 89%. It also demonstrated coherent localization of the arrhythmogenic sites with respect to conventional voltage and local activation time maps. Moreover, by using saliency maps, AVPs discriminant signatures were highlighted at high frequencies (i.e., in the 103–125 Hz band, which was generally relevant for every network), in line with prior evidence.</p></div><div><h3>Conclusion</h3><p>For the first time, deep learning has been successfully applied and robustly evaluated in the field. The proposed approach paves the way to the development of effective AI-driven systems. These systems will enable a faster, trustworthy and operator-independent identification of AVPs in VT EAM procedures. Furthermore, even without injecting prior knowledge in the adopted models, the analysis of saliency maps revealed that CNNs are prone to autonomously select time–frequency ranges of the EGMs in agreement with the current knowledge.</p></div>\",\"PeriodicalId\":55362,\"journal\":{\"name\":\"Biomedical Signal Processing and Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1746809424009029/pdfft?md5=586f3001745dc1f4ce9c0a47c52e28fe&pid=1-s2.0-S1746809424009029-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Signal Processing and Control\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1746809424009029\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809424009029","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Arrhythmogenic sites identification in post-ischemic ventricular tachycardia electrophysiological studies by explainable deep learning
Background and objective
Abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs) are frequently considered as markers of arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) during electroanatomic mapping (EAM) procedures. Their detection is strongly operator-dependent and time-consuming. This work explores the adoption of explainable deep learning to support the discrimination between physiological EGMs and AVPs.
Methods
Three convolutional neural networks were trained to discriminate the target signals based on their time–frequency representations by synchrosqueezed wavelet transform. The efficacy of the method was assessed on 2561 real bipolar EGMs collected from nine post-ischemic VT patients.
Results
The proposed approach achieved high performance, with accuracy levels reaching up to 89%. It also demonstrated coherent localization of the arrhythmogenic sites with respect to conventional voltage and local activation time maps. Moreover, by using saliency maps, AVPs discriminant signatures were highlighted at high frequencies (i.e., in the 103–125 Hz band, which was generally relevant for every network), in line with prior evidence.
Conclusion
For the first time, deep learning has been successfully applied and robustly evaluated in the field. The proposed approach paves the way to the development of effective AI-driven systems. These systems will enable a faster, trustworthy and operator-independent identification of AVPs in VT EAM procedures. Furthermore, even without injecting prior knowledge in the adopted models, the analysis of saliency maps revealed that CNNs are prone to autonomously select time–frequency ranges of the EGMs in agreement with the current knowledge.
期刊介绍:
Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management.
Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.