参考文献/References:
[1] Yu LZhou L,Cao G,et al. Optogenetic modulation of cardiac?sympathetic nerve activity to?prevent ventricular?arrhythmias[J]. J Am Coll Cardiol,2017,70(22):2778-2790.
[2] Yu L,Huang B,Po SS,et al. Low-level tragus stimulation for the treatment of ischemia and reperfusion injury in patients with ST-segment elevation myocardial infarction:a proof-of-concept study[J]. JACC Cardiovasc Interv,2017,10(15):1511-1520.
[3] Kwon JM,Kim KH,Jeon KH,et al. Artificial intelligence algorithm for predicting mortality of patients with acute heart failure[J]. PLoS One,2019,14(7):e219302.
[4] Awan SE,Sohel F,Sanfilippo FM,et al. Machine learning in heart failure:ready for prime time[J]. Curr Opin Cardiol,2018,33(2):190-195.
[5] Attia ZI,Noseworthy PA,Lopez-Jimenez F,et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm:a retrospective analysis of outcome prediction[J]. Lancet,2019,394(10201):861-867.
[6] Ibrahim GM,Sharma P,Hyslop A,et al. Presurgical thalamocortical connectivity is associated with response to vagus nerve stimulation in children with intractable epilepsy[J]. Neuroimage Clin,2017,16:634-642.
[7] Li Z,Feng X,Wu Z,et al. Classification of atrial fibrillation recurrence based on a convolution neural network with SVM architecture[J]. IEEE Access,2019,7:77849-77856.
[8] Gliner V,Yaniv Y. An SVM approach for identifying atrial fibrillation[J]. Physiol Meas,2018,39(9):94007.
[9] Feeny AK,Rickard J,Patel D,et al. Machine learning prediction of response to cardiac resynchronization therapy: improvement versus current guidelines[J]. Circ Arrhythm Electrophysiol,2019,12(7):e007316.
[10] Yang L,Wu H,Jin X,et al. Study of cardiovascular disease prediction model based on random forest in eastern China[J]. Sci Rep,2020,10(1):5245.
[11] Ayyad SM,Saleh AI,Labib LM. Gene expression cancer classification using modified K-Nearest Neighbors technique[J]. Biosystems,2019,176:41-51.
[12] Cikes M,Sanchez-Martinez S,Claggett B,et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy[J]. Eur J Heart Fail,2019,21(1):74-85.
[13] Siontis KC,Noseworthy PA,Attia ZI,et al. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management[J]. Nat Rev Cardiol,2021,18(7):465-478.
[14] Cámara-Vázquez M?,Hernández-Romero I,Morgado-Reyes E,et al. Non-invasive estimation of atrial fibrillation driver position with convolutional neural networks and body surface potentials[J]. Front Physiol,2021,12:733449.
[15] Ramesh J,Solatidehkordi Z,Aburukba R,et al. Atrial fibrillation classification with smart wearables using short-term heart rate variability and deep convolutional neural networks[J]. Sensors,2021,21(21):7233.
[16] Kusayama T,Wong J,Liu X,et al. Simultaneous noninvasive recording of electrocardiogram and skin sympathetic nerve activity (neuECG)[J]. Nat Protoc,2020,15(5):1853-1877.
[17] Sevcencu C,Nielsen TN,Struijk JJ. A neural blood pressure marker for bioelectronic medicines for treatment of hypertension[J]. Biosens Bioelectron,2017,98:1-6.
[18] Vallone F,Ottaviani MM,Dedola F,et al. Simultaneous decoding of cardiovascular and respiratory functional changes from pig intraneural vagus nerve signals[J]. J Neural Eng,2021,18(4):0460a2 .
[19] Sabetian P,Sadat-Nejad Y,Yoo PB. Classification of directionally specific vagus nerve activity using an upper airway obstruction model in anesthetized rodents[J]. Sci Rep,2021,11(1):10682.
[20] Samejima S,Khorasani A,Ranganathan V,et al. Brain-computer-spinal interface restores upper limb function after spinal cord injury[J]. IEEE Trans Neural Syst Rehabil Eng,2021,29:1233-1242.
[21] Avdeew Y,Bergé-Laval V,le Rolle V,et al. Assessment of the use of multi-channel organic electrodes to record ENG on small nerves:application to phrenic nerve burst detection[J]. Sensors,2021,21(16):5594.
[22] Xu J,Nguyen AT,Wu T,et al. A Wide dynamic range neural data acquisition system with high-precision Delta-Sigma ADC and on-chip EC-PC spike processor[J]. IEEE Trans Biomed Circuits Syst,2020,14(3):425-440.
[23] Gold MR,van Veldhuisen DJ,Hauptman PJ,et al. Vagus nerve stimulation for the treatment of heart failure:the INOVATE-HF trial[J]. J Am Coll Cardiol,2016,68(2):149-158.
[24] Hamann JJ,Ruble SB,Stolen C,et al. Vagus nerve stimulation improves left ventricular function in a canine model of chronic heart failure[J]. Eur J Heart Fail,2013,15(12):1319-1326.
[25] Zannad F,de Ferrari GM,Tuinenburg AE,et al. Chronic vagal stimulation for the treatment of low ejection fraction heart failure:results of the NEural Cardiac TherApy foR Heart Failure (NECTAR-HF) randomized controlled trial[J]. Eur Heart J,2015,36(7):425-433.
[26] Toffa DH,Touma L,El MT,et al. Learnings from 30 years of reported efficacy and safety of vagus nerve stimulation (VNS) for epilepsy treatment:a critical review[J]. Seizure,2020,83:104-123.
[27] Ravan M,Sabesan S,D’Cruz O. On quantitative biomarkers of VNS therapy using EEG and ECG signals[J]. IEEE Trans Biomed Eng,2017,64(2):419-428.
[28] Mandal S,Sinha N. Prediction of atrial fibrillation based on nonlinear modeling of heart rate variability signal and SVM classifier[J]. Res Biomed Eng,2021,37:725-736?.
[29] Uradu A,Wan J,Doytchinova A,et al. Skin sympathetic nerve activity precedes the onset and termination of paroxysmal atrial tachycardia and fibrillation[J]. Heart Rhythm,2017,14(7):964-971.
[30] Ali L,Niamat A,Khan JA,et al. An Optimized stacked support vector machines based expert system for the effective prediction of heart failure[J]. IEEE Access,2019,7:54007-54014.
[31] Walsh D,Nelson KA. Autonomic nervous system dysfunction in advanced cancer[J]. Support Care Cancer,2002,10(7):523-528.
[32] Guo Y,Koshy S,Hui D,et al. Prognostic value of heart rate variability in patients with cancer[J]. J Clin Neurophysiol,2015,32(6):516-520.
[33] Shukla RS,Aggarwal Y. Nonlinear heart rate variability based artificial intelligence in lung cancer prediction[J]. J Appl Biomed,2018,16(2):145-155.
[34] Nolde JM,Marisol LL,Carnagarin R,et al. Machine learning powered tools for automated analysis of muscle sympathetic nerve activity recordings[J]. Physiol Rep,2021,9(16):e14996.
[35] Silverman HA,Stiegler A,Tsaava T,et al. Standardization of methods to record Vagus nerve activity in mice[J]. Bioelectron Med,2018,4:3.
[36] Aggarwal Y,Das J,Mazumder PM,et al. Heart rate variability features from nonlinear cardiac dynamics in identification of diabetes using artificial neural network and support vector machine[J]. Biocybern Biomed Eng,2020,40(3):1002-1009.
[37] Eryilmaz H,Dowling KF,Hughes DE,et al. Working memory load-dependent changes in cortical network connectivity estimated by machine learning[J]. Neuroimage,2020,217:116895.
相似文献/References:
[1]渠海贤 李涛 程流泉.人工智能在心脏磁共振成像中的应用进展[J].心血管病学进展,2019,(5):659.[doi:10.16806/j.cnki.issn.1004-3934.2019.05.001]
[2]袁佳栎 王群山.人工智能在心律失常诊断中的前景与挑战[J].心血管病学进展,2020,(10):999.[doi:10.16806/j.cnki.issn.1004-3934.2020.10.001]
YUAN JialiWANG Qunshan.Prospects and Challenges of Arrhythmia Diagnosis by Artificial Intelligence[J].Advances in Cardiovascular Diseases,2020,(6):999.[doi:10.16806/j.cnki.issn.1004-3934.2020.10.001]
[3]王苏淮 李晶洁.机器学习在心血管疾病中的临床应用进展[J].心血管病学进展,2021,(2):144.[doi:10.16806/j.cnki.issn.1004-3934.2021.02.013]
WANG Suhuai,LI Jingjie.Clinical Applications of Machine Learning in Cardiovascular Disease[J].Advances in Cardiovascular Diseases,2021,(6):144.[doi:10.16806/j.cnki.issn.1004-3934.2021.02.013]
[4]兰贝蒂 王瑞涛.人工智能及3D打印技术在心血管疾病诊疗中的应用进展[J].心血管病学进展,2021,(4):292.[doi:10.16806/j.cnki.issn.1004-3934.2021.04.002]
LAN Beidi,WANG Ruitao.Application Progress of Artificial Intelligence and 3D Printing Technology in the Diagnosis and Treatment of Cardiovascular Diseases[J].Advances in Cardiovascular Diseases,2021,(6):292.[doi:10.16806/j.cnki.issn.1004-3934.2021.04.002]
[5]艾克力亚尔·艾尼瓦尔 马翔.机器学习CNN模型在心血管疾病诊疗中的临床应用及研究进展[J].心血管病学进展,2021,(6):488.[doi:10.16806/j.cnki.issn.1004-3934.2021.06.003]
Aikeliyaer.ainiwaerMA xiang.Application of Convolutional Neural Network Model of Machine Learning for the Diagnosis and Treatment of Cardiovascular Diseases[J].Advances in Cardiovascular Diseases,2021,(6):488.[doi:10.16806/j.cnki.issn.1004-3934.2021.06.003]
[6]王继航 田进文 王建 郭毅 周星儿 付振虹 沈明志 刘亮.基于人工智能可穿戴设备及物联网的胸痛区域平台研究进展[J].心血管病学进展,2021,(6):492.[doi:10.16806/j.cnki.issn.1004-3934.2021.06.004]
WANG JihangTIAN JinwenWANG JianGUO YiZHOU XingerGUO utingFU ZhenhongSHEN MingzhiLIU Liang.Chest Pain Area Platform based on Artificial Intelligence Wearable Devices and Internet of Things[J].Advances in Cardiovascular Diseases,2021,(6):492.[doi:10.16806/j.cnki.issn.1004-3934.2021.06.004]
[7]孜拉来·艾尼瓦尔 周贤惠.无症状性心房颤动检测设备的研究进展[J].心血管病学进展,2022,(7):624.[doi:10.16806/j.cnki.issn.1004-3934.2022.07.000]
Zilalai AiniwarZHOU Xianhui.Detection Devices of Asymptomatic Atrial Fibrillation[J].Advances in Cardiovascular Diseases,2022,(6):624.[doi:10.16806/j.cnki.issn.1004-3934.2022.07.000]
[8]陈忠秀 李娅姣 李春梅 李晨.无导丝虚拟血流储备分数技术在稳定性冠心病介入治疗中的指导价值[J].心血管病学进展,2022,(10):865.[doi:10.16806/j.cnki.issn.1004-3934.2022.10.001]
CHEN Zhongxiu,LI Yajiao,LI Chunmei,et al.The Value of Wireless Virtual Fractional Flow Reserve in Guiding Percutaneous Coronary Intervention in Stable Coronary Artery Disease[J].Advances in Cardiovascular Diseases,2022,(6):865.[doi:10.16806/j.cnki.issn.1004-3934.2022.10.001]
[9]林锡祥 杨菲菲 陈煦 何昆仑.人工智能赋能医学影像在先天性心脏病医学诊治中的研究进展[J].心血管病学进展,2022,(12):1063.[doi:10.16806/j.cnki.issn.1004-3934.2022.12.002]
LIN Xixiang,YANG Feifei,CHEN Xu,et al.Artificial Intelligence Medical Imaging Technology in Medical Imaging of Congenital Heart Disease[J].Advances in Cardiovascular Diseases,2022,(6):1063.[doi:10.16806/j.cnki.issn.1004-3934.2022.12.002]
[10]中国智慧化血管健康全生命周期数字管理分级诊疗实践指南编写组 中国医药教育协会血管医学专业委员会 中国研究型医院学会移动医疗专业委员会 北京大学医学部血管健康研究中心 分子心血管学教育部重点实验室(北京大学).中国智慧化血管健康全生命周期数字管理分级诊疗实践指南（2022第一次报告）[J].心血管病学进展,2023,(1):71.[doi:10.16806/j.cnki.issn.1004-3934.2023.01.017]
China Medicine Education Association Vascular Medicine Professional Committee,et al..Digital Hierarchic Healthcare Clinical Practice Guideline for Intellectualized Whole-life Vascular Health Management in China[J].Advances in Cardiovascular Diseases,2023,(6):71.[doi:10.16806/j.cnki.issn.1004-3934.2023.01.017]
[11]沈文茜 杜国庆.机器学习在超声心动图中的应用进展[J].心血管病学进展,2021,(1):43.[doi:10.16806/j.cnki.issn.1004-3934.2021.01.000]
SHEN Wenqian,DU Guoqing.Machine Learning in Echocardiography[J].Advances in Cardiovascular Diseases,2021,(6):43.[doi:10.16806/j.cnki.issn.1004-3934.2021.01.000]
[12]秦地茂 李梦依 吴霜 邓祁 姚尧 刘英杰 郑颖.人工智能在心房颤动预测中的价值[J].心血管病学进展,2022,(10):874.[doi:10.16806/j.issn.1004-3934.2022.10.003]
QIN Dimao,LI Mengyi,WU Shuang,et al.Artificial Intelligence for Predicting Atrial Fibrillation[J].Advances in Cardiovascular Diseases,2022,(6):874.[doi:10.16806/j.issn.1004-3934.2022.10.003]
[13]屈展 刘凯.人工智能技术在高血压诊疗中的应用进展[J].心血管病学进展,2023,(1):48.[doi:10.16806/j.cnki.issn.1004-3934.2023.01.012]
QU Zhan,LIU Kai.Applications of Artificial Intelligence for Hypertension Management[J].Advances in Cardiovascular Diseases,2023,(6):48.[doi:10.16806/j.cnki.issn.1004-3934.2023.01.012]