Relationship between electrocardiogram-based features and personality traits: Machine learning approach.
Relationship between electrocardiogram-based features and personality traits: Machine learning approach.
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DOI：
10.1111/anec.12919
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发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
Milašinović G
中科院分区：
文献类型：
--
作者：
Boljanić T;Miljković N;Lazarevic LB;Knezevic G;Milašinović G
在已知人类情绪与标准体表心电图（ECG）之间关系的基础上，本文探讨了从放松状态下记录的标准ECG中提取的特征与七种人格特质之间的关系（诚实/谦逊、正直、超越、诚实、尽责、开放，和分解）通过使用机器学习（ML）该方法从基于ECG的特征中学习，并通过采用自动化软件算法来预测适当的人格特质。共有71名健康的大学生参加了这项研究。为了量化每个ECG记录的62个基于ECG的参数（心率变异性以及基于时间和幅度的参数），我们使用了计算程序以及公开可用的数据和代码。在62个参数中，根据其在临床实践中的诊断相关性，将34个参数分为单独的特征。为了研究特征对人格特质分类的影响并进行分类，我们使用了随机森林ML算法。当采用临床相关ECG特征时，解体（81.3%）和诚实/谦卑（75.0%）的分类准确率较高，开放性（73.3%）和尽责性（70%）的分类准确率为中高，而易混淆性（56.3%），过度性（47.1%）和不诚实性（43.8%）的分类准确率较低。当使用所有计算的特征时，分类准确率相同或更低，除了eXtravision（52.9%）。相关性分析选定的功能。结果表明，临床相关的功能可能适用于人格特质的预测，虽然没有显着差异，发现选定的参数组。应进一步探讨已建立的关系的生理关联。
Based on the known relationship between the human emotion and standard surface electrocardiogram (ECG), we explored the relationship between features extracted from standard ECG recorded during relaxation and seven personality traits (Honesty/humility, Emotionality, eXtraversion, Agreeableness, Conscientiousness, Openness, and Disintegration) by using the machine learning (ML) approach which learns from the ECG‐based features and predicts the appropriate personality trait by adopting an automated software algorithm. A total of 71 healthy university students participated in the study. For quantification of 62 ECG‐based parameters (heart rate variability, as well as temporal and amplitude‐based parameters) for each ECG record, we used computation procedures together with publicly available data and code. Among 62 parameters, 34 were segregated into separate features according to their diagnostic relevance in clinical practice. To examine the feature influence on personality trait classification and to perform classification, we used random forest ML algorithm. Classification accuracy when clinically relevant ECG features were employed was high for Disintegration (81.3%) and Honesty/humility (75.0%) and moderate to high for Openness (73.3%) and Conscientiousness (70%), while it was low for Agreeableness (56.3%), eXtraversion (47.1%), and Emotionality (43.8%). When all calculated features were used, the classification accuracies were the same or lower, except for the eXtraversion (52.9%). Correlation analysis for selected features is presented. Results indicate that clinically relevant features might be applicable for personality traits prediction, although no remarkable differences were found among selected groups of parameters. Physiological associations of established relationships should be further explored.
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影响因子：
7.6
作者：
Ashton, MC;Lee, K;De Raad, B
通讯作者：
De Raad, B
DOI：
10.1111/anec.12848
发表时间：
2021-07
期刊：
Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
影响因子：
--
作者：
Krantz DS;Harris KM;Rogers HL;Whittaker KS;Haigney MCP;Kop WJ
通讯作者：
Kop WJ
影响因子：
3.3
作者：
DeYoung, Colin G.
通讯作者：
DeYoung, Colin G.
影响因子：
4.6
作者：
Censi F;Corazza I;Reggiani E;Calcagnini G;Mattei E;Triventi M;Boriani G
通讯作者：
Boriani G
影响因子：
5.4
作者：
Hämmerle P;Eick C;Blum S;Schlageter V;Bauer A;Rizas KD;Eken C;Coslovsky M;Aeschbacher S;Krisai P;Meyre P;Vesin JM;Rodondi N;Moutzouri E;Beer J;Moschovitis G;Kobza R;Di Valentino M;Corino VDA;Laureanti R;Mainardi L;Bonati LH;Sticherling C;Conen D;Osswald S;Kühne M;Zuern CS;Swiss‐AF Study Investigators
通讯作者：
Swiss‐AF Study Investigators