AI for Emerging Verticals: Human-robot computing, sensing and networking最新文献_第2页

Multitask learning for autonomous driving 自动驾驶的多任务学习

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/pbpc034e_ch13

引用次数: 0

Conclusions 结论

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/pbpc034e_ch18

引用次数: 0

Machine-learning-enabled ECG monitoring for early detection of hyperkalaemia 基于机器学习的心电图监测，用于早期检测高钾血症

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-04-10 DOI: 10.1049/PBPC034E_CH14

Constance Farrell, M. Z. Shakir

{"title":"Machine-learning-enabled ECG monitoring for early detection of hyperkalaemia","authors":"Constance Farrell, M. Z. Shakir","doi":"10.1049/PBPC034E_CH14","DOIUrl":"https://doi.org/10.1049/PBPC034E_CH14","url":null,"abstract":"Hyperkalaemia is the medical terminology for a blood potassium level above normal parameters (greater than 5.5 mmol). This can have a variety of causes; however, patients with significant renal impairment/disease are particularly at a high risk as their kidneys are compromised and unable to filter out excess potassium from the bloodstream. Hyperkalaemia is a medical emergency and requires urgent medical intervention; if left untreated, there is an extremely high risk of cardiac arrest and death as high potassium levels directly affect the electrical activity of the heart. Cardiac activity can be observed by performing an electrocardiogram (ECG) test that is routinely used in all clinical areas worldwide. This research investigates the use of ECG tests as a diagnostic tool for the early detection of hyperkalaemia, and the role of machine learning in the prediction of blood potassium levels from ECG data alone. Support vector machines (SVMs), k-nearest neighbour (k-NN), decision tree and Gaussian Naive Bayes classifiers were used comparatively to classify ECG data as either `normokalaemia' or 'hyperkalaemia'. Results showed that the decision tree model performed the best, achieving a 90.9 per cent predictive accuracy.","PeriodicalId":187411,"journal":{"name":"AI for Emerging Verticals: Human-robot computing, sensing and networking","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121604110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EEG-based biometrics: effects of template ageing 基于脑电图的生物识别:模板老化的影响

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-02-09 DOI: 10.1049/PBPC034E_CH11

Pablo Arnau-González, Stamos Katsigiannis, M. Arevalillo-Herráez, N. Ramzan

{"title":"EEG-based biometrics: effects of template ageing","authors":"Pablo Arnau-González, Stamos Katsigiannis, M. Arevalillo-Herráez, N. Ramzan","doi":"10.1049/PBPC034E_CH11","DOIUrl":"https://doi.org/10.1049/PBPC034E_CH11","url":null,"abstract":"This chapter discusses the effects of template ageing in EEG-based biometrics. The \u0000chapter also serves as an introduction to general biometrics and its main tasks: Identification and verification. To do so, we investigate different characterisations of \u0000EEG signals and examine the difference of performance in subject identification between single session and cross-session identification experiments. In order to do \u0000this, EEG signals are characterised with common state-of-the-art features, i.e. Mel \u0000Frequency Cepstral Coefficients (MFCC), Autoregression Coefficients, and Power \u0000Spectral Density-derived features. The samples were later classified using various \u0000classifiers, including Support Vector Machines and k-Nearest Neighbours with different parametrisations. Results show that performance tends to be worse for crosssession identification compared to single session identification. This finding suggests that temporal permanence of EEG signals is limited and thus more sophisticated methods are needed in order to characterise EEG signals for the task of subject \u0000identification","PeriodicalId":187411,"journal":{"name":"AI for Emerging Verticals: Human-robot computing, sensing and networking","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116798970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0