Traditional risk score prediction is based on vital signs and clinical assessment. In this paper, we present an intelligent scoring system for the prediction of cardiac arrest within 72 h. The patient population is represented by a set of feature vectors, from which risk scores are derived based on geometric distance calculation and support vector machine. Each feature vector is a combination of heart rate variability (HRV) parameters and vital signs. Performance evaluation is conducted on the leave-one-out cross-validation framework, and receiver operating characteristic, sensitivity, specificity, positive predictive value, and negative predictive value are reported. Experimental results reveal that the proposed scoring system not only achieves satisfactory performance on determining the risk of cardiac arrest within 72 h but also has the ability to generate continuous risk scores rather than a simple binary decision by a traditional classifier. Furthermore, the proposed scoring system works well for both balanced and imbalanced datasets, and the combination of HRV parameters and vital signs shows superiority in prediction to using HRV parameters only or vital signs only.

In this paper, we present a new method for classification of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) method. The intrinsic mode functions (IMFs) generated by EMD method can be considered as a set of amplitude and frequency modulated (AM-FM) signals. The Hilbert transformation of IMFs provides an analytic signal representation of the IMFs. The two bandwidths, namely amplitude modulation bandwidth (B(AM)) and frequency modulation bandwidth (B(FM)), computed from the analytic IMFs, have been used as an input to least squares support vector machine (LS-SVM) for classifying seizure and non-seizure EEG signals. The proposed method for classification of EEG signals based on the bandwidth features (B(A M) and B (FM)) and the LS-SVM has provided better classification accuracy than the method of Liang et. al [20]. The experimental results with the recorded EEG signals from a published dataset are included to show the effectiveness of the proposed method for EEG signal classification.

The IEEE Transactions on Information Technology in Biomedicine (T-ITB) will be retitled as the IEEE Journal of Biomedical and Health Informatics (J-BHI) starting in January, 2013. The IEEE Transactions on Information Technology in Biomedicine was launched with four issues per year in 1997. After more than a decade of steady growth, the journal ventured into new challenges: open access of publication and rapid expansion in all health informatics-related fields. In this concluding issue of T-ITB the authors would like to take this opportunity to report briefly some historical retrospectives of journal with known statistics of it and to extend a special appreciation from the current editorial office to readers, authors, reviewers, associate editors, Engineering in Medicine and Biology Society (EMBS) relevant committees, IEEE publication staff, and especially two previous Editors-in-Chief for their great contributions to make the Transactions successful. The launching of the J-BHI as the new title of T-ITB is an outcome of the vision of the EMBS's 2011 Publications Committee. The title change and scope revision were accomplished through more than two years of team efforts by T-ITB Editorial Board, EMBS Executive Office, Publication Committee, and AdCom. In the next editorial to be published on January issue of J-BHI, we will discuss the rationale behind the title change and scope revision as well as the grand challenges in health informatics with future perspectives.