Second-Order Disjoint Factor Analysis

Hierarchical models are often considered to measure latent concepts defining nested sets of manifest variables. Therefore, by supposing a hierarchical relationship among manifest variables, the general latent concept can be represented by a tree structure where each internal node represents a specific order of abstraction for the latent concept measured. In this paper, we propose a new latent factor model called second-order disjoint factor analysis in order to model an unknown hierarchical structure of the manifest variables with two orders. This is a second-order factor analysis, which—respect to the second-order confirmatory factor analysis—is exploratory, nested and estimated simultaneously by maximum likelihood method. Each subset of manifest variables is modeled to be internally consistent and reliable, that is, manifest variables related to a factor measure “consistently” a unique theoretical construct. This feature implies that manifest variables are positively correlated with the related factor and, therefore, the associated factor loadings are constrained to be nonnegative. A cyclic block coordinate descent algorithm is proposed to maximize the likelihood. We present a simulation study that investigates the ability to get reliable factors. Furthermore, the new model is applied to identify the underlying factors of well-being showing the characteristics of the new methodology. A final discussion completes the paper.

Robust and Highly Secure Technique for Wireless Body Sensor Network Using Sequence of ECG Data

Lately, Random Binary Sequences (RBSs) are being computed using Heartbeat signals acquired from ECG and they are crucial for security purposes in wireless Body Sensor Networks (WBSNs). Presently, 128-bit RBSs in healthcare sector takes long computation time to be generated using ECG based heartbeat signals. To reduce the computation time, a novel technique for generation of RBSs using heartbeat’s Interpulse Intervals (IPI) is proposed in this paper. In this paper, the technique uses generation of monotonic increasing, finite IPI sequences and encoding process using cyclic block to extract entropic bits in high numbers from each of the IPI. The dataset used for this paper were taken from Physionet Arrhythmia database. Using the proposed method, around 16 bits can randomly be extracted from each ECG heartbeat signal In order to produce RBSs of 128 bits by concatenating eight IPIs sequentially. Using the tests given in National Institute of Standards and Technology statistical (NIST) and the hamming distance function, the distinctiveness and randomness of the generated RBSs of 128 bits can be measured. The RBSs of 128 bits that are generated from the results of both patients and healthy subjects can be utilized as encryption keys or identifiers in order to protect the WBSNs. The method proposed has been observed to be better than the existing methods by being four times faster.