Fuzzy Linguistic Protoforms to Summarize Heart Rate Streams of Patients with Ischemic Heart Disease

Abstract

Cardiac rehabilitation is a key program which significantly decreases mortality rates in high-risk patients with ischemic heart disease. Due to the huge lack of accessibility to such programs at Health Centers, outdoor-based programs for cardiac rehabilitation have been proposed as an excellent tool to improve accessibility for patients at Health Centers. These outdoor-based programs make use of wrist-worn devices for real-time monitoring of rehabilitation sessions based on clinical guidelines. In this way, a greater number of patients can fortunately gain access to the rehabilitation program. However, this advantage also means that the cardiac rehabilitation team has to monitor a greater number of sessions due to the increase of the number of benefited patients, so the team members spend a lot of time analyzing each patient’s sessions. In this paper, we present a methodology to evaluate heart rate streams of patients with ischemic heart disease using a linguistic approach. This innovative methodology manages relevant linguistic descriptions (protoforms) for the cardiac rehabilitation team to identify sessions with interest indicators by means of linguistic summaries. Therefore, the analysis process is automated in a comprehensible way, offering short linguistic descriptions to the cardiac rehabilitation team, who promptly provide feedback to their patients. In order to show the great efficiency and effectiveness of the proposed methodology, we have used and applied this methodology to real data provided by patients of an outdoor cardiac rehabilitation program run by the Health Council of the Andalusian Health Service (Spain).

Extracting an Optimal Set of Linguistic Summaries using Genetic Algorithm Combined with Greedy Strategy

“…The fitness function Fit of each individual that represents a set of linguistic summaries is a weighted aggregate measure of two measure: the goodness of the linguistic summary Gd in formula (7), and the diversity of the set of linguistic summaries De in formula (8). The value of fitness function Fit is a value in the interval [0, 1] calculated by formula (11). The best individual in the last generation is selected as the solution to the problem when that individual has the greatest value of Fit.…”

“…As analyzed in Section III.2, these linguistic summaries tend to increase the fitness function values of the individuals. That is, the results of the proposed algorithm Greedy-GA will give a set of better linguistic summaries according to the adaptability evaluated by the fitness function Fit as in formula (11).…”

“…The selection rate is 0.15, and the mutation rate is 0.1. The fitness function Fit evaluates each individual in equation (11) with parameter values = 0.7, = 0.3.…”

“…The form of knowledge represented in sentences in natural language is easy to understand for every human user. We study the structures of the LS used in most studies on linguistic data summarization, which are the sentences with linguistic quantifier proposed by Yager [2]: "Q y are S" or "Q F y are S" [2][3][4][5][6][7][8][9][10][11][12][13][14]. For example, "Very few (Q) sales of printers (y) is with high commission (S)" [10], "Most (Q) hospitals (y) with very high average hospital stay (F) have very low computer (S)" [8].…”

Personalized linguistic summaries in smartphone-based monitoring of bipolar disorder patients

“…Linguistic summaries have been proven successful in various healthcare-related context, e.g., in [25,6,1,23]. For example, in [25], Wilbik et al focus on potential changes in time and introduce modication of summaries into the linguistic medoid prototypes, that may be exemplied with the following sentence: Mrs. Wilbewski is much better this week than she was last week.…”