Health Monitoring for Elderly: An Application Using Case-Based Reasoning and Cluster Analysis

This paper presents a framework to process and analyze data from a pulse oximeter which remotely measures pulse rate and blood oxygen saturation from a set of individuals. Using case-based reasoning (CBR) as the backbone to the framework, records are analyzed and categorized according to their similarity. Record collection has been performed using a personalized health profiling approach in which participants wore a pulse oximeter sensor for a fixed period of time and performed specific activities for pre-determined intervals. Using a variety of feature extraction methods in time, frequency, and time-frequency domains, as well as data processing techniques, the data is fed into a CBR system which retrieves most similar cases and generates an alarm according to the case outcomes. The system has been compared with an expert's classification, and a 90% match is achieved between the expert's and CBR classification. Again, considering the clustered measurements, the CBR approach classifies 93% correctly both for the pulse rate and oxygen saturation. Along with the proposed methodology, this paper provides a basis for which the system can be used in the analysis of continuous health monitoring and can be used as a suitable method in home/remote monitoring systems. 1. Introduction Today, the possibility to remotely monitor physiological health parameters provides a new approach for disease prevention and early detection [1, 2]. Furthermore, such health monitoring systems could be useful for the elderly in independent and assisted living [3]. In developing health monitoring systems, several intelligent data processing methods have been proposed in the literature, for instance, neural network (NN) [4] and support vector machine (SVM) [5]. These methods are often black box methods and make it difficult for experts to gain further insight into the structure presented in the data. In this paper, a clinical decision support system (CDSS) has been proposed where case-based reasoning (CBR) approach [6] is applied to analyze and process the data coming from a pulse oximeter that contain measurements of both pulse rate and blood oxygen saturation. A case-based reasoning (CBR) [6–17] approach can work in a way close to human reasoning, for example, it solves a new problem applying previous experiences, which is more common for doctors, clinicians, or engineers. In the proposed system, CBR is the part of a large framework where first the data is preprocessed and features are extracted to find significant parameters of interest using time, frequency, and time-frequency