Hadi Banaee

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

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 experts classification, and a 90% match is achieved between the experts 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.

A Framework for Automatic Text Generation of Trends in Physiological Time Series Data

Health monitoring systems using wearable sensors have rapidly grown in the biomedical community. The main challenges in physiological data monitoring are to analyse large volumes of health measurements and to represent the acquired information. Natural language generation is an effective method to create summaries for both clinicians and patients as it can describe useful information extracted from sensor data in textual format. This paper presents a framework of a natural language generation system that provides a text-based representation of the extracted numeric information from physiological sensor signals. More specifically, a new partial trend detection algorithm is introduced to capture the particular changes and events of health parameters. The extracted information is then represented considering linguistic characterisation of numeric features. Experimental analysis was performed using a wearable sensor and demonstrates a possible output in natural language text.

Data-Driven Rule Mining and Representation of Temporal Patterns in Physiological Sensor Data

Mining and representation of qualitative patterns is a growing field in sensor data analytics. This paper leverages from rule mining techniques to extract and represent temporal relation of prototypical patterns in clinical data streams. The approach is fully data-driven, where the temporal rules are mined from physiological time series such as heart rate, respiration rate, and blood pressure. To validate the rules, a novel similarity method is introduced, that compares the similarity between rule sets. An additional aspect of the proposed approach has been to utilize natural language generation techniques to represent the temporal relations between patterns. In this study, the sensor data in the MIMIC online database was used for evaluation, in which the mined temporal rules as they relate to various clinical conditions (respiratory failure, angina, sepsis, ...) were made explicit as a textual representation. Furthermore, it was shown that the extracted rule set for any particular clinical condition was distinct from other clinical conditions.

Intelligent Healthcare Services to Support Health Monitoring of Elderly

This paper proposed an approach of intelligent healthcare services to support health monitoring of old people through the project named SAAPHO. Here, definition and architecture of the proposed hea ...

Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data

This paper presents an approach to automatically mine rules in time series data representing physiological parameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined for each physiological time series data. The generated rules based on the prototypical patterns are then described in a textual representation which captures trends in each physiological parameter and their relation to the other physiological data. In this paper, a method for measuring similarity of rule sets is introduced in order to validate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classes in the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rule mining technique is able to acquire a distinctive model for each clinical condition, and represent the generated rules in a human understandable textual representation.

A new DP algorithm for comparing gene expression data using geometric similarity

Microarray gene expression data comes as a time series, where the expression level of a gene is recorded at specific time points. Comparing the time series produced by two genes can give us information about the regulatory or inhibitory relationship between the genes. We present a Dynamic Programming (DP) method to compare gene expression data using geometric similarity. We aim to detect similarities and relationships between genes, based on their expression time series. By representing the time series as polygons and compare them, we can find relationships that are not available when the two time series are compared point-by-point. We applied our algorithm on a dataset of 343 regulatory pairs from the alpha dataset and compared them to randomly generated pairs. Using an SVM classifier, we find the optimal similarity score that separates the regulatory dataset from the random pairs. Our results show that we can detect similar pairs better than simple Pearson correlation and we outperform many of the existing methods. This method is an ongoing approach, that can be applied to finding the similarity of any data that can convert to 2D polygon. In the future, we plan to introduce this method as a new classifier.

Using Conceptual Spaces to Model Domain Knowledge in Data-to-Text Systems

This position paper introduces the utilityof the conceptual spaces theory to conceptualisethe acquired knowledge in data-totextsystems. A use case of the proposedmethod is presented for text genera ...