Mobyen Uddin Ahmed

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.

A Case-Based Decision Support System for Individual Stress Diagnosis Using Fuzzy Similarity Matching

Stress diagnosis based on finger temperature (FT) signals is receiving increasing interest in the psycho‐physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret, and analyze complex, lengthy sequential measurements to make a diagnosis and treatment plan. The paper presents a case‐based decision support system to assist clinicians in performing such tasks. Case‐based reasoning (CBR) is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the CBR system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty‐nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness‐of‐fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation that shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with CBR is a valuable approach in domains, where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable, where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho‐physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process.

A multi-module case-based biofeedback system for stress treatment

OBJECTIVE Biofeedback is today a recognized treatment method for a number of physical and psychological problems. Experienced clinicians often achieve good results in these areas and their success largely builds on many years of experience and often thousands of treated patients. Unfortunately many of the areas where biofeedback is used are very complex, e.g. diagnosis and treatment of stress. Less experienced clinicians may even have difficulties to initially classify the patient correctly. Often there are only a few experts available to assist less experienced clinicians. To reduce this problem we propose a computer-assisted biofeedback system helping in classification, parameter setting and biofeedback training. METHODS The decision support system (DSS) analysis finger temperature in time series signal where the derivative of temperature in time is calculated to extract the features. The case-based reasoning (CBR) is used in three modules to classify a patient, estimate parameters and biofeedback. In each and every module the CBR approach retrieves most similar cases by comparing a new finger temperature measurement with previously solved measurements. Three different methods are used to calculate similarity between features, they are: modified distance function, similarity matrix and fuzzy similarity. RESULTS AND CONCLUSION We explore how such a DSS can be designed and validated the approach in the area of stress where the system assists in the classification, parameter setting and finally in the training. In this case study we show that the case based biofeedback system outperforms trainee clinicians based on a case library of cases authorized by an expert.

Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and Case-Based Reasoning

Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare 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.

Case-Based Reasoning for Medical and Industrial Decision Support Systems

The amount of medical and industrial experience and knowledge is rapidly growing and it is increasingly difficult to be up to date leading to less informed decisions, mistakes and serious accidents. The demand for decision support system (DSS) is especially important in domains where experience and knowledge grow rapidly. However, traditional approaches to DSS are not always easy to adapt to a flow of new experience and knowledge and may also show a limitation in areas with a weak domain theory. This chapter explores the functionalities of Case-Based Reasoning (CBR) to facilitate experience reuse both in clinical and in industrial decision making tasks. Examples from the field of stress medicine and condition monitoring in industrial robots are presented here to demonstrate that the same approach assists both clinical applications as well as decision support for engineers. In both domains, DSS deals with sensor signal data and integrate other artificial intelligence techniques into the CBR system to enhance performance in a number of different aspects. Textual information retrieval, Rule-Based Reasoning (RBR), and fuzzy logic are combined together with CBR to offer decision support to clinicians for a more reliable and efficient management of stress. Agent technology and wavelet transformations are applied with CBR to diagnose audible faults on industrial robots and to package such a system. The performance of the CBR systems have been validated and have shown to be useful in solving such problems in both of these domains.

Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning

Increased exposure to stress may cause health problems. An experienced clinician is able to diagnose a persons stress level based on sensor readings. Large individual variations and absence of general rules make it difficult to diagnose stress and the risk of stress-related health problems. A decision support system providing clinicians with a second opinion would be valuable. We propose a novel solution combining case-based reasoning and fuzzy logic along with a calibration phase to diagnose individual stress. During calibration a number of individual parameters are established. The system also considers the feedback from the patient on how well the test was performed. The system uses fuzzy logic to incorporating the imprecise characteristics of the domain. The cases are also used for the individual treatment process and transfer experience between clinicians. The validation of the approach is based on close collaboration with experts and measurements from 24 persons used as reference.

A hybrid case-based system in clinical diagnosis and treatment

Computer-aided decision support systems play an increasingly important role in clinical diagnosis and treatment. However, they are difficult to build for domains where the domain theory is weak and where different experts differ in diagnosis. Stress diagnosis and treatment is an example of such a domain. This paper explores several artificial intelligence methods and techniques and in particular case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic to enable a more reliable diagnosis and treatment of stress. The proposed hybrid case-based approach has been validated by implementing a prototype in close collaboration with leading experts in stress diagnosis. The obtained sensitivity, specificity and overall accuracy compared to an expert are 92%, 86% and 88% respectively.

A Computer Aided System for Post-operative Pain Treatment Combining Knowledge Discovery and Case-Based Reasoning

The quality improvement for individual postoperative-pain treatment is an important issue. This paper presents a computer aided system for physicians in their decision making tasks in post-operative pain treatment. Here, the system combines a Case-Based Reasoning (CBR) approach with knowledge discovery. Knowledge discovery is applied in terms of clustering in order to identify the unusual cases. We applied a two layered case structure for case solutions i.e. the treatment is in the first layer and outcome after treatment (i.e. recovery of the patient) is in the second layer. Moreover, a 2nd order retrieval approach is applied in the CBR retrieval step in order to retrieve the most similar cases. The system enables physicians to make more informed decisions since they are able to explore similar both regular and rare cases of post-operative patients. The two layered case structure is moving the focus from diagnosis to outcome i.e. the recovery of the patient, something a physician is especially interested in, including the risk of complications and side effects.

Mining rare cases in post-operative pain by means of outlier detection

Rare cases are often interesting for health professionals, physicians, researchers and clinicians in order to reuse and disseminate experiences in healthcare. However, mining, i.e. identification of rare cases in electronic patient records, is non-trivial for information technology. This paper investigates a number of well-known clustering algorithms and finally applies a 2nd order clustering approach by combining the Fuzzy C-means algorithm with the Hierarchical one. The approach was used to identify rare cases from 1572 patient cases in the domain of post-operative pain treatment. The results show that the approach enables the identification of rare cases in the domain of post-operative pain treatment and 18% of cases were identified as rare.