An Intelligent System for Classification of Patients

MobiHealth 2010 International ICST Conference on Wireless Mobile Communication and Healthcare

An Intelligent System for Classification of Patients Suffering from Chronic Diseases Christos Bellos1, Athanasios Papadopoulos1, Dimitrios I. Fotiadis2, and Roberto Rosso3 1Foundation

for Research and Technology - Hellas, Biomedical Research, Ioannina Greece 2Dept. of Materials Science and Engineering, University of Ioannina, Greece 3TESAN Telematic & Biomedical Services S.p.A., Vicenza Italy

Contents • Necessity and Outcome of the System • State of the Art • Introduction to CHRONIOUS System • Main System Components – Pre-processing and Feature Extraction – Patient Profiler – Mental Support Tool – Decision Support System – Severity Estimation and Alerting

• Conclusions • Future Work

Necessity of the System • Need to perform all medical procedures and monitor chronically ill people remotely in the most efficient way according to the most updated international guidelines (in our case for COPD and kidney diseased patients) • Clinicians need to: – check the patient profile related information (history , latest measurements, prescriptions, appointments) – prescribe a patient plan: diet, drugs, exercises, lab tests and questionnaires) – set the patients’ alarms – query to the most updated medical knowledge

Outcome of the System Patients

• Lifestyle improvement • Active participation in monitoring and decision-making process • Helps patients to control their mental status

Healthcare Providers

• Improvement of the prognosis for the chronic diseases • Selection of the most appropriate treatment planning • New guidelines for patient remote monitoring

Healthcare Institutes

• Reduction of the number and duration of hospitalization • Increase of the home care assistance

State of the Art 1/2 • Several Chronic Disease Management tools are commercially available or supported by various healthcare services – Mainly home care systems – Very few of them provide wearable and mobile solutions for outdoors monitoring and disease management

• Very few of them, integrate decision support tools and alerting mechanisms for caregivers • Only a few of them are accompanied with patient and health professional interfaces for communication and education purposes

State of the Art 2/2 Systems utilizing similar technology based on the analysis vital and motion signals

Research projects utilizing techniques based on the development of Wireless Personal Area Network (WPAN)

MIThril (MIT Media Lab)1

Mobihealth5

CodeBlue (Harvard University)2

SAPHIRE6

Medical Embedded Device for Individualized Care (MEDIC)3

MyHeart7

Lifeguard system4

HEARTS8 U-R-SAFE9 Mobi-Dev10

[1] S. Pentland, IEEE Comp 37(5), 34-41, 2004 [2] K.Lorinz et al, IEEE Perv Comp 3(4) 16-23, 2004 [3] H. Winston et al, Artif Intel Med, 42, 137-152, 2008 [4] K. Montgomery et al, California EMBC 04, 240, 2004

[5] A. Van Halteren et al, Studies in Health Technology and Informatics, N.108, pp 181-193 IOS Press, 2004 [6] O. Nee, et al, IEE Proc CornSpecial issue on Telemedicine and e-Health Communication Systems, June 2007 [7] R.Paradiso, 28th IEEE EMBS Annual International Conference New York City, USA, 2006 [8] O. Faschimbecher, J. Epidemiol Comm Health, 56, 2002 [9] P. Rumeau et al, Int Assoc Geront Con Rio, June, 2005 [10] Mobi-Dev, 1st Inter Symp on Mobile Devices for Healthcare, Frascati, Italy, May, 2003

External Devices Spirometer Home Patient Monitor

Administrator

Glucometer Clinician HPM DB Central DB Blood Pressure

Internet Laboratory PDA DB

PDA

Weight Scale

Wearable

Env. Monitor

• Consists of a tight-fitting and washable shirt which provides the support for the stabilization of the sensor network • A data handler device is placed at the lower part of the shirt – accurate collection and management of the wireless (BT) transmission of the signals

• Categories of Sensors – – – – – –

Motion system – 3axis accelerometer ECG sensor (3 channels) Respiratory sensor (2 respiratory bands) Pulse Oximeter Temperature (Body and Ambient), Humidity Audio Sensor (Microphone)

Accelerometer Body Temperature sensor Ambient Temperature & Humidity Sensor

Pulse Oximeter sensor ECG electrodes Respiration bend (T)

Audio Sensor (Microphone)

Respiration band (A)

• Placed at Smart Assistant Device Consists of:

Aims to:

Feature Extraction

Identify and classify abnormal health events

Heterogeneous Data Fusion

Detect and evaluate a stressful situation

Decision Support System (DSS)

Provide alerts and/or advices to end-users

Mental Support Tool

Transmit data to Central system for further analysis

Severity Estimation and Alerting

Other Data Sources

Wearable Sensors

Pre-processing and Feature extraction

Mental Support Tool


Alerting Mechanism

Data transmission to Central system for further analysis

Alarms & Displays

Data Fusion

Pre-processing and Feature extraction

Sensor Data / Data Handler

Patient Inputted data (food, drug, activity)

Data from External Devices

Heterogeneous Data fusion Graphical User Interface

Patient Inputted Data Analyzer

Home Patient Monitor

Patient Profiler Output

Pre-processing and Feature Extraction • 2D signals collected from jacket’s sensors Electrocardiogram (ECG)

Respiratory Signals

Acceleration Signals

Pre-processing and Feature Extraction - ECG Preprocessing steps

Electrocardiogram Respiration Accelerometer

Related Activity

Removal of low frequency base line wandering (BW)

Linear filtering and Polynomial fitting techniques are employed

Removal of high frequency noise

The Daubechies (DB4) wavelet is employed

QRS Detection

1. Filtering the ECG signal with continuous (CWT) and fast wavelet transforms (FWT)1 2. Papaloukas et al. [2]

[1] YC Chesnokov, DN. Computers in Cardiology, 2006. [2] C. Papaloukas, D.I. Fotiadis, A. Likas, and L.K. Michalis, Journal of Electrocardiology, 2002.

Pre-processing and Feature Extraction – ECG Features Time domain features1 SDNN (msec): Standard deviation of all normal RR intervals in the entire ECG recording

SDANN (msec): Standard deviation of the mean of the normal RR intervals for each 5 minutes period of the ECG recording SDNNIDX (msec): Mean of the standard deviations of all normal RR intervals for all 5 minutes segments of the ECG recording pNN50 (%): Percent of differences between adjacent normal RR intervals that are greater than 50 msec r-MSSD (msec): Square root of the mean of the sum of the squares of differences between adjacent normal RR intervals over the entire ECG recording

[1] M. Bolanos, H. Nazeran, E. Haltiwanger, Engineering in Medicine and Biology Society, 2006.


Pre-processing and Feature Extraction – ECG Features


Frequency domain features The Low – Frequency band (LF) which includes frequencies in the area [0.03 – 0.15] Hz The High – Frequency band (HF) which includes frequencies in the area [0.15 – 0.40] Hz

Pre-processing and Feature Extraction – Respiration


Respiration features Respiration Rate : The number of breaths per minute Tidal Volume (VT) : The normal volume of the air inhaled after an exhalation Vital capacity (VC) : The volume of a full expiration. This metric depends on the size of the lungs, elasticity, integrity of the airways and other parameters, therefore it is highly variable between subjects Residual volume (VR) : The volume that remains in the lungs following maximum exhalation

Pre-processing and Feature Extraction – Accelerometer


Features

Methodology

Detect the postures of the patient

Posture detection is done by summing up 100 samples and analyzing them to find the highest value regarding the axes x, y or z

Count the number of steps in order to estimate the calorie burning rate and the effect of the exercise program

To recognize steps, peaks over a certain threshold are searched

Detect falling (tumbling), in order to trace the health status

To detect falls the difference between two consequent samples is measured and in case they exceed a limit, the event is characterized as ‘fall’

Feature Extraction Testing Environment

Data Collection Protocol Step 1: Healthy volunteers were recruited Step 2: Calibration procedure of the sensors (5 min)

Step 3: Validation against gold standard devices

Step 4: Recording of the collected signals at the several postures

• Standing • Lying • Falling down • Lay down slowly • Walking

Step 5 Annotation of the Dataset performed by experts

Data Collection • The test activities of the first phase will be done in Italy involving the main patients category which will take advantage of the highest benefits foreseen in terms of management of scientific and technical result Careggi Hospital Application field: COPD Patients involved: 40 patients ULSS 2 - Feltre Application field: KD Patients involved: 10 patients at hospital, 20 patients at home

Patient Profiler - Scope • This component enhances the personalization of the Clinical Decision Support System (CDSS) • Aims at identifying representative profiles of patients by clustering technique • Is a disease-specific web service which forms a vector containing the centroids of the cluster that the patient belongs to • The output vector enters the CDSS with an enhanced weight affecting the final decision.

Central DB PDA DB

Vector of the Profile Heart Rate

68

Steps

250

…

…

Activity Calories

122

Heterogeneous Data fusion


Some Indicative Attributes Several attributes have been identified as significant for characterizing the patients Attribute

Data Type

Attribute

Data Type

Day Period

String

Steps

Number (steps)

Heart Rate (HR)

Number (HR)

Falls

Number (events)

SpO2

Number (SpO2)

Systolic BP

Number

Humidity

Percent (%Humidity)

Diastolic BP

Number

Ambient Temperature

Number (oC)

Drug Condition

String

Body Temperature

Number (oC)

Total Calories

Number

Respiration Rate

Number

Total Lipids

Number (fat intake)

Lying

Number (minutes)

Activity Calories

Number

Standing

Number (minutes)

Activity Comments

String

Patient Profiler – Implementation (1/2) • Several clustering algorithms have been developed and tested for their efficiency • Clinicians have checked and assessed the output vector of each cluster for each clustering method • The output of the k-Means algorithm with five prespecified clusters has been chosen by the clinicians as the appropriate for the specific attributes and the testing dataset

Patient Profiler – Implementation (2/2) Algorithm EM Clustering Algorithm1 K-Means2 K-Means2

Hierarchical Clusterer3

Number of Clusters 4

2 (Prespecified) 5 (Prespecified)

5 (Prespecified)

[1] N. Mustapha, et al, European Journal of Scientific Research, 2009. [2] C.M. Bishop. Oxford, England: Oxford University Press, 1995. [3] Romesburg, H. Clarles, Cluster Analysis for Researchers, Pub. Co., 2004.

Clustered Instances 0

18

1

3

2

5

3

7

0

30

1

3

0

13

1

2

2

2

3

11

4

5

0

6

1

4

3

1

4

1

Mental Support Tool - Objectives • Detect a stressful situation by combining various parameters stored in PDA Database • Evaluate the identified situation by estimating a Stress Index and provide a respective advice to the patient • Evaluating the patient’s adherence to the system’s scheduled activities and recommendations • Keep patient’s moral high and motivating him/her to assist the disease management system • Provide tips once a day with short messages of clinical knowledge

Mental Support Tool – Block Diagram Patient Inputted data (food, drug, activity)

Data Fusion

Mental Support Tool

Sensor Data / Data Handler

Stress Evaluator

Alarm Viewer (GUI)

Implementation Steps

Attributes processing and Data Fusion

Constructing the Bayesian Network

Learning the Bayesian Network

Implementation

Learning will be achieved through: • Available data • Expert’s knowledge • Combination of both

Validation

Attributes processing and Data Fusion Specification of the attributes

• The attributes that affect the stress index and their weights have been specified based on the clinicians’ feedback • The specified parameters have been grouped to causes and symptoms of stress • Further processing of some attributes has been performed and meta-features have been extracted

Indication of the independent probability of causing stress Calculation of the transition matrix

• Further relations between causes and symptoms have been specified linking the attributes and combining their probability of stress presence

Indicative Attributes and Probabilities Attribute

Attribute Categorization

Smoking Environmental Noise and rowded/Noisy places

Causes Hypoglycaemia

Symptoms

Heart Rate Skin Temperature Breathing asynchrony Sleep Disturbances (Questionnaires) Mood (Questionnaires)

Activity Comments

Different States

Probability of causing Stress (%)

YES

90

NO

10

High

70

Medium Low YES NO High Normal Cool

25 5 85 15 85 15 35

Sweat YES NO YES NO Better Same Worst Feel sick, nausea Exhaustion, fatigue Discomfort in the chest, upper body, or jaw Irregular or extremely rapid heart beats None

65 90 10 62.5 37.5 5 25 70 18 23 23 28 8

Constructing the Bayesian Network • The Bayesian network has been designed.

WP 8

Learning the Bayesian Network • To this point both structure and parameters are defined based on expert’s knowledge • However is difficult for experts to quantify knowledge • It is important to update the model using real data1 – Data are also required for validation of the model – The more the available data, the better the quality of the resulting model

• Missing data can occur due to many reasons: – Sensor’s failure – Expert’s uncertainty on annotation

• There is a variety of methods for learning from missing data and have been applied (Antal et al. [2], Fengzhan et al. [3], Liao et al. [4])

[1] W. Liao and Q. Ji, Pattern Recognition, 2009. [2] P. Antal, et al., Artificial Intelligence in Medicine, 2004. [3] T. Fengzhan, et al, ICDM 2003. Third IEEE International Conference on Data Mining, 2003. [4] W. Liao and Q. Ji, Pattern Recognition, 2009.

Implementation • The specified parameters have been grouped to causes and symptoms of stress • The probability of causing stress by the presence or the absence of a specific state of each parameter has been specified. • We have implemented the Bayesian networks engine for inference and parameter learning: – – – –

The engine is based on the Junction Tree algorithm1. Implemented in C++. A C# wrapper is used to invoke the engine from the User Interface (UI). Developed for desktop and mobile applications and tested on Windows XP and Windows Mobile platforms.

[1] C. Huang and A. Darwiche, International Journal of Approximate Reasoning, 1996.

Mental Tool – Testing Environment

Validation Protocol Patients’ actions: Step 1: Patients wear the jacket during the setup phase (1 week collection of data) Step 2: Through validated questionnaires report stressful situations during the day Step 3: Classify the severity of the stressful episodes judging by experience

Clinicians’ actions: Step 1: Clinicians monitor the patient during the initial collection of data Step 2: Identify possible stressful episodes through clinical data and sensors’ values examination Step 3: Classify the severity of the stressful episodes

Decision Support System – Technical Roadmap Contains two classifiers running in parallel: – Expert system: The decision gives an explanation of the severity assessment • Rule based1

– Supervised Classification System: Detects and evaluates the episodes • Support Vector Machines2 • Random Forest3 • Multi-Layer Perceptron4

• Decision Tree5 • Naïve Bayes6 • PARtial decision Trees7

[1] A. Giacometti, E.K. Miyaneh, P. Marcel, A. Soulet, A Generic Framework for Rule-Based Classication [2] Cristianini N, Shawe-Taylor J. USA: Cambridge University Press; 2000 [3] L. Brieman. Machine Learning Journal, 45, pp. 5-32,2001 [4] Haykin, Simon. Prentice Hall, 1998 [5] J48, R. Quinlan, CA: Morgan Kauffman, 1993 [6] N. Friedman, D. Geiger, M. Goldszmidt, Machine Learning, 1997 [7] Eibe Frank , Ian H. Witten, Proceedings of the Fifteenth International Conference on Machine Learning, 1998

Severity Estimation and Alerting Level 1

Severity Estimation

Severity Level

No Action

Level 2

Level 3, 4, 5

DSS

Pop-up on PDA (simple advice that depends on patients profile)

Call Patient

Patient

Alarm Manager SMS Transfer Method

Contact User

E-Mail

Clinician GUI Clinician

Healthcare Assistant

24-h standby personnel

Conclusions • The project results to the development of an advanced multi-parametric intelligent system that fuses information effectively from various sources using intelligent techniques • Several features have been successfully extracted from Accelerometer, ECG and Respiration signals – In addition several data are being added through Graphical User Interfaces regarding food intake, drug intake and activity information

• The Decision Support System exploits both a neural network and a rulebased system running in parallel • The CHRONIOUS system achieves personalization of the severity estimation: – several profiles are created, describing with accuracy the health status of the patient, and used as an input to the Intelligence of the System

Future Work • Healthy volunteers and patients suffering from Chronic Obstructive Pulmonary Disease (COPD) and Chronic Kidney Disease (CKD) will be recruited to collect data • The weights of the various fused attributes will be validated and re-calculated • The developed classifiers and the respective algorithms will be re-trained in order to increase the accuracy of the system • The Patient Profiler component will be re-trained in order to conclude to efficient, with clinical meaning and disease-specific profiles