Review of the mobile ECG systems worldwide.

Jaroslav Tuhársky KKUI TUKE

Carpathian Virtual Institute for Research and InnovationSummer school 2007 Miskolctapolca

Motivation

Cardiovascular Diseases contribute 30% of total deaths in world.Every 34 seconds, a person dies from Heart Disease in the US. Speed is the essential key to limiting damage to the heart. Online ECG monitoring.Long distance diagnostic.

What is ECG?

ECG is the graphic recording of electric potentials generated by the heart.Mostly used is 12 lead ECG monitoring.12 lead ECG contain:

3 bipolar limb leads – I,II,III3 unipolar limb leads – AVF,AVR,AVL.6 unipolar chest leads – V1 až V6

Let me shortly introduce you few details about ECG

Posterior

Anterior

Limb leads orientation with respect to heart

Chest leads orientation

12 views of the heart

Anatomy of Heart and ECG sig. ECG complex consists of five waveforms labeled with

letters P,Q,R,S,T as shown in figure. ECG is very helpful in the diagnoses of cardiac disease.

Changes in ECG caused by diseases.

Myocardial ischemia – due to lack of adequate blood flow to myocardial.

T-wave peaking Symmetric T-wave inversion ST segment elevation

Arrhythmia – all of irregular beat phases. 2 of them:

Tachycardia – Heart Rate above 100 BPM Bradycardia – Heart Rate below 60 BPM

Myocardial Ischemia ECG

Normal signal ECG with ST segment elevation

ECG with T-wave inversion ECG with peak T-wave

Arrhythmia

Normal ECG signal

Bradycardia ECG signal

Tachycardia ECG signal

What is mobile ECG?

Monitoring ECG signal with mobile ECG device.

Philips ViaPac

What is mobile ECG?

Mobile ECG device - Philips ViaPac

Philips Telemedicine

Is a joint venture between Philips and SHL TeleMedicine Izrael.Provide telemedicine services to cardiology patients across Europe (Italy, Switzerland and Germany. For monitoring uses mobile ECG devices:

ViaPac 12-lead ECG monitor and transmitterOutPac single-lead ECG monitor and transmt.

How does it work? Each patient has a ECG transmitter that canTransmit a ECG signal down a telephone line.Patients worried by chest paint or othersymptoms can call the Monitor Center. The medical professional then accesses thethe patient’s medical records and asksquestions about the symptoms. Then patientmay then be asked to send an ECG. This is transmitted by the ViaPac or, if the patient isaway from home, by the OutPac.

Philips Telemedicine

How could it be?Computational Intelligence helpingspecialists to analyse ECG for diagnosing heart diseases, and to predict risk of cardiovascular event.

How can we do it?Detecting arrhythmias with ANN classifier Predicting the risk of cardiovascular

event by computational intelligence.

ANN classifier detects arrhythmias.

Why ANN?

Correct classification of heart beats is fundamental to ECG monitoring systems such as an intensive care, home care etc.

ANNs can detect patterns and make distinctions between different patterns that may not be apparent to human analysis.

ANN classifier detects arrhythmias.

To date, several researchers have made attempts to use ANN to classify ECG beats.

Here are 2 main approaches of them:In first approach they trained ANN to classify 10 different arrhythmias in it’s classes. In second they developed ANN that can do just binary classification.

ANN classifier – 1.approach Type of ANN

FF NN with BP of errorArchitecture of the ANN is 200-15-10

Input layer has 200 neuronsOne hidden layer has 15 neuronsOutput layer has 10 neurons

Training Data for ANN consists 10 types of arrhythmias selected from MIT-BIH which is a big DB of ECG recordings on internet.

ANN classifies ECG to 10 different classes of arrhythmias.

ANN classifier – 1.approach

ANN Architecture

N- Normal sinus rhythmBr- Sinus bradycardiaVT- Ventricular tachycardiaSA- Sinus arrhythmiaAPC- Atrial premat.contractionP- Paced beatR- Right bundle branch blockL- Left bundle branch blockA.Fib.- Atrial fibrillationA.Fl.- Atrial flutter

Each neuron in output layer represents one class of arrhythmias.

ANN classifier – 1.approach

Block diagram of ECG recording system

ANN classifier – 1.approachThe ECGWin Software

Includes real-time monitoring and recording, filtering, finding the R peaks, ANNs, displaying outputs and storing patient info.

Predicting the risk of cardiovascular events.

Heart attack StrokeCardiovascular death

Predicting the risk of cardiovascular events.

The European EPI-Medics project has designed intelligent Personal ECG Monitor (PEM).PEM is capable of recording a 3-lead ECG, to detect arrhythmias and ischemia or heart attack by ANN, and to send an alarm message with a copy of the patient’s Electronic Health Record (EHR) and the concerned ECG to the relevant health care professionals.

Predicting the risk of cardiovascular events.

Their approach consists separately the ECG classifier and the risk predictor, then merging them into one final decision using empirical rules.As the ECG interpretation module is used:

ANN classifier.As the Risk factors estimation module:

Bayesian network BN.Fuzzy-logic based layer controls dialog between both modules making final decision

Predicting the risk of cardiovascular events.

They build up an ANN ensemble that consists of a committee of 50 ANN.Architecture of each individual ANN is 15-10-1.

Input layer with 15 neurons – one for each predictive variable.One hidden layer with 10 neurons.Output layer with 1 neuron to predict event. It’s values are in interval between 0 and 1.

Predicting the risk of cardiovascular events.

ANN architecture

Predicting the risk of cardiovascular events.

The risk factors used to train the Bayesian Network, the ANN and the the Logistic Regression model.

Age, Body Mass Index BMI, Cholesterol, Blood Glucose, Sex, Old Left Ventricular Hypertrophy, Old Myocardial Infarction, Old Stroke, Diabetes, History of Hypertension Treatment, Smoking, Symbolic BP, Diasymbolic BP.

Predicting the risk of cardiovascular events.

Architecture of the Bayesian network used to estimate the risk

The decision making model for detecting ischemia

Conclusion

Recent years have witnessed a growing interest in developing personalized and non-hospital based care systems to improve the management of cardiac care.

The design of low-cost, high-performance, simple to use, and portable equipment for ECG signal monitoring, that offers a combination of diagnostic features, seems to be a goal that is highly worthwhile.

References

Nishant Chandra, Mrigen Negi, Meru A Patil. Neural Networks in ECG classification. A.Mampuya. Telemedicine for the heart.Yüksel Özbay, Bekir Karlik. Recognition of ECG arrhythmias using ANNs, 2001.H Atoui, J Fayn, F Gueyffier, P Rubel. Cardiovascular Risk Stratification in DecisionSupport Systems: A Probabilistic Approach. Application to pHealth