Automatic QRS Complex Detection Algorithm Designed for a Novel Electrocardiogram Recording Device

Co-authorsKenneth Egstrup, OUH Svendborg HospitalJens Branebjerg, DELTAGunnar Bjarne Andersen, DELTAHelge B. D. Sørensen, Technical University of Denmark

Dorthe Bodholt Nielsen, Ph.D. student, DELTA / Technical University of DenmarkContact: dbn@delta.dk

Agenda

Application Example: Atrial Fibrillation

Advantages of our wireless ePatch technology

Algorithm: Automatic QRS complex detection

Detection Results

Conclusions and Future Work

The Heart and ECG Signals

Reference: http://elf.cs.pub.ro/pm/wiki/eestec/3

Atrial Fibrillation (AF)

Definition:

Irregular and very fast activation of the atria

Irregular and fast pulse (ventricular contractions)

Prevalence:

1 – 2 % of the general population

The prevalence increases with age:

5 – 15 % at the age of 80 years

Progression of disease:

Paroxysmal → persistent → permanent

Symptoms

Palpitations (“hjertebanken”)

Dyspnoea

No symptoms

Atrial Fibrillation

Adverse clinical events

heart failure

Death rate is doubled

Risk of stroke is 5-fold compared to general population

Treatment of AF

Stroke prophylaxis with anticoagulation therapy

Importance of early detection of AF

It is very important to diagnose patients with AF early to start anticoagulation

treatment and decrease stroke risk.

Asymptomatic patients: Screening for AF in the general population or

high risk groups.

Paroxysmal AF: Very long term monitoring might be needed to find an

episode of AF and diagnose the patient.

Advantages of the ePatch Heart Monitor

The ePatch heart monitor Traditional HOLTER monitor

http://flightphysical.com/Exam-Guide/CV/Holter-Monitor.htm

Automatic AF Detection

Embedded implementation of automatic signal processing

algorithms for detection of cardiac arrhythmias, like atrial fibrillation.

Hardware implementation of automatic ECG arrhythmia

detection algorithms

Atrial Fibrillation in ECG Signals

Definition of AF in ECG signals

Surface ECG shows irregular RR intervals

Surface ECG shows no distinct P waves

The interval between two atrial activations is usually variable and <200ms

Example of AF recorded with the ePatch heart monitor:

Example of normal ECG recorded with the ePatch heart monitor:

Step I: Detection of Heart Beats

Automatic AF detection algorithms in the literature have three

different approaches for automatic AF detection:

Detection based on the irregular RR intervals

Detection based on the absence of P-waves

Detection based on both irregular RR intervals and absence of P-waves

In order to apply either of these, it is necessary to design an

automatic QRS complex detection algorithm.

Automatic QRS Complex Detection

Schematic illustration of the designed automatic QRS complex

detection algorithm:

Automatic QRS Complex DetectionRaw ECG, Lead I

Feature I, Lead I

Adaptive thresholding, Feature I, Lead I

Binary feature signal, Feature I, Lead I

Final QRS position

Databases

The ePatch database:

30 minute records from 11 different patients

Manual annotation of more than 22,000 heart beats

The MIT-BIH Arrhythmia Database (standard database)

30 minute records from 48 different patients

Manual annotation of more than 91,000 heart beats

QRS Detection Results – ePatch database

Performance measures:

Sensitivity = TP/(TP + FN)

Positive predictivity = TP/(TP + FP)

QRS detection performance:

All abnormal beats were correctly detected by the algorithm

# of patients Sensitivity Positive predictivity

11 99.57 % 99.57 %

9 99.95 % 99.92 %

QRS Detection Results – Standard Database

Detection results compared to other studies using a 2 channel

approach to automatic QRS complex detection:

Study Sensitivity Positive predictivity

This work 99.63 % 99.63 %

Ghaffari et al. 99.94 % 99.91 %

Boqiang et al. 99.91% 99.93 %

Chiarugi et al. 99.76 % 99.81 %

Conclusions and Future Work

Promising performance:

The algorithm should, of course, be evaluated on a larger ePatch database

This algorithm might be applied to initiate different arrhythmia detection

algorithms that rely on the detection of heart beats.

Our current work is to design new algorithms for automatic detection of critical

heart arrhythmias, like atrial fibrillation.

Thank you for listening...

Questions and comments are very welcome!

Contact: dbn@delta.dk