Classifying Normal Classifying Normal and Abnormal and Abnormal

Heartbeats From a Heartbeats From a Noisy ECGNoisy ECG

Eric PetersonEric Peterson

ECE 539ECE 539

OutlineOutline

Filtering – Some BasicsFiltering – Some Basics Beat Detection – FailedBeat Detection – Failed MLP Beat Classification – Works…MLP Beat Classification – Works…

SometimesSometimes SVM Beat Classification – Similar SVM Beat Classification – Similar

ResultsResults Conclusion – More Pre-Processing Conclusion – More Pre-Processing

NeededNeeded

Filtering – High-PassFiltering – High-Pass

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16-80

-70

-60

-50

-40

-30

-20

-10

0

10

Frequency (kHz)

Mag

nitu

de (

dB)

Magnitude Response (dB)

Filtering – Band-PassFiltering – Band-Pass

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16-120

-100

-80

-60

-40

-20

0

Frequency (kHz)

Mag

nitu

de (

dB)

Magnitude Response (dB)

Beat DetectionBeat Detection

Supplied the Filtered SignalSupplied the Filtered Signal Overwhelmed the ANNOverwhelmed the ANN SNR does not matterSNR does not matter FAILURE!!!FAILURE!!!

Pan-TompkinsPan-Tompkins Overwhelmed againOverwhelmed again May not actually be linearly seperableMay not actually be linearly seperable

Modifications requredModifications requred

MLP Beat ClassificationMLP Beat Classification

Used annotations to focus on beats onlyUsed annotations to focus on beats only Annotations of either normal or Annotations of either normal or

abnormal beatsabnormal beats Attempted many parameter variationsAttempted many parameter variations

Best classification rate: 95.8824%Best classification rate: 95.8824% Confusion Matrix: 159Confusion Matrix: 159 22

88 44 Results were dominated by the normal beatsResults were dominated by the normal beats

Failed with a SNR<24dBFailed with a SNR<24dB

MLP Beat ClassificationMLP Beat ClassificationInputs Learning Rate Momentum Hidden Layers Classification Rate Confusion Matrix

2 0.001 1 2 95.8824 159 2 BEST8 4

2 0.01 0.001 2 95.2941 159 17 3

2 0.01 0.01 2 95.2941 159 17 3

2 0.01 0.1 2 95.2941 159 17 3

2 0.01 1 2 95.2941 159 27 3

2 0.1 0.5 3 95.2941 159 17 3

10 0.1 0.5 7 95.8824 160 0 BEST7 3

50 0.01 0.5 5 95.2941 160 08 2

SVM Beat ClassificationSVM Beat Classification

RBF kernel did not RBF kernel did not workwork

Similar results to Similar results to MLPMLP

Still seems dominated Still seems dominated by the normal beatsby the normal beats

Failed at <24dB SNRFailed at <24dB SNR

Inputs Kernel Type Accuracy Confusion Matrix2 Polynomial 93.53% 158 2

9 1

5 Polynomial 92.94% 155 57 3

10 Sigmoid 94.71% 156 45 5

50 Sigmoid 93.53% 158 29 1

SVM Beat ClassificationSVM Beat Classification

ConclusionConclusion

More Pre-Processing is needed!!!More Pre-Processing is needed!!! Possibility of better filtering?Possibility of better filtering? Further analysis of the signalFurther analysis of the signal

Feed the neural nets with important valuesFeed the neural nets with important values

Templates were used in many Templates were used in many previous papersprevious papers Not ideal for many types of abnormal Not ideal for many types of abnormal

beatsbeats

Questions?Questions?

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