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E LECTROCARDIOGRAM C OLLECTION, P ATTERN R ECOGNITION, AND C LASSIFICATION S YSTEM S UPPORTING A M OBILE C ARDIOVASCULAR D ISEASE D ETECTION A ID A Thesis.

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Presentation on theme: "E LECTROCARDIOGRAM C OLLECTION, P ATTERN R ECOGNITION, AND C LASSIFICATION S YSTEM S UPPORTING A M OBILE C ARDIOVASCULAR D ISEASE D ETECTION A ID A Thesis."— Presentation transcript:

1 E LECTROCARDIOGRAM C OLLECTION, P ATTERN R ECOGNITION, AND C LASSIFICATION S YSTEM S UPPORTING A M OBILE C ARDIOVASCULAR D ISEASE D ETECTION A ID A Thesis in Computer Engineering Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science By Patrick R. DaSilva

2 C OMMITTEE Dr. Paul Fortier (Advisor) Dr. Howard Michel Dr. Kristen Sethares Dr. Honggang Wang 2

3 A GENDA Introduction Background Problem Statement Research Questions Research Organizational Questions Development Detection Classification Performance Testing Implementation Live System Testing Conclusion Questions 3

4 I NTRODUCTION 4

5 H EART D ISEASE 5 CDC Division of Vital Statistics Heart disease accounts for 26% of total deaths in 2007 Preliminary 2009 data showed that heart disease was still the main cause of death Patients don’t understand symptoms Associate with other ailments Leads to multiple hospitalizations Costs 34 billion dollars yearly Puts a financial strain on our healthcare system

6 C URRENT N URSING M ETHODS Nurse-led HF clinics Home care nursing Visit frequency varies Depends on patient physical activity ability Still require face-face Patients don’t remember or understand instructions Visitations not real-time do not always happen when they are needed 6

7 S UPPORTED S YSTEM Focus is to support a mobile patient-centric, self- monitoring and self-intervention system with a clinical nursing tool to aid in collaborative patient- clinician chronic disease management 7

8 M OBILE C ARDIAC H EART M ONITOR S ENSOR S UITE 8 Synthetic Sensor Derivations Memory Cardiac Wellness Engine Cardiac Event Monitor Tx Rx Position / Motion FSM Ctrl Temp DSP A/D Conversion SPO2 EKG Analog Conditioning

9 P ROBLEM S TATEMENT Focus on the research, development, design, testing and validation of the electrocardiogram sensor 9 R ESEARCH Q UESTIONS Is it feasible to autonomously detect and classify EKG arrhythmias to assist in a medical situational assessment and health status feedback? Will the system be able to differentiate a normal rhythm from an abnormal one? Will the system be able to perform in or close to real- time?

10 R ESEARCH 10

11 W HAT IS AN EKG? Recorded electrical activity that represents the heart’s conduction system 11

12 H OW IS AN EKG COLLECTED ? 12 3 Lead 5 Lead 12 Lead Limb Leads Lead I, Lead II, Lead III Precordial Leads V1, V5 V2, V3, V4, V6 Augmented Limb Leads aVR, aVL, aVF

13 W HICH C LASSIFICATIONS ? 13 Normal EKG (LII, V1) Sinus Bradycardia (LII) Sinus Tachycardia (LII) Supraventricular Tachycardia (LII, V1) Sinus Bradycardia (LII) Sinus Tachycardia (LII) Supraventricular Tachycardia (LII, V1) Atrial Fibrillation (LII) Atrial Flutter (LII) First-Degree AV Block (LII) Type 1 Second-Degree AV Block (LII) Junctional Rhythm (LII, V1) Atrial Flutter (LII) First-Degree AV Block (LII) Type 1 Second-Degree AV Block (LII) Junctional Rhythm (LII, V1) Type 2 Second-Degree AV Block (LII) Third-Degree AV Block (LII) Bundle Branch Block (V1) Premature Ventricular Complex (LII, V1) Type 2 Second-Degree AV Block (LII) Third-Degree AV Block (LII) Bundle Branch Block (V1) Premature Ventricular Complex (LII, V1) Ventricular Tachycardia (V1) Ventricular Fibrillation (V1) Asystole (LII, V1) Ventricular Tachycardia (V1) Ventricular Fibrillation (V1) Asystole (LII, V1) Slow-Fast Rate Irregular Rhythm Abnormal P Only Abnormal QRS Only Abnormal P and QRS

14 H OW IS AN EKG PERFORMED TODAY ? 14 Stress tests are performed to monitor the effect of exercise on the heart Motion artifacts appear as various peaks or HF noise which affect detections Artifacts as well as an increased heart rate ultimately affect classifications Research has been done by others to eliminate such noise Software will assume patient is at rest

15 W HAT ARE THE PIP S ? 15 CharacteristicDescription P wave3mm high 0.12 seconds long upright QRS complexg.t. 5mm high 0.06 – 0.12s long upright T waveg.t. 0.5mm high upright U waveupright PR interval0.12 – 0.2s long Atrial rate60 – 100bpm Ventricular rate60 – 100bpm P R Q S T PR Interval

16 W HAT ARE CURRENT PIP EXTRACTION METHODS ? Pan Tompkins method Non-linear transformation Developed in 1985 Jiapu Pan and Willis Tompkins Detection rule set further developed by Patrick Hamilton and EPLimited Finds QRS complex, but further filters required to find P and T waves 16

17 W HAT ARE CURRENT PIP EXTRACTION METHODS ? ( CONT.) 17 Dyadic Quadratic Spline Wavelet Transform Focuses on time frequency analysis Uses 5 FIR filters running in parallel Separates EKG characteristics into various scales A peak’s delayed location is found when there is a zero crossing between two local maxima and minima peaks on a subset of the filter outputs Onsets and offsets calculated based on maxima and minima onsets and offsets

18 D EVELOPMENT 18

19 S YSTEM B LOCK D IAGRAM 19 ADC Filter 1 Filter 2 Filter 3 Filter 4 Filter 5 Threshold QRS Detect P/T Detect Post-DetectClassify

20 F ILTER D ESIGN AND I MPLEMENTATION 20

21 F ILTER D ESIGN AND I MPLEMENTATION ( CONT.) 21 Constant 62 millisecond delay MIT-BIH Record 100 Filtered

22 D ETECTION D ESIGN AND I MPLEMENTATION QRS complex and P/T wave detection schemes run in parallel Decided by thresholds Shared Buffer Possible QRS peaks stay on the QRS side Possible P/T peaks may migrate to the QRS side 22 Threshold QRS Detect P/T Detect Post-Detect

23 D ETECTION T HRESHOLDS Autonomous threshold technique used to find modulus peaks on filter outputs 4 sets of 2 2 sets for QRS (1,2) 2 sets for P/T (3,4) 23

24 QRS D ETECTION 24 Filters Q1, Q2, Q3 to find QRS Three simultaneous zero crossings correlate to a QRS peak Filter Q2 is used to locate the onset and offset of each QRS MIT-BIH Record 100 Filtered

25 C ALCULATING P ULSE R ATE Ventricular heart rate calculated at this point as the mean of the last 8 heart rates found by getting the difference between successive R peaks 25 MIT-BIH Record 100

26 P AND T WAVE D ETECTION Filters Q3, Q4, Q5 to find P and T waves Found peak on Q3 && Q4 || Q4 && Q5 Detected as ‘Blip’ wave P,T, or U wave decided in post- detection Onset and offset are calculated similar to QRS onset and offset using Q4 26 MIT-BIH Record 100 Filtered

27 P OST D ETECTION 27

28 D ETECTION D ELAYS QRS detection delay: 262ms to 462ms Lag seen after 129 beats per minute P/T detection delay: 162ms to 362ms Lag seen after 165 beats per minute Filter Delay 62 milliseconds Blanking Window 200 milliseconds for QRS, 100 milliseconds for P/T Future Values 200 milliseconds Finding second modulus peak Finding offset 28

29 C LASSIFICATION D ESIGN AND I MPLEMENTATION 29 1.Examine the Rate 2.Examine the Rhythm 3.Examine the axis, intervals and segments 4.Examine everything else

30 C LASSIFICATION D ESIGN AND I MPLEMENTATION ( CONT.) 30 1.Examine the Rate 2.Examine the Rhythm 3.Examine the axis, intervals and segments 4.Examine everything else Ex. Sinus Rhythm is present when there is a 1:1 P wave ratio with all P waves being upright

31 C LASSIFICATION N- ARY T REE 31

32 C LASSIFICATION D ELAY Overall classification delay is one beat Takes three normal beats for a normal classification 32 MIT-BIH Record (NSR)

33 P ERFORMANCE T ESTING Test setup MIT-BIH Arrhythmia Database MIT-BIH Normal Sinus Rhythm Database Test procedure Resample signals to 250Hz Start at 20 seconds into signal Stop at 10 minutes into signal Count hits and misses Test result type Positive Abnormal Classification (PC) Positive Unknown Classification (PU) Positive Normal Classification (PN) Negative or missed Abnormal Classification (NC) Negative or missed Normal Classification (NU) Test Metrics Classification Hit Ratio (%) ((PC+PN)/(PC+PN+NC))*100 Normal Hit Ratio (PN/(PN+NU))*100 33

34 P ERFORMANCE T ESTING R ESULTS 34

35 I NVALID N ORMAL C LASSIFICATIONS 35 Classified Normal as Abnormal Sinus Rhythm (100, 101, 103, 106, 223, 16265, 16272, 16773) Morphology calculations landed outside of textbook normal Still found a sinus rhythm, just wider/taller waves Classified Normal as Sinus Tachycardia (16265, 16773) Software was correct after a further look into the signal Classified Normal as PVC (219, 223, 16265) Missed P waves, low ST segments that look like inverted T waves, same ASR issues Still underlying normal heart rate Classified Normal as Unknown High frequency noise that carried over to filters (108, 222) Missed P waves, low ST segments cause inverted T waves Tall Narrow P waves in close proximity to QRS (222)

36 A BNORMAL C LASSIFICATIONS PVCs classified as Unknown Incorrect QRS polarity detection (106,109) Missing P waves and underlying sinus rhythm (109,219) Really wide QRS complex (124) Triplet PVCs at rate less than 100 BPM (124) Atrial Fibrillation classified as Unknown Fibrillatory waves’ amplitude too low to detect atrial rate (219,222) Atrial Flutter classified as Unknown P waves tall, wide, close together cause incorrect P wave detections (222) 36

37 F IXING ASR To fix ASR issue, slightly modified textbook normal morphology P wave height and width, PR interval Increased accuracy by an average of 38.06% More reliable fix Use a frequent Pattern Tree in a learning phase to learn the patient’s normal sinus rhythm Accelerometer input 37 MIT-BIH Record 103 before (top) & after (bottom)

38 Fix to Atrial Flutter classified as Unknown Create separate detection with same filter or separate filter/detection scheme all together I NVALID A TRIAL F LUTTER 38 MIT-BIH Record 222

39 P/T D ETECTION I NTERFERENCE 39 To fix the Normal as Unknown classifications Add smoothing filters on front end Independent filter output buffers for QRS and P/T detection MIT-BIH Record 222

40 C LASSIFICATION M ODIFICATIONS 40

41 P ERFORMANCE T ESTING S UMMARY Detection modifications still need to be made Classification modifications were made Improved accuracy Auto-Threshold allowed for QRS detection after pacemaker stopped pacing to still detect QRS complexes Pacemaker spikes detected as QRS complexes Wavelet Transform still allows QRS complexes to be detected among HF baseline drifts 41

42 P ROPOSED F UTURE M ODIFICATIONS 42 Smooth Filter QRS Detect P/T Detect Post-DetectClassify Filter 5x Threshold12 Threshold34 ADC

43 L IVE S YSTEM T ESTING 43

44 L IVE S YSTEM T ESTING 44 BytesBeforeAfter.text data 336.bss Program Memory Usage 32932, 6.3%32784, 6.3% Run Length (16 MHz Clock) Initialization: 100 us Classification Not Found: 150 – 210 us Classification Found: 460 – 550 us

45 L IVE S YSTEM T ESTING D EMO 45

46 C ONCLUSION 46

47 P ROBLEM S TATEMENT Focus on the research, development, design, testing and validation of the electrocardiogram sensor 47 R ESEARCH Q UESTIONS Is it feasible to autonomously detect and classify EKG arrhythmias to assist in a medical situational assessment and health status feedback? Will the system be able to differentiate a normal rhythm from an abnormal one? Will the system be able to perform in or close to real- time?

48 R ESULTS Proved feasibility to autonomously detect arrhythmias, differentiate between normal and abnormal rhythms all in real-time Contributed an additional combination of detection and classification software geared towards classifying normal sinus rhythm as well as 15 other abnormal classifications in real- time on an embedded platform with the ability to become mobile once integrated with other noise canceling sensors/filters 48

49 S UGGESTIONS FOR F UTURE W ORK Further research and modifications needed for P and T wave detections Research required into a frequent pattern tree for classification with a learning phase 49

50 Q UESTIONS 50


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