Luận án tiến sĩ: Hiệu quả của hệ thống giám sát ECG cầm tay từ xa - UNC Chapel Hill

EFFECTIVENESS OF A HANDHELD REMOTE ECG MONITOR Swaroop Swaran Singh A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biomedical Engineering, School of Medicine. Chapel Hill 2006 Approved by, Henry S. Hsiao, PhD Carol L. Lucas, PhD Stephen R.

Quint, PhD Barbara Waag Carlson, PhD UMI Number: 3239195 UMI Microform 3239195 Copyright 2007 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest Information and Learning Company 300 North Zeeb Road P.

Box 1346 Ann Arbor, MI 48106-1346 © 2006 Swaroop S. Singh ALL RIGHTS RESERVED ii ABSTRACT Swaroop S. Singh: Effectiveness of a Handheld Remote ECG Monitor (Under the direction of Prof. Hsiao) This present study deals with designing a real-time remote handheld ECG monitoring system and evaluating its potential usefulness in early detection of heart conduction problems.

The raw ECG recordings were sent by the handheld monitor (client) to a remote server, which performed an on-line ECG analysis and sent the results back to the client. Real-time feedback provided to the client included display of ECG, results of ECG analysis and alarms (if required). The objective of this work was to determine its effectiveness in real-time identification of particular pattern preceding ventricular fibrillation. The remote server identified the occurrence of QRS complex and premature ventricular contractions and monitored ECG for ventricular tachycardia and variations in heart rate variability indices.

The sensitivity and specificity of the QRS detection to ECG recordings from MIT- Arrhythmia database were 99. Similarly these parameters of the premature ventricular contraction detection were 87. The time between alarm and the onset of ventricular fibrillation was measured on ECG recordings where premature ventricular contractions were found to lead to ventricular fibrillation. The remote monitor was able to successfully identify the onset on ventricular fibrillation.

Early detection could contribute to better response to an emergency intervention. iii HRV indices sensitive to the differences between normal and subjects with congestive heart failure were monitored in real-time. They were heart rate, statistical index RMSSD, total spectral power, high frequency power and the ratio of low frequency to high frequency power (LFP:HFP). The effectiveness of HRV indices was tested on an ECG recording of a sleep study subject, who experienced cardiac arrhythmia.

Cyclic changes observed in total spectral power prior to onset of cardiac arrhythmia could be attributed to REM sleep cycles. No other conclusive change in HRV indices was observed. The monitor’s usefulness in predicting long-term prognosis of post-MI subjects was tested on ECG recordings from two subjects made immediately after conclusion of cardiac arrhythmia and during a follow-up visit. Both showed higher RMSSD, total spectral power and LFP:HFP ratio.

Personalizing the monitor for each patient further improves its accuracy in measurement of various parameters. iv To my parents and my better half v TABLE OF CONTENTS LIST OF TABLES………………………………………………………………… x LIST OF FIGURES……………………………………………………………….1 Statement of Problem………………………………………………….1 Electrical activity of the heart……………………….2 Premature Ventricular Contractions…………………………………… 7 2.4 Heart Rate Variability………………………………….……………… 18 3 Effectiveness of Handheld Real Time Remote ECG Monitor….2 Remote Computation Server………………….2 QRS Detection/Classification Algorithm……….………………………………… 45 4 Evaluation of Heart Rate Variability Indices to Predict Cardiac Event Using a Real Time Handheld Remote ECG Monitor………….2 Remote Computation Server…….4 Heart Rate Variability……………………….1 Time domain measurements…………………………….2 Frequency domain measurements…………………….……………… 67 5 Development of a handheld remote ECG monitor to assess cardiac risk based on heart rate variability………………………………….2 Remote Computation Server…………………………………… 73 5.4 Heart Rate Variability…………………………………………… 74 5.1 Time domain measurements…………………………….2 Frequency domain measurements…………………….1 References……………………………………………………………… 87 APPENDIX………………………………………………………………… 88 ix LIST OF TABLES Table 3. Arrhythmia classification table……………………………………………. QRS sensitivity and specificity measurements on ECG records from NSR-DB data ………….

Sensitivity and specificity measurements by QRS & PVC detectors on ECG records from MITDB ……. Sensitivity and Specificity measurements of QRS detection on normal and arrhythmic ECG recordings……………………………………………. Summary of differences between various HRV indices……. Sensitivity and Specificity measurements of QRS detection on normal and arrhythmic ECG recordings…………………………………………….2 Summary of changes during follow-up visit………………………………… 80 x LIST OF FIGURES Figure 2.

Electrical stimulation of the heart…………………………………………… 6 Figure 2. Normal sinus rhythm recorded on Lead II……………………………………. ECG record with premature ventricular contraction ………. Common structure for non-syntactic QRS detectors………………………….

QRS detection using Wavelet Transform……………………………………… 12 Figure 3. ECG record with premature ventricular contraction ………. Remote client-central server model………………………. Software model at the central computation server………………………….4: Multilevel dyadic wavelet analysis filter bank……………….

The scaling and wavelet function (‘bior2.6’) used for analysis……. ECG data and reconstructed data from wavelet levels 1-7……………………. Flow chart of QRS detection algorithm………………. Flow chart of PVC detection algorithm……………………………….9 (a) Photograph of handheld device (b) Screenshot of the remote server….10 Negative time measurements.

A screen-shot of the server application of successful early detection………. Software model at the central computation server……………………. Flow chart summarizing individual steps in HRV analysis……………………. Comparison of HR and RMSSD……………………………………………….

Power spectrum of a normal ECG recording………………………………. Power spectrum of a CHF ECG recording…………………………………. Comparison of total spectral power and normalized spectral components…. Comparison of LFP:HFP ratio……………………………………………….

Variation in HR over 36 5 minute time points…………………………………. Variation in RMSSD 45 minutes prior to chest pain…………………………… 61 Figure 4. Total spectral power at various time-points prior to chest pain………………… 62 Figure 4. HFP(norm) at various time-points prior to chest pain………………………….

Variation of LFP:HFP ratio prior to chest pain………………………………. Variation in low frequency and total spectral power measured 45 minutes prior to chest pain………………………………………………… 65 Figure 5. Software model at the central computation server……………. RMSSD after arrhythmia and during follow-up……………………………….

Total spectral power and LFP:HFP ratio after arrhythmia and during follow-up in Subject A………………………………………………………… 78 Figure 5. RMSSD after arrhythmia and during follow-up………………………………. Total spectral power and LFP:HFP ratio after arrhythmia and during follow-up in Subject B…………………………………………………………. 79 xii LIST OF ABBREVIATIONS CHF – Congestive Heart Failure ECG – Electrocardiogram HRV – Heart rate variability LF/HF – Ratio Low frequency power to high frequency power PDA – Personal Desktop Assistant (also referred to as handheld device) PVC – Premature ventricular contraction RMSSD – Root mean squared sum of differences VT – Ventricular Tachycardia xiii CHAPTER 1 Introduction A large number of people need immediate attention when they experience life- threatening ventricular arrhythmia or angina.

Most of the sudden deaths are caused by cardiac arrest, usually resulting from ventricular arrhythmia that occurs as a result of myocardial ischemia. Moreover, many studies attest that rapid response times in pre- hospital period is key in reducing mortality and dramatically improved patient outcomes [1-3]. Electrocardiogram (ECG) is the most important noninvasive diagnostic tool used for assessing the probability of cardiac event, for stratifying its degree (stable, unstable angina, risk of out-hospital or in-hospital death) and for guiding therapy. Early detection of potentially dangerous cardiac arrhythmia could lead to timely intervention.

Significant changes have been reported in the analysis of beat-to-beat intervals of heart rate (heart rate variability) in the period immediately preceding ventricular tachyarrhythmia [4-5]. Such patients may benefit from anti-arrhythmic therapy or intervention. Short-term heart rate variability measures are used for initial screening of all survivors after an acute myocardial infarction [6], prediction of outcomes after myocardial infarction [7] and monitoring of patients after medication and exercise. For monitoring purposes, the Holter based equipment requires clinical supervision and provides no real-time feedback for the patient.

Wireless devices provide additional mobility but do not provide adequate real-time monitoring. Handheld devices like Personal Digital Assistants (PDAs) are compact and have increasingly powerful computing capability for complex calculations required for this work. The newer models with features like built-in networking and their integration into the cellular phone has provided the remote monitor access to hospital services. When integrated with a remote processing server, the PDA provides an effective and inexpensive method to monitor real time display of cardiac signals for (i) Normal sinus rhythm (ii) Premature ventricular contractions (PVC) (iii) Ventricular tachycardia and (iv) Changes in heart rate variability indices in normal and in patients affected by cardiac conditions.1 Statement of Problem In early myocardial ischemia, ventricular fibrillation is often preceded by ventricular tachycardia, which eventually gives way to the ventricular fibrillation [8].

Since the onset of ventricular fibrillation is extremely difficult to pinpoint in many cases [9], it would be useful to design a monitor that accurately detects the onset of ventricular tachycardia. Reducing the time from the detection of “warning” signs of ventricular tachycardia to emergency intervention may prove helpful in preventing the onset of ventricular fibrillation and allow for more rapid delivery of lifesaving interventions. Thus, any clinically useful detector should respond to the runs of tachycardia preceding fibrillation. In other words, the system should exhibit a ‘negative time to alarm’ compared to the onset of ventricular tachycardia and fibrillation.

2 The primary objective of this dissertation is to design and measure the effectiveness of the handheld remote ECG monitor, which includes a QRS and PVC detection algorithms. The QRS and PVC detection algorithms were validated against a standard annotated database. The effectiveness of the monitor for detecting the onset of life threatening arrhythmia (ventricular fibrillation) was quantified by measuring the ‘negative time to onset’ of ventricular fibrillation. The secondary objective is to determine the usefulness of adding HRV measures to real-time remote ECG monitoring.

A number of HRV indices were assessed including heart rate, SDNN, RMSSD, Total spectral power, Low-frequency power, High-frequency power and High frequency power to low frequency power ratio. The sensitivity of these indices to differentiate ECG recordings from normal and subjects with congestive heart failure (CHF) was evaluated. The indices identified to be sensitive in differentiating normal from subjects with CHF were used to determine if they predict the onset of chest pain (arrhythmia) in recording of an older subject during sleep The tertiary goal is to determine the usefulness of the remote monitor in providing long-term prognosis based on HRV changes. HRV indices calculated from ECG recordings of two subjects made after conclusion of cardiac arrhythmia and a follow-up study done a year later were compared.

Changes in HRV indices would provide better assessment of cardiac risk. Delay in presentation after myocardial infarction. Journal of The Royal Society of Medicine 1993;86:62-64. Canto JG, Rogers WJ, Bowlby LJ, et al.

The pre-hospital electrocardiogram in acute myocardial infarction: Is its full potential being realized? Journal of American College of Cardiology 1997;29:498-505. Rubel P, Gouaux F, Fayn J, et al. Towards intelligent and mobile systems for early detection and interpretation of cardiological syndromes. Computers in Cardiology.

Lombardi F, Porta A, Marzegalli M, et. Heart rate variability patterns before ventricular tachycardia onset in patients with implantable cardioverter defibrillator. American Journal of Cardiology 2000;86:959-63. Nemec J, Hammill SC, Shen WK, et.

Increase in heart rate precedes episodes of ventricular tachycardia and ventricular fibrillation in patients with implantable cardioverter defibrillators. Pacing and Clinical Electrophysiology 1999;22:1729-38. Odemuyiwa O, Malik M, Farrell T, et. Comparison of the predictive characteristics of heart rate variability index and left ventricular ejection fraction for all-cause mortality, arrhythmic events and sudden death after acute myocardial infarction.

American Journal of Cardiology 1991;68: 5: 434-439. Carpeggiani C, L’Abbate A, Michelassi C, et al. Early assessment of heart rate variability is predictive of in-hospital death and major complications after acute myocardial infarction. International Journal of Cardiology 2004;93:3:361-368.

Pogwizd SM and Corr PB. Mechanisms underlying the development of ventricular fibrillation during early myocardial ischemia. CREI-GARD, a new concept in computerized arrhythmia monitoring systems. Computers in Cardiology 1986;13:515-518.

4 CHAPTER 2 Background The heart is a muscular organ responsible for pumping blood through rhythmic contractions. It receives deoxygenated blood from the venous system and after oxygenation in the lungs the blood is sent back into arterial system. These contractions are associated with electrical activity of the heart and can be detected by surface electrodes.1 Electrical activity of the heart Electrical stimulation of the heart originates at the sino-atrial (SA) node in the upper section of right atrium. Since the atria are insulated from the ventricles, electrical excitation passes only through the atrioventricular (AV) node.