Neonatal ECG Monitoring: Neonatal QT Interval Measurement System

Neonatal ECG Monitoring: Neonatal QT Interval Measurement System Sanket Mugali 1 Uday Nair 2 1 1 Automatic Control and Systems Engineering, University of Sheffield, Sir Henry Stephenson Building, Mappin Street, Sheffield, S1 3JD 2 Management School, Sheffield University Management School, Conduit Road, Sheffield S10 1FL Abstract Long QT Syndrome is considered to be significant in the issue of infant mortality. It is the condition caused due to delay in electrical activity of the heat which reflects in the Q-T interval of an ECG. Q-T interval is a measure of time elapsed between start of Q wave and end of T wave [5]. The complexity of determination of this delay increases in infants because of the high heart rate. A system to represent the accurate Q-T interval measurement was proposed for a portable ECG monitor. The objective of the project is to create a Q-T interval measurement system for neonatal ECG signal analysis. The process includes data acquisition, processing, extraction and extensive representation of the required information. The system was designed and tested on sample data acquired from physiobank database [19]. Q start point was successfully established however, end of T wave could not be established with great accuracy. MATLAB v. 8.1.0.604 is used to develop the system. Keywords Electrocardiograph (ECG), Long QT Syndrome (LQTS), MIT-BIH Arrhythmia Database and QT Database, Pan- Tompkins Algorithm, Wavelet Decomposition however, end or offset of T wave could not be established accurately. I. INTRODUCTION 1 Electrocardiograph (ECG or EKG) is a transthoracic translation of the electrical activity and function of the heart [3]. It is an aide in diagnosis of any irregularities regarding the heart s activity. The rhythmic activity is represented as waves in an ECG. It is interpreted as peaks and intervals. It is composed of P, Q, R, S, T and U waves. The Q-T interval is of significance since it provides vital information regarding existence of ailments, if any. Prolongation of this interval is popularly known as long Q-T syndrome, which is a cause for sudden death in patients. Over the years, the Sudden Infant Death syndrome (SIDS) has been meticulously studied [6]. The infant mortality due to long QT syndrome has been significant in number of fatalities. The prolongation in the QT interval proves to be fatal because of the high heart rate in infants, ranging from 80 to180 beats/min. Due to its significance, a rugged, economic and portable device to measure neonatal QT interval is important. The objective of the project is to create an optimal Neonatal Q-T interval measurement system for portable devices. The main objective of the project is to eliminate the noise and artifacts from the original ECG signal to get an accurate measurement of the QT interval. This can be achieved by effective signal processing of the acquired data. The data is acquired from MIT-BIH Arrhythmia database and QT database available on www.physionet.org[19]. To extract Q-T interval, extraction of QRS complex and T wave is necessary. This is implemented using Pan-Tompkins Algorithm [18] and Wavelet Decomposition [12]. These methods were efficient in extraction of QRS complex. The Q-T interval is the time elapsed between onset of Q wave and end of T wave. Onset of Q wave was successfully established, II. BACKGROUND This section covers the topic of generation of electrical pulse in the human heart, detection and representation of this electrical activity in the form of an ECG, and review the techniques used in extraction of the necessary information from an ECG signal. A regular heart beat is generated by a tiny pulse of electric current. This small electric current rapidly spreads through the heart and contracts the heart muscles. The process begins at the top of the heart, spreads down and then goes back to the top again, causing an optimal contraction of the heart muscles to pump blood [1]. The cardiac cells at rest are considered to be polarized, i.e. absence of electrical activity. Ions such as sodium, potassium and calcium, with different concentrations are separated by the cell membrane of the cardiac muscle cell. This period is called the resting potential. A specialized cardiac cell automatically generates an electrical impulse. Generation of this electrical impulse is the cause for the ions to cross the cell membrane and cause action potential. This process is called depolarization [2]. Ion movement across their channels is the drive causing contraction of the cardiac muscle. This myocardial muscle contraction due to depolarization moves across the heart as a wave. The relaxation of the myocardial muscle results in the return of the ions to their previous resting state. This process is called repolarization [2]. The muscle activity is, therefore, the result of depolarization and repolarization electrical activities. The electrical impulse generated with depolarization and repolarization is not confined only to the heart, but spreads across the body. An Electrocardiograph (ECG or EKG) is the interpretation of these electrical activities recorded to an external device, detected by the 1

electrodes attached to the skin surface, over a period of time. ECG is an effective way to interpret the function of the heart. The successive depolarization and repolarization, detected by the electrodes, is represented in an ECG as waves and intervals [3]. A. ECG Analysis The cardiac cycle, as represented in the ECG, comprises of P, Q, R, S, T and U waves. These waves are generated with the successive depolarization and repolarization activity of the heart. The major points of interest among these are P- wave, QRS complex and T-wave. NORMAL RANGE OF THE ECG P-R INTERVAL: 0.12 TO 0.20 SECONDS Q-T INTERVAL: 0.35 TO 0.44 SECONDS S-T SEGMENT: 0.05 TO 0.15 SECONDS The Q-T interval is heart rate, gender and age dependent and can be adjusted to improve the detection of high risk arrhythmia. The duration is around 440 milliseconds. B. QT Interval Representation The standard clinical procedure to determine this interval is use of Bazett s formula [6] which is given by, (1) Where, QTc is the corrected Q-T interval measurement and R-R is the duration between two successive R-peaks. Q-T segment reflects many of the cardiac abnormalities. Prolongation of the Q-T interval can be an indication towards certain types of arrhythmias, a fatal condition. C. QT Interval Interpretation As mentioned, Q-T interval is the measure of ventricular depolarization and repolarization, the prolongation is caused due to electrical dysfunction of the ion channels in the cardiac muscle cells as a result of increased positively charged ions, causing a delay in repolarization which increases the risk of sudden death. This is often referred to as long QT syndrome (LQTS), which is a cause of infant mortality. LQTS is an ion channel disorder, i.e.; the disorder is caused due to mutation in either cardiac potassium channel gene [KCNQ1(LQTS1), KCNH1(LQTS2)] or cardiac sodium channel gene [SCN5A(LQTS3)][5]. LQTS is categorized under Sudden Infant Death syndrome (SIDS)[6] and is considered as one of the significant cause of death in neonates or new born children. It is necessary to determine the Q-T interval accurately for proper treatment. Mass data analysis of neonatal ECG shows the positive predicted accuracy for SIDS is just 1.4% for a QTc of more than 440 milliseconds [14]. With high repeatability rate, about 50 milliseconds, accurate QTc measurement becomes a challenge. Determination of the T wave offset or T wave end is difficult at such brisk heart rates. This tends the Bazett s formula [6], for corrected Q-T interval (QTc), invalid, further reducing the prediction accuracy of 1.4% [14]. Physical interpretation of the Q-T interval is difficult in such case. Difficulties in Interpretation Manual interpretation is even more difficult with the inclusion of noise in the ECG. Noise source can be from muscle contractions, respiration, power line interference, inductance from the leads and/or surrounding electronic equipment [7]. The interferences generally lead to a signal drift from the baseline, creating an instance called base line wandering. All such interferences have to be eliminated for accurate extraction of the Q-T interval. ECG signal needs to be processed optimally to achieve this. A robust system is necessary to aide in clear interpretation of the Q-T interval. The main objective here is to design such a system for a portable ECG recording device. The portable devices range from two lead to twelve lead measurements. For a multi lead device, the Q-T interval is a measure of average of all the leads. If a single lead is to be chosen, the priority is lead V(3) or V(5)[15]. The signal processing includes elimination of the base line drift and other instances of noise, feature extraction involving extraction of Q-T interval information and representation. The range of an ECG is about 0.1-250 Hz in frequency. Low frequency interference in the range of <0.03 Hz causes base line wandering [17]. Various filtering methods can be employed to correct the base line drift of the signal, such as, FIR filters, Wavelet decomposition, Adaptive filters, Zero Phase IIR filters and moving averaging filters [16]. Power line interference, as the name suggests, can be caused due to the power supply component of the device. It may not be an issue with the portable devices because of its relatively low operating voltage; however the possibility cannot be ignored. This noise is generally in the range of 50 Hz which can corrupt the ECG signal and prevent the detection of certain types of arrhythmia. Process Extraction of the required information from an ECG after the filtering is of primary interest. Research in this area is highly active among both medical practitioners and engineers. Many algorithms and mathematical models have been developed and tested over the years. One of the most popular methods employed for this purpose is Pan- Tompkins algorithm [18]. The ECG signal is filtered using band pass filter with a cascade of a low pass and high pass filter. Next steps involve differentiating and squaring the signal. Finally, the signal is integrated with a moving window method [18]. The method is accurate for extraction of QRS complex and is discussed further in the report. Another method employed for the feature extraction is based on the idea of wavelet decomposition or wavelet transform. An array of low pass and high pass filters are used for the decomposition of the signal, which filters the signal and simplifies the feature extraction. The combination of successive low pass and high pass filters used in this is known as filter banks [12]..

3 The detection of the QRS complex is started by first detecting the most prominent peak in the signal which is the R peak. The detection of Q peak and S peak is relatively simpler from this point on. This is followed by detection of T peak in the signal using a similar approach. However, measuring the offset or end of T wave is a challenging task. This is very important, as Q-T interval is a measure of the time III. DATA ACQUISITION The sample data was acquired from an online database, available on the website www.physionet.org. The database contains large collection of recorded physiological data and is open source [19]. The data used in ECG analysis in this report was acquired from the MIT-BIH Arrhythmia Database and QT Database. The MIT-BIH Arrhythmia database contains two lead ECG data recored for half hour. The data set is a mixture of both in patients and out patients, annotated by cardiologists for digital interpretation. The records were digitized at a sampling rate of 360Hz[20]. The QT Database contains fifteen-minute ECG recordings, which were selected from the MIT-BIH Arrhythmia database, European Society of Cardiology ST-T database and various other sources to work with the real-time QT interval detection algorithms. The data was annotated by cardiologists and digitized at a sampling rate of 250Hz[20][21] The digitized ECG data is available in various formats such as *.txt, *.dat, which is desirable for analysis [19]. The digitized ECG data can be loaded into MATLAB directly using the WFDB Toolbox, open source software package available from the website and is compatible with all versions of MATLAB. The toolbox contains applications to read, write and plot the physiobank data [22]. method has its limitations, which may lead to distortion of the ECG. To refrain the ECG signal distortion, linear phase filtering can be used such as Forward-Backward IIR filters or Zero Phase Filtering. Powerline Interference Power line interference, as the name suggests, can be caused due to the power supply component of the device. This noise is generally in the range of 50 Hz, which can corrupt the ECG signal and prevent the detection of certain types of arrhythmia. The power line noise can be filtered out using a notch filter set at 50 Hz. However, the power line noise can sometimes be unpredictable and may vary beyond 50 Hz. The design can be improved by using adaptive filtering technique [9]. For a portable device, this may not be an issue. Filters are implemented using the DSP Toolbox in MATLAB. Butterworth filters are implemented for interference elimination. Bandpass filters with the combination of High-pass filter and Lowpass filters set at 0.5 Hz and 40 Hz respectively are used to eliminate base line drift and resist power line interference. V. FEATURE EXTRACTION This is the most crucial part of procedure involving analysis of sample data and extraction of the QT interval. Two methods are tried for this purpose, one based on PanTompkins Algorithm[18] and other based on Wavelet Decomposition[12]. A. Pan-Tompkins Algorithm This feature extraction was developed in 1985 by Jaipu Pan and Willis. J. Tompkins. It is one of the widely used algorithms to detect QRS complex, remove noise and correct base line drift. This approach combines filtering and detection based on threshold approximation. The filtering stage is preprocessing of the signal to identify and enhance QRS complex and suppress the remaining elements in the signal, i.e., P wave and T wave. Processing stage is a combination of: ECG sample data acquired from the database is processed for Linear filters, correction of base line drift and elimination of power line Nonlinear transformation and interference if any, and detection of QRS complex, T wave and Decision rule algorithm. extraction of Q-T interval. Initially, linear filters extract the desired frequencies and each positive peak is identified by nonlinear transformation. Presence or Interference Elimination absence of QRS complex is determined by further processing the This stage eliminates the two most common type of interferences signal using thresholding technique. affecting the ECG recordings which are base line drift and power line interference. B. Decision Rule Algorithm Baseline Wander The top priority of this stage is detection of R peak. Detection of Q Baseline wandering is an effect which shifts the position of and S peaks is simplified once this is achieved. signal base.in an ECG, this can be caused due to respiration, IV. SIGNAL DATA PROCESSING perspiration or any physical activity. The range of the frequency components is usually within 0.5 Hz(<0.03 Hz)[17], however, this can be higher under stress. The low frequency state of the baseline is in range with the S-T segment, which makes it tricky to eliminate. Narrowband filtering can be used for this purpose. This After the integration, width of the QRS complex is equivalent to the time duration of the integrated waveform s rising edge. From this rising edge, a fiducial point of the QRS complex can be located and can be estimated as the R peak. 3

Next step is detection of R peak in the synthesized signal. Since the signal is noise free, a value 60% greater then the maximum of the actual signal must be detected. However, after estimation of R peak in the decomposed signal, its value in the original signal must also be estimated as the signal is downsampled. The R peak is not a single impulse peak. This may result in two or more points which satisfy the criteria. Other unwanted points in the proximity of R peak must be eliminated. This is achieved by finding the R peaks that are at a distance of 10 samples from each other. Once an A maximum point value located in a signal changing its direction in optimum R peak is detected, it must be multiplied by four due to the specified time interval is defined as a peak[18]. SP1 is the its decomposition. A search window of +/- 20 samples must be already established peak by the algorithm. Any other peak not established in the original signal to obtain the actual R peak based on the one detected in the downsampled signal [23]. related to QRS is NP1, the noise peak. Threshold Adjustment Adaptive threshold adjustment is used to search for the peaks. The thresholds adjust automatically to float over the noise[18]. Enhancement in the signal to noise ratio by bandpass filter enables the use of low thresholds. Two thresholds are used, one being higher than the other. The higher one is used for analysis of the signal. If no QRS is detected, the lower threshold is used as a search back method in that particular interval. Q and S peak QRS complex begins at the onset of Q peak and ends at the offset of S peak. The Q and S peaks have to de determined to obtain the onset and offset of the QRS complex. The R peak is taken as the initial reference point and search windows of 150 millisecond are established on either side of it. Q fiducial point lies to the left of the R peak. The search window looks for the highest minimum or negative point. This is Q peak. Similarly, S fiducial point is obtained with a search window to find the highest minimum or negative point to the right of R peak. Q onset is located with a search window starting at Q peak and moving towards the left to find the lowest minimum or negative point. Once the R peak is detected in the original signal, other peaks can be located by establishing search windows on either side of the R peak. Search windows are selected based on their position from the R peak. A search window initiating after 10 samples and terminating at 100 samples is implemented to the left of R peak. This window searches for the highest minimum or negative point. This point is Q peak. The onset of Q wave is established by creating a search window of 20 samples to the left of Q peak. The least minimum value point is searched which gives the onset point of Q wave. Similarly, a search window initiating after 25 samples and terminating at 100 samples to the right of the R peak is established. This window provides the T peak by locating the highest positive point. The T offset is located with a search window initiating at T peak and terminating after 20 samples to the right of the peak. T peak and offset detection T peak is the second maximum peak in the ECG signal. A search window is initiated after 20 milliseconds to the right of the R peak and is terminated at 100 milliseconds after the R peak. The maximum amplitude point is estimated and located as T peak. D. Equations Detection of T offset is tried by establishing a search window from the T peak to the right. The least maximum value in the window..(2) width is the T offset point. The threshold for the window is defined based on the T peak value. y(nt) = 2y(nT-T) - y(nt-2t) + x(nt) The sampling frequency for data acquired from 2x(nT 6T) + x(nt QT database is 250 Hz. 12T)..(3) The sampling frequency for data acquired from MIT-BIH database is 360 Hz...(4) C. Wavelet Decomposition y(nt) = 32x(nT - 16T) - [y(nt - T) + x(nt) - x(nt It is the second method tried in ECG feature extraction. The signal 32T)]..(5) is down sampled in this process. This helps in preservation of the QRS complex. This is achieved with the wavedec function in the wavelet toolbox in MATLAB. (6) The signal is decomposed by four levels. The first level of decomposition reduces the number of samples by one-fourth of its y(nt) = [x(nt)]^2 (7) original value. The second level will have half the number of samples of the first level. The third level will have half the number of samples of the second level. The fourth level will have half the y(nt) = (1/N)[x(nT-(N - 1)T) + x(nt - (N - 2)T+...+x(nT)] number of samples of the third level. The decomposed signals are (8) essentially free from interference [23].

5 SP1 = 0.125 PK1 + 0.875 SP1 (if PK1 is signal peak) (9) NP1 = 0.125 PK1 + 0.875 NP1(if PK1 is noise peak) (10) THI1 = NP1 + 0.25(SP1 - NP1). (11) THI2 = 0.5 THI1 (12) PK1 is the average peak SP1 is the detected signal peak NP1 is the detected noise peak THI1 is the first threshold THI2 is the second threshold VI. RESULTS VII. DISCUSSION PAN-TOMPKINS ALGORITHM This method for extraction of QRS complex is efficient. Detection of Q onset was also achieved, however detection of T offset did not prove to be accurate for different sample data sets. More research in this could increase the accuracy of T offset detection. Wavelet Decomposition This method is efficient in elimination of noise from the ECG signal. However, it is not as efficient as PanTompkins Algorithm for feature extraction. The method also involves lot of mathematical calculations which increases the complexity in implementation. VIII. CONCLUSION Two methods are tried in feature extraction of an ECG signal, the primary interest being Q-T interval. The data is acquired from two databases. Extraction methods are implemented in MATLAB v. 8.1.0.604 using the DSP System toolbox, Signal Processing toolbox and Wavelet toolbox. The methods prove to be efficient in QRS complex extraction. Extraction of T wave offset was tried by threshold technique. T wave offset could not be located accurately for all of the sample data tested. This could be due to inefficiency in the design. The objective could not be achieved due to inaccuracy in detection of T wave offset, however the system is reliable in detection of the Q wave onset. References 1. "The electrocardiogram, ECG". Nobelprize.org. http://openx.nobelprize.org/educational/medicine /ecg/ecg-readmore.html, 24 Apr 2013. 2. Anatomy, Physiology, an Electrophysiology, http://www.andrews.edu/~schriste/ C o u r s e _ N o t e s /Anatomy Physiology and_ele c t / anatomy physiology and_elect.html, viewed on 24/04/2013. 3. cardivascular physiology concepts 1998-2013 Richard E. Klabunde http://www.cvphysiology.com/arrhythmias/a009. htm viewed on 19/08/2013. 4. Long QT syndrome and life threatening arrhythmia in a newborn: molecular diagnosis and treatment response, http://www.ncbi.nlm.nih.gov/pmc/articles/ PMC1768001/ 11/03/2013 18:05. 5. QT Interval Measurement Before Sudden Infant Death Syndrome,D P SOUTHALL, W A ARROWSMITH, V STEBBENS, AND J R ALEXANDER,Department of Paediatrics, Cardiothoracic Institute, Brompton Hospital, London, Doncaster Royal Infirmary, Doncaster, South Yorkshire, and Department of Mathematics, Statistics and Computing, Thames Polytechnic, LondonArchives of Disease in Childhood,1986, 61, 327-333. 6. FILTERING TECHNIQUES FOR ECG SIGNAL 5

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