Keywords: Electrocardiography, Heartbeat, Holter monitor, HRV, Hurst parameter, signal processing. 1. INTRODUCTION. Electrocardiography is one of the ...
Complex Control Systems 2012 Institute of Systems Engineering and Robotics ISSN 1310 - 8255
INTELLIGENT SYSTEM FOR PROCESSING AND ANALYSIS OF CARDIOLOGICAL DATA
G. Georgieva-Tsaneva, M. Gospodinov, E. Gospodinova, K. Cheshmedzhiev
Abstract: In this paper a new intelligent software system for processing and analyzing of cardiac Holter’s data is proposed. The electrocardiogram (ECG) analysis plays an important role in identifying the cardiovascular disorders. The standard lengths of the records, used in ECG analysis are 5 minutes and 24 hours. In this research is investigated the records of 24 hours. Heart Rate Variability (HRV) of ECG is the temporal variation between sequences of consecutive heartbeats. HRV analysis is a technique that relies on the assessment of fluctuations on the intervals among successive ECG R waves. This article investigates the behavior of the Hurst parameter over the HRV. Keywords: Electrocardiography, Heartbeat, Holter monitor, HRV, Hurst parameter, signal processing. 1. INTRODUCTION Electrocardiography is one of the modern contemporary non-invasive methods for analyzing the functional state of the cardiovascular activity. An electrocardiogram (Fig.1) is a measurement of the electrical activity of the heart. Some of the feature extraction methods implemented in the scientific research include Fourier Transform, Wavelet Transform and other methods. The particular attention in this paper is given to the analysis of the Variability of the Heartbeats – the characteristic depending on the intervals between heart pulses. The scientific studies in this direction [1, 2, 3, 5] indicate especially importance of this parameter in cardiology. One of the new diagnostic methods for heart variability research and diagnostic is based on the mathematical analysis of the Hurst parameter. This parameter has different values in case of healthy and diseased patients. After heart disease and after physical exercise the Hurst parameter increases. The research activities carried out over the real cardiac database, obtained from the Department of Cardiology at the regional hospital, confirms these results. The conducted studies show that Hurst parameter can be used as a predictor of cardiovascular disease and the results can be applied in clinical practice. The presented intelligent cardiological system included: - Maintaining of database with real ECG data; - Patient choice and review of cardiological data; - Determining of Heart Rate Variability;
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Calculation of the Hurst parameter over HRV; Simulation of HRV data for test of the created algorithms.
2. ECG DATA SETS In this research is analyzed the information from database composed of ECG data records for patients with cardiovascular disease. The ECG database is given by the Veliko Tarnovo Regional Hospital “Dr. Stephen Cherkezov”. The length of each ECG record is around 24 hours. The sampling rate of the ECG is 200 Hz. The investigated group of patients is in the age 42-92 years. Data were obtained by Holter monitoring of the heart activity. The patient’s resumes usual activities while the Holter records a continuous ECG tracing for at least 24 hours. Data obtained by Holter monitor are compressed and the information is organized in 5 second blocks. After decompression by the developed algorithm of the intelligent system the blocks are organized in 4 channels with different length. R wave RR interval ECG ECG AMPLITUDE
1.5 1 0.5 0 -0.5 -1 0
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Fig. 1 Electrocardiogram The developed system performs the following steps: Determining the number of recorded channels; Setting the operating mode; Decompression of data; Combines different 5-seconds blocks; Reproduce each recorded channel; Determining the cardiac intervals; Creating HRV sequence; Hurst parameter computation and analysis of deviation from the normal status. 3. HEART RATE VARIABILITY AND HURST PARAMETER The HRV is a measurement of the interaction between sympathetic and parasympathetic activity in autonomic functioning. The health monitoring is important task [6]. On a standard electrocardiogram, the maximum upward deflection of a normal QRS complex is at the peak of the R wave (Fig. 1). The duration between two adjacent QRS peaks resulting from sinus node is termed the NN (normal to normal) interval. HRV is the measurement of the variability of the NN intervals. The high variability is an indicator of good health (Fig. 2) The low variability indicates serious risk for pathological diseases (Fig. 3).
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RR INTERVAL [SEC]
HIGH HRV
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Fig. 2 Normal HRV
RR INTERVAL [SEC]
LOW HRV 1.3 1.1 0.9 0.7 0.5 1
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Fig. 3 Abnormal HRV In science literature the analysis techniques from fractal theory are applied to assess the HRV [ 1, 9, 11, 14]. In this research the fractal scaling properties of the Heart Rate Variability time series are studied using the Hurst parameter. The published results of the investigated values of the Hurst parameter (H > 0.5) show increasing after the disease, trauma and physical activity [10, 14]. The described in this article investigation by analyzing the Heart Rate Variability confirm the above mentioned results. The R/S diagram of HRV time series for Hurst parameter is shown in Fig. 4. As mentioned in figure, the Hurst parameter increased after the cardiovascular disease. In the developed system are used three methods to determine the accuracy of the Hurst parameter: R/S (Rescaled adjusted range) method, ANOVA analysis (Analysis of Variance) and IDC (Indexes of Dispersion) method. Only the R/S method for cardiac data is used in the scientific literature. The developed system uses three methods. The comparative analysis shows higher accuracy of ANOVA and IDC [7, 8]. This is the reason for using of these two methods in determining the Hurst parameter. The relative error of the estimated Hurst parameter defined by [4]: (1)
Н
Hˆ H H
.100% ,
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10 HEALTHY PATIENTS
LOG(R/S)
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6 4 2 0 0
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LOG(LENGHT OF BLOCК)
Fig. 4 The Hurst parameter of the HRV for ECG patients after the cardiovascular disease ˆ - determined value of Hurst parameter; where: Н Н – the real (input) value of the Hurst parameter.
For simulation of cardiac sequences are used Wavelet transform and Gaussian distributions. The developed algorithm with Fourier transform is described in [13]. The typical bimodal spectrum of the short sets of HRV data is created by the sum of two Gaussian distributions [13]: (2) (
Si (f)
f fi 2 , exp 2 2. . с i2 2c i σ i2
f - frequency; f i - means of frequency; сi - standard deviations;
i2
- power in the
LF and HF bands). The substitution of the Fourier transform with Wavelet transform gives the following: - a high performance; - a better representation of the data in time-frequency domain. 4. INTELLIGENT SYSTEM ECGAniSis The software realization of intelligent system is ECGAniSis. The basic components of the developed software are as follows: 1. ECG database with functions: maintenance, open records, editing and adding new records; 2. Visualization of the patient’s data. The patient’s data includes: name, address, phone, physiological data, Holter recording date, start time and duration of the record, name of the doctor, address of the hospital and medical conclusion (Fig. 5). 3. Display of four ECG leads (Fig. 6) 4. Determining Heart Rate Variability. 5. Calculation the Hurst parameter for each patient. 6. Simulation of HRV data with the ability to set various input values (Fig. 7).
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Fig. 5. Data of the patients
Fig. 6 Four ECG leads. 5. CONCLUSIONS In this article a new intelligent software system for processing and analyzing of cardiac Holter’s signals is described. The system is result of research of the Hurst parameter over the Heart Rate Variability. The study of the Hurst parameter along with other parameters (used in the study of ECG) will assist in the setting of correct diagnosis and prevention of cardiovascular diseases. On the base of the scientific research is
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developed practical applied software with database for patient’s monitoring of the HRV and long-term storage of the results and investigation of their medical status.
Fig. 7 Simulation of HRV data
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