Inter. Conf. on IML 2009
BCG Monitoring System using Unconstrained Method with Daubechies Wavelet Transform Yun-Hong Noh1, Hsein-Ping Kew1, Do-Un Jeong2 Dept. of Ubiquitous IT Eng., Graduate School of Design & IT, Dongseo University, Busan, South Korea 2 Division of Computer & Information Engineering, Dongseo University, Busan, South Korea E-mail:
[email protected]
1
Abstract This paper focuses on analysis of BCG (ballistocardiogram) signals and performance which can be used as an advanced diagnosis for continuous heart rate activity monitoring. In general, an electrode is attached to patients’ body to monitor the activity of the heart and is connected to the measurement system using lead line. Therefore, measurement of signals can cause inconvenience to patients. In this paper, unconstrained method is used either at home or office to monitor the activity of the heart contraction and relaxation of the heart caused by ballistic measurement. The main advantage is BCG measurement system does not required electrodes to attach to patients’ body during recording which is different from measurement of ECG (electrocardiogram). It is also a long term measurement system to monitor the heart condition. In this paper, non-restrained BCG measurement for chair type was designed for wirelessly BCG monitoring data. The measured BCG signal, which is the heart rate value, undergoes signal processing method for baseline wander noise cancellation using DB4 (daubechies 4) Wavelet. The template matching to detect the signal by using the signal normalization and characteristic point detection is useful in signal reconstruction. Adaptive threshold was used to detect the heart rate from BCG signals. The performance evaluation of the implemented system for the ECG and BCG were measured simultaneously. The system implemented confirm that using two signals were compared, peak detection algorithm was developed and heart rate is measured herein makes a strong possibility to measure BCG signals in non-restrained environments.
not use BCG as clinical purpose due to lack of interpretation algorithm, unrefined BCG signal acquisition technologies and lack of practical devices. In this research, non-restrained approach to monitor the activity of heart using BCG measurement is introduced where monitoring can be done either at home or office. The BCG signal is measured according to contraction and relaxation of the heart. The measurement of ballistic signal is depending on the changes of blood flow in heart and blood vessel in counter flow. With the information of BCG monitoring, symptoms and diagnosis of myocardial function can be carry out easily. In actual clinical observation of heart failure and recovery, several heart diseases diagnose could be utilized as secondary source by evaluating the heart function and determine effects of treatments for heart failure. Both BCG and ECG signal are similar as an index in showing the status of heart activities. However, unlike the measurement of ECG, electrodes do not need to attach to the patient’s body. This is one of the main strong advantages of BCG for signal monitoring the condition of heart for a long period of time [3]. The visual analysis of ballistic measurement is not generally used in clinical because it is just a signal presence at a sickbed. However, due to the advancement of computer processing and signal processing algorithms, it develops a new interest of BCG signals [4]. In existing research, the measurement of BCG used the concept of Electromechanical Film (EMFi) sensors where changes of the thickness occur at the time when sensor detected a signal and it can apply as a pressure sensor. This make the research of ballistic measurement system is accomplished [5], [6]. However, there is a problem in accuracy of measurement using this method. This is because the attitude of the object person either sitting or lying down causing the force or weight distribution problem occurs. This research takes place either at home or office in daily life which can perform effectively in signal measurement by using a new type of ballistic measurement system which is chair style. The instrumentation is installed in the chair between the upper petal and ready for the press of the chair is to establish a sensor where it can be used to measure the weight in attitude or the location which object sits. The heart beating on the power was implemented to detect the changes of the force. The output signal from ballistic measurement system undergoes amplification and filtering. Wireless transmission of the signal and data radio transmission of the ballistic signal is transmitting using an ultra-low power wireless sensor nodes which are powered by microprocessor. In order to detect the characteristic ingredient for the case monitoring from the ballistic signal, it measured from the embodied system this is wavelet conversion and the template matching method. The signal processing method is
Keywords: BCG (ballistocardiogram), DB4 (daubechies 4), ECG (electrocardiogram), template-matching, unconstrained, wavelet.
1. Introduction In modern world, heart related diseases mainly caused by business overweight and increasing stress in human daily life. Many researches are accomplished to cope with the emergency situations of health condition due to increasing heart diseases by continuously monitoring. In current situation, belt type electrode are attached to patient’s body while doing daily activities to measure and monitor the ECG signal are introduced [1], [2]. However, this method brings inconvenience to the patients’ daily activities where during the ECG signals monitoring process, electrodes are required to attach to patients body using lead line. Besides that, the connection between the electrode and the instrumentation system also causes some difficulties to patients to move freely around. In addition, previous health care systems do 338
BCG Monitoring System using Unconstrained Method with Daubechies Wavelet Transform proposed which used the mixes and adaptation threshold technique. Heart beat is detected from the applied ballistic signal. To evaluate the performance of the implemented system, both embodied ECG and BCG are measured simultaneously. From the two signals, it compared the detection performance and the possibility of monitoring the availability.
The relativity of ECG signal and BCG are analyzed simultaneously. The amplitude of ECG signal is large especially the R-peak, thus making the R-peak detection easier. One the other hand, J component of BCG is difficult in peak detection because the amplitude and frequency domain of H, I and L component of BCG are similar. Thus, making BCG signal more difficult to visualize and analyze the peak detection.
2. Implementation and Methods
2.2 BCG Measurement System
2.1 BCG Signal
There are quite a number of electronic devices have been developed to help clinicians in monitoring and diagnosing heart related diseases. BCG was once popular in 1970’s but due to device was difficult to construct, other methods have replaced it [7]. Recently, there are new developed sensors that offer new possibilities. BCG is an interesting measurement feature where no electrodes are needed to be attached to patient’s body during recording. Thus, it provides a strong potential possibility to evaluate the patient’s heart condition either at home or office.
Over several decades, several electronic devices have been developed to assist medical professional to monitor patient hear conditions and monitor their body’s signal. Among the several method used for these purposes, BCG has a unique characteristic where no electrodes are need to attached to patients’ body during measurement. In current medical technology, BCG is one of the newest clinical tools for monitoring, diagnosing and managing myocardial disorders related to heart disease. The disorders are due to sudden recurrent and transient disturbances of myocardial function or mechanical movements of the heart. The ballistic signal is measured according to the contraction and relaxation of the heart. It means that the signal measures the ballistic which follows the flow of blood change from the heart and blood vessel, range of upper signal is 0 to 7 mg and signal frequency range is 0 to 40 Hz. It is a vital signal in the 1-20 Hz frequency range mainly caused by the movements of heart and blood. It can be recorded from the human body using noninvasive method. In addition, unlike the measurement of ECG where electrode is attached, continuous monitoring of the heart is healthier using this new approach. Figure 1 shows the example of ballistic signal.
Fig. 2 Overall BCG Monitoring Architecture. Figure 2 show the overall system design for BCG monitoring system using unconstrained method where patient sit on chair type BCG measurement system and measured ballistic signal is wirelessly transmitted via Zigbee 2.4 GHz using a BCG transmitter. A sensor node with Zigbee CC2420 radio chip which has a build in short-range wireless sensor network is used. A BCG receiver at the receiver computer side receives the measured signal. The computer able displays the BCG data in BCG Monitoring Program and save the required data.
Fig. 1 Component of BCG Waveform. The BCG is composed of 9 wave shapes which generally are F, G, H, I, J, K, L, M, and N in order. BCG wave shapes are divided in three main groups which are pre-ejection (FCH), ejection (IJK) and diastolic (LMN). FGH is before emitting the blood from the heart, IJK is the emission of the blood and LMN is expanders which are equivalent to the heart. H is the blood from the heart which it sells the blood quickly when emitting. It occurs at the same time towards a down the line aorta. I is at the point which it follows the aorta palace and an carotid artery internal change of acceleration and lung artery surrounding of the blood. J is at point which is falling the degree of moderation of acceleration and the abdomen aorta of the blood. The size reflects the strength of contraction of the left ventricle IJ and IJ will reflect the speed of elastic. The time domain of BCG waveform is obvious periodicity. BCG has a corresponding relationship with the ECG signal.
Fig. 3 Chair type BCG Measurement System. 339
Inter. Conf. on IML 2009 In this research, a chair style ballistic measurement system to continuous monitor the activity of the heart is implemented. The instrument consists of upper petal and ready for press of chair (BC single point load cell, 30kgf 150kgf, Cas) is attached to measure the change of the object’s weight. In order to extract the output ballistic signal from the weight and force sensor signals, a signal processing block diagram as shown in Figure 4 was implemented. Firstly, the output from the micro-force sensor, load cell undergoes amplifying circuit which uses a measuring amplifier (INA118, BB Co., USA) in order to remove the 60Hz power noise notch filter. For the amplification of the signal, it composed of the signal amplifying circuit. It uses the operational amplifiers to remove the high frequency noise which include two low pass filters. After the low-pass filter (fc=20Hz), it consists of 2 point calibration which is the BCG and weight. The weight signal as a reference for health monitoring can be used as supplement to decide whether object to sit on chair measurement system and thus further reduces power consumption.
Fig. 5 Block Diagram of BCG Wireless Transmit System The ballistic which is transmitted to PC and a weight data in PC is analyze using a VisualStudio2005 as a monitoring program is implemented. From the data sent from the wireless sensor nodes, the monitoring program is used to display data in real time, including data storage, review and further analyze in graph. The overall system design of BCG monitoring system using unconstrained method is shown in Figure 2.
3. Classifications Algorithm Over the past few years, tones of research have been developed on biomedical signal classification method. Among the classification method are single and multi channel template matching, amplitude separation, principle component analysis and etc. Most of the existing methods perform well but there is a drawback when the motion artifacts, non-linear disturbance for example electrical drifting, electronic devices noises happened. In this research paper, some methods are used for BCG features extraction and classification using wavelet transform, template matching, and adaptive threshold. The compactly supported Daubechies wavelet transforms are used to recognize the most important BCG features, signal classification and to decrease information redundancy. The algorithm is necessary to keep simple but accurate in order to fulfill the requirement of real-time and low processing load.
Fig. 4 Block Diagram of BCG Signal Processing Circuit Beside the signal processing circuit for the digital conversion, the ballistic signal is detected and wireless transmission using IEEE802.15.4 sensor network technique. A Zignee-compatible wireless sensor node (TIP710CM, Maxfor Co., Korea) was used for wireless transmission of the signal. The sensor node is controlled by the low power MSP430F1611 microprocessor, built-in ADC of 12-bit resolution which sampled the weight signal at 1 sample/sec and ballistic signal is 100 samples/sec sampling rate. In order to effectively sampled the wireless transmission of data packets, the length of the variable is adjust to configure the wireless transceiver and this avoid the frequent use of the low-power wireless communications. In addition, the wireless sensor node detects a signal from the weight by using the application of weight calibration technique in order to have a more accurate monitoring result. With the detection of changes in weight, it can determine whether the power management system is further designed for low-power operation. Then when it is ready to transmit, it create packet for transmission in packet creator process. Using Zigbee CC2420 radio chip, the packet is transmitted to PC Monitoring and further analysis in done using BCG Monitoring Program. The configuration of the implemented BCG wireless transmission system is show in Figure 5.
Pre-Processing ECG ECG
(Wavelet)
Adaptive Threshold
Wavelet (DB4)
BCG (Wavelet)
(Peak Detection)
BCG
Template Matching
BCG (Wavelet + T.M)
Fig. 6 BCG Signals Processing Components. As shown in Figure 6, the first stage in BCG signal processing procedure is pre-processing. Firstly, BCG segmentation stage to extract BCG cycles and further specifies the waveforms. However, the ECG signal in segmentation stage is only used for extracting of BCG templates and not for classification purpose. The measured BCG signal, which is the heart rate value, undergoes signal processing method for baseline wander noise cancellation 340
BCG Monitoring System using Unconstrained Method with Daubechies Wavelet Transform using DB4 Wavelet. The template matching to detect the signal by using the signal normalization and characteristic point detection is useful in signal reconstruction. Adaptive threshold was used to detect the heart rate from BCG signals. The performance evaluation of the implemented system for the ECG and BCG were measured simultaneously.
DB4 which are compactly supported Wavelet transforms are used in order to remove baseline of BCG and to eliminate the noise. The wavelets decomposition and reconstruction process which applied in this research was indicated in Figure 7.
3.2 Template Matching for Normalize 3.1 Wavelet Transform for Noise Cancellation
Template matching is generally used in object location tracking, in the form of an object as a fixed template, where it compare each part of the image and decides the similar degree in location of the object [9]. Template matching is used for the purpose of signal normalization and characteristic point detection. In this research, as shown in Figure 6, the ballistic signal is extracted from the ideal template and the template was calculated from the correlation coefficient between the input signals. Then, the numerical formula is obtained from the correlation coefficient.
During BCG recording, the references of ECG signal was recorded simultaneously from the chest of patients’ body where both recorded at the same sampling rate. In order to detect a heartbeat of same number, the ballistic signal must detect the J wave shape from BCG which is corresponding to R-component of the ECG signal. However, peak detection is impossible to detect in J wave shape from BCG even with signal processing method because of the size and frequency range is similar. In this research, the removal of ballistic signal and noise component from the underlying changes in line for the detection of preprocessing process was performed. Underlying function such as conversion and disjoint a signal for base function set to decompose the signal with a set of high-frequency band as a way to express the low-frequency band in a narrow window is used in the wide window. Therefore, the time-frequency analysis in the area of abnormal signal has favorable attributes. The BCG cycles are filtered to reduce the noise especially background noise and then normalized into the specific window range. The analysis is done using wavelet function and scale function. It double the functions where input signal with high frequency in the area by multiplying the frequency =h(k) and low frequency =g(k). It can further decompose into the frequency component with 60Hz power line noise, basis line fluctuations, noise, etc. Thus, noise cancellation of the wide selection is possible only to detect peak detection [9].
where
𝛹 𝑛 =
𝑘
ℎ 𝑘 𝛷 2𝑛 − 𝑘
(1)
𝛷 𝑛 =
𝑘
𝑔 𝑘 𝛷(2𝑛 − 𝑘)
(2)
𝑟𝑥𝑦 =
𝑁 [ 𝑛 =1 𝑁 {𝑥 𝑛 =1
𝑥 𝑛 −𝑥 𝑦 𝑛 −𝑦 ]
2 𝑛 −𝑥 }2 𝑁 𝑛 =1 {𝑦 𝑛 −𝑦 }
(3)
The template shown in Figure 8 is represents the process of matching. x is the input signal, y is the template signal, 𝑥 and 𝑦 is the mean value of x and y respectively. Correlation coefficient value close to 1 means that the signal is similar to the template and thus the template matching can normalized the size of the signal.
𝛹 is wavelet function and 𝛷 is scale function. Fig. 8 Template Matching.
3.3 Adaptive Threshold for Peak Detection Using the wavelet and template matching as pre-processing technique, the ballistic signal to detect the signal from the peak will be variable depending on the adaptive threshold value. The adaptive technique is applied in this research where it set 5 second periodically of the ballistic signal which is preprocessed and with 80% of the maximum threshold is set to be TH_a1 and 40% of the maximum threshold is set to be TH_a2. TH_a1 and TH_a2 are used within the window to perform the peak detection until it get the number of the same peak value (R1=R2). If the peak value is not same, the TH_a1 will reduce the threshold value and the TH_a2 will increase the threshold value and repeat the process. This process is repeating until it gets the R1=R2, and thus detection is completed. The algorithm describe above for adaptive threshold method is shown in Figure 9.
Fig. 7 Wavelet Decomposition and Reconstruction Process. In this research, wavelet DB4 using a total of 6 phases which decomposition cD3, cD4, cD5 and are able reconstruction the signal by removing the baseline signal.
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Inter. Conf. on IML 2009
Fig. 11 Screenshot of BCG Monitoring Program
4.2 Preprocessing Performance Analysis In this part, performance of preprocessing techniques including Wavelet Transform and Template Matching is evaluated in this experiment. Adaptive threshold method is used in detection. For the peak detection purpose, the first 20 second ballistic signal is measured and Adaptive Threshold method is applied only as shown in Figure 12(a). However, there is no preprocessing process undergoes in the signal. The red colour circle means peak is detected and the black colour cross symbol means peak is not detected. A total of 8 peaks are not detected from the range of 0 to 20 s. It can conclude that the performance is bad when there is no preprocessing method used. In Figure 12(b), using the same ballistic signal, it undergoes adaptive threshold method for peak detection and wavelet transform for preprocessing part. From the result, there are total of 4 black colour cross symbol meaning peak is not detected from the range of 0 to 20s. It explained that the performance is better as compare to Figure 6(a) where the number of peak not detected is reduce to half. In Figure 12(c), preprocessing of wavelet transform and template matching methods are used. It shown the performance is very good where there all the peaks are detected. For performance evaluation and comparison purpose, ballistic signal undergoes signal processing algorithm as purposed in this paper. The ballistic signal is performed and ECG is measured simultaneously for the peak detection result as shown in Figure 12(d). From the observation of experimental results, it shown that by applying Wavelet Transform and Template Matching as preprocessing method, it provides better performance in heart rate detection. Thus, it is essential to have preprocessing method as purposed in this paper.
Fig. 9 Adaptive Threshold Algorithm.
4. Experiments and Results 4.1 Implemented BCG System In this research, a chair style ballistic measurement system to monitor the activity of the heart was implemented. The actual implementation of the chair style ballistic measurement system is shown in Figure 10. A BCG transmitter attached to the chair style ballistic measurement system. The purpose of BCG transmitter is to sense the signal and transmit to PC via Zigbee 2.4 GHz wirelessly. The instrumentation system of the ballistic signal is measured at the PC real time where a PC program is implemented. The actual ballistic monitoring screenshot of the PC program was shown in Figure 11.
(a) Heart Rate Detection from Original BCG
Fig. 10 Actual BCG Measurement System.
(b) BCG Heart Rate Detection after Wavelet Transform
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BCG Monitoring System using Unconstrained Method with Daubechies Wavelet Transform
(c) BCG Heart Rate Detection after Wavelet Transform and Template Matching
(b) BCG Heart Rate detection using Wavelet Transform and Template Matching Fig.13 Heart Rate Detection Results. (d) ECG Heart Rate Detection after Wavelet Transform Fig.12 Preprocessing Technique Results.
From ECG signal which have the same hear beat number which detected and wavelet template matching from ballistic signal, the correlation coefficient result is analysis. The correlation coefficient is 0.8534 which is the less than 1 and in the interval of 0 to 1. The analysis showed a significant statistical same (p-value
