On-line Monitoring and Analysis of Bioelectrical Signals

Procedia Computer Science 42 ( 2014 ) 365 – 371

Available online at www.sciencedirect.com

1877-0509 © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).Peer-review under responsibility of the Center for Humanoid Robots and Bio-Sensing (HuRoBs) doi: 10.1016/j.procs.2014.11.075

ScienceDirect

International Conference on Robot PRIDE 2013-2014 - Medical and Rehabilitation Robotics and Instrumentation, ConfPRIDE 2013-2014

On-line Monitoring and Analysis of Bioelectrical Signals

Anas M.N.*, A.N. Norali, W. Jun

Bioinstrumentation Laboratory ,School of Mechatronic Engineering, Universiti Malaysia Perlis, Arau, 02600, Malaysia

Abstract

On-line signal processing and feature extraction are limited by sophisticated and highly expensive instrumentation. Many clinical instruments as well as research purpose, such as electrocardiogram, electromyogram and encephalogram instrumentation system neglect certain critical of signal parameters, where most of the time, these parameters are critical to be analyzed in real-time to acquire a fast result. Furthermore, the result using conventional devices is relied to the highly trained person which sometimes could lead to the delayed of medical interpreting diagnostic and cause a high risk to the patient. This paper, the real-time application of bioelectrical signal processing and analysis is developed to assist users to get preliminary diagnosing results. The algorithm of these bioelectrical signal measurement systems is developed, which provide better interpretation of signal parameters in certain conditions. The overall system is comprised of hardware, mainly the bioelectrical circuitry as well as software for analysis, data logging and user interfacing. The developed system prototype will be able to monitor and analysis in real-time condition, which could be used for many applications. © 2014 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Center for Humanoid Robots and Bio-Sensing (HuRoBs).

Keywords: bioelectrical signals; electromyogram; electrocardiogram; electroencephalogram; measurement system; real-time

1. Introduction

Bioelectrical signal, namely; electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), electrooculogram (EOG), Electrorentinogram (ERG) is the typical measurement signals which apply not only in the clinical application, but also used in the research field. These signals could be monitored and recorded the electrical

* Corresponding author. Tel.: +6-013 4400 858

E-mail address:[email protected]

© 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).Peer-review under responsibility of the Center for Humanoid Robots and Bio-Sensing (HuRoBs)

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366 M.N. Anas et al. / Procedia Computer Science 42 ( 2014 ) 365 – 371

activity of heart, muscle, brain and eyes potentials respectively using dedicated instrumentation or device [1]. The measurement of some physical quantity could be useful in order to obtain a pre-diagnosis result or for a treatment. Most of medical instrumentation is based on electronic systems compared to the mechanical systems. A typical electronic medical instrumentation would consist of basic functions and components such as transducers or sensors, signal conditioners, and display system from the measurand which could be executed either in-vivo or in-vitro based upon the application requirements [2].

Numerous existing measurement systems such as ECG, EMG and EEG are of off-line analysis, worst still, some system did not support multiple measurements, costly, not portable or inflexible, large or bulky system, and sometimes, only offer limited or special application purpose only. Moreover, measurement data will be analyzed by experienced physicians and therefore it is time-consuming. The human interpretation is not always proper and have limitations due to many contributing factors.

Many researchers, practitioners and inventors discussed the need of intelligence or expert system in monitoring, alarming and also decision making in many medical conditions such as in the critical care unit, operating theatre, early detection diagnosis, treatment, and many others more [3,4]. Currently, the challenges in the modern medicine required the solving huge and complex data or signal in very short time where the classic conventional method is unsuitable. Hence, an intelligence processing such as artificial intelligence techniques are widely being explored in medical field's application [4]. The demand of the medical instrumentation expert-system is higher in order to get quick diagnosis and proper treatment. Recent technologies have emerged to support this needed [5].

This paper is not intended to introduce intelligence or an expert decision-making of a medical device system. The objective of this project is to develop an instrumentation system that could be used for the on-line monitoring and analysis of bioelectrical signals, ECG, surface EMG and EEG. The on-line analysis is recommended to deliver better understanding and less interpretation complexity of information about the measurement result compared to certain conventional instruments or devices. The developed prototype system comprise of a hardware and software which will be further explained in the next chapter of this paper.

2. Methods and Materials

2.1. Bio-Potential Circuitry

The bioelectrical signals will be amplified using an instrumentation amplifier configuration sometimes recognize as bio-amplifier. AD620 amplifier form Analog Device is selected because it is suitable for medical instrumentation, especially in biosignals application, and a cost-effective device, very-low power consumption, high accuracy of amplification, broad frequency range, high input impedance, high common mode rejection ratio (CMMR), ultra-low noise and many more claimed by the manufacturer. This signal conditioning circuit used to amplify the small generated signal and as well as to limit output voltage to a certain level which able to interface to the data-acquisition device. The values of 300, 200 and 400 of gain amplification is selected for ECG, EMG and EEG respectively.

The design circuit is simulated first using NI Multisim software and the result shows the simulation of the designed bio-amplifier circuitry performance is well accepted for the purpose of this application. However, simulation environment is different to the real-world condition and it will be discussed in the discussion section. Fig. 1 shows the circuit hardware developed in-lab.


Fig. 1. The developed signal conditioning circuit on printed circuit board

This circuit is developed on the printed circuit board (PCB) which provides a good electrical grounding for the better performance and noise immunity. The driven right leg (DRL) show in the Fig. 1 often used with bio-potential differential amplifiers to reduce common mode voltage for the ECG measurement.

2.2. Data Acquisition and Electrodes

The data acquisition (DAQ) device model PCI-6251 from National Instrument is used to convert analog signal from the developed circuit to digital data for the use of PC for further signal processing. The bio-signals are sampled to 6000 samples per seconds (6000 S/s) and data are transmitted to the computer through a parallel PC interface connection.

The pickup electrode used for this project to sense the bioelectrical signal voltage is a surface type electrode with silver-silver chloride. The silver-silver chloride electrode is non-polarize or minimal polarization, which reduced the error of a motion artifact. This electrode also has a thin layer of an electrolyte gel to establish a good contact between the electrode and skin surface. The ECG, EMG and EEG surface electrode used for this system is a disposable type (from Kendall/Tyco ARBO).However, this measurement only focused on simulator's signals because of the safety purposed. Measurement using real bio-signals use only to validate the evaluation of the prototype system.

2.3. Software and Interfacing

The LabVIEW software is use in the development of signal processing, analysis, and algorithm with user interface. On the signal filtering section, frequency of interest for each ECG, EMG and EEG is different. ECG filtering frequency is in the range of 0. 05Hz to 150Hz, EMG is in the range of 20-2000Hz and EEG is 13-30Hz (beta frequency only). Third-order Butterworth IIR filter is selected because of fast response and suitable for bioelectrical signal application. An IIR filter with moving average is also implemented in this system to get the further signal smoother after the first stage of band-pass filtering.

In the event of signal measurement, the signal features such as voltage peak, frequency, minimum voltage, time and many more is extracted and analyze to provide some important information about the measurement signal.

For the ECG signal, features such as amplitude, wave-time interval and also wave-time position. These features are common in the analyzing the ECG signal [6-8]. Fig. 2 shows the block diagram of the developed algorithm used for the ECG measurement.

EMG ECG EEG EMG

Input

Output

DRL


Fig. 2. The signal measurement algorithm block diagram of ECG

There are many techniques of analysis of EMG features extraction and analysis such as in the time-domain,

frequency-domain and also time-frequency domain. The frequency-domain such as mean power frequency and median power frequency are used in this work to determine the EMG signal analysis. These features is widely being used to monitor muscle conditions [8-11]. In this analysis, the measurement result is provided on-line, which is better than conventional software, which sometimes needs further off-line analysis. Fig. 3 shows the block diagram of the developed algorithm calculating the Mean Power Frequency (MNF) and Median Power Frequency (MDF).

Fig. 3. General block diagram of EMG features algorithm

The EEG Signal measurement is difference from EMG or ECG. Many researchers are interest in the frequency

band of EEG and analyze the signal using artificial intelligence for signal classification method [13]. The amplitude and frequency are computed using Fast Fourier Transform and also power spectral density (PSD). Normally, in EEG analysis, classification methods are always selected to relate the brain activity with a certain condition [6,14]. Fig. 4 shows the block diagram algorithm for measuring amplitude, frequency and PSD of EEG signals.

Fig. 4. General block diagram of EEG features algorithm

2.4. Bioelectrical Signal Simulation

The system compromising a bi-polar single channel signal measurement technique. To replicate the human bioelectrical signals, a hardware and software simulator is use. ECG simulation signal will use a hardware simulator (arrhythmia simulator model 10A, from Datatrend), EMG and EEG signals will simulate using biosignals generator software (National Instrument). The ECG simulator will be connected directly to the ECG electrode circuit. EMG and EEG are measured using software signal simulation. The digital signal will be converted to an analog signal using PCI-5269 device and output from the device will be connected to EMG and EEG electrode's circuit. Fig. 5 shows the overall measurement system setup diagrams if needed. Sampling time and samples of measurement could be changed from the graphical user interface (GUI). Fig. 5 shows overall developed system with support testing.

Calculate P,QRS,T Wave and Heart Rate

(Mean, Standard Deviation, Time,

Amplitude)

Signal Pre-Processing (Band-Pass,

Notch Filtering) Signal Average

Rectification

Calculate ECG Signal

(Amplitude, Peak, Time,

Slope and Width)

Signal Wave Detection

(P,Q,R,S,T)

GUI Display

Signal Pre-Processing

(Butterworth Band-Pass and

Notch)

Computing the

short-time Fourier transform

(STFT) Based Spectrogram

Calculate the

Mean and Median Instantaneous

Frequency from STFT

GUI Display

Signal Pre-Processing

(Butterworth Band-Pass and

Notch)

Computing the

Fast Fourier transform (FFT)

Calculate the

Power Spectral Density (PSD) and Amplitude

GUI Display


Fig. 5. The overall block diagram of developed system and testing

The GUI is developed to display the on-line measurement and analysis of bioelectrical signal activity on PC screen. All measured data could be stored into the PC for further analysis if needed. The number of sampling time and samples of data measurement could be a change from the GUI.

3. System Evaluation

The heart rate simulated signal is calculated around 78.66 bpm with QRS features amplitude mean has been identified less than 6mV and meantime calculated is around 0.156s. For PR and QT signals, the interval is 0.1s and 0.4s respectively. Fig. 6 shows the time-domain waveform and also ECG parameters on the user interface.

Fig. 6. The ECG GUI

Fig. 7 shows the example result obtained from EMG simulated signal in a time-domain and time-frequency (spectrogram) graph. Two contractions-relaxations of muscle are simulated in 10 seconds. Bottom left of the figure is the on-line spectrogram for the EMG output waveform. The MNF and the MDF is calculated real-time are shown on the GUI. Spectrogram, MNF and MDF are the signal features extracted from EMG. In this example, the MNF and MDF show frequency range during contraction simulation signal within ranges of 100Hz until 700Hz with amplitude ranges from 10 mV to 200 mV. This spectrogram provides details of time, frequency and amplitude of the signal measured.

ECG Circuit ECG Signal Simulator

EMG Circuit

EEG Circuit

Data Acquisition

Device (DAQ)

Signal Pre-Processing and

Features

Graphical User Interface

Signal Converter

(DAC)

Digital EMG Signal

Simulator

Digital EEG Signal

Simulator

Digital EEG Signal

Simulator


Fig. 7. The EMG signal with spectrogram and features

The EEG result shows the calculated mean value is 30mV and dominant frequency ranges between 13Hz to 30Hz calculated in real-time using Fast Fourier Transform. This frequency is identified as beta band frequency. Fig. 8 shows the result obtained from simulated EEG signal to the developed system. EEG Beta signal is the rhythm of simulated signal in this project. As shown in Fig. 8 EEG spectrum, the beta rhythm is dominant and active in frequency range of 14 to 24Hz (87% of Beta signal).

4. Discussions

The overall performance of the measurement system is adequate using bio-signals simulator. In the measurement of ECG signal, features extracted using the developed algorithm demonstrate that this system is capable of not only monitoring the signal, moreover, the on-line features processing, providing a fast result compared to the conventional clinical instrument in determining ECG conditions of a patient such as the time-slope, the PQRS wave width and also signal peak value. However, the system tested on a simulation signal technique as an alternative of measuring the real condition of human ECG which need to be concerned using this prototype due to human safety ethics. On the other hand, the EMG measurement result also deliver a rapid analysis of MNF and MDF. These frequencies parameters are always used in the EMG analysis for monitoring several details such as motor firing frequency, identifying muscle spectral, time response, fatigued level and many more during muscle contractions-relaxation activity. However, for EEG measurement, the RMS, mean amplitude and spectral monitoring not well

Fig. 8. The EEG Signal GUI


defined in this system yet. The voltage and the signal spectral have discrepancies from the input simulated value. This could be because of, the EEG signal is well known very small amplitude, short-time voltage spiking and highly sensitive to the environment noise as well as instrument noise.

5. Conclusions

There are various factors affecting the performance of the developed measurement system such as signal conditioning circuit, the conversion of analog to digital and digital to analog using DAQ device and also the developed algorithm for the on-line signal analysis. The hardware bio-amplifier signal conditioning and circuit could be improved by employing higher-performance components, including both active and passive components included in the circuit. These components will determine how well the circuit measures the small bioelectrical signal in noisy condition. Some modification on the safety features in the front-end part such as a high-voltage protection circuitry is suggested. For the acquisition and conversion of the signal, a higher-bit of ADC conversion (higher resolution) is recommended to ensure that the analog signal sample is almost to a zero offset error. The suitable resolution for measuring bioelectrical signals is using 24-bit ADC or higher. Higher resolution will provide the ability of a smaller voltage change measured by ADC. Another suggestion, the feature's extraction of ECG and EMG algorithm could be improved by adding a frame or segmentation to the signal during each event to provide more clean signal and lower the mislead analysis and interpretation. From a product safety and human aspect, the developed prototype system is far from medical device regulation and requirements, which are yet inadequate use to the human. However, with certain improvement and modification, this prototype could be implemented in many applications in the future.

Acknowledgements

This project is funded by Research Acculturation Grant Scheme, Ministry of Education Malaysia.

References

[1] Webster, J. G. (1973). Medical instrumentation. Application and Design, Houghton Mifflin Company, Boston. 197& g [2] Khandpur, R. (2004). Biomedical instrumentation: Technology and applications. McGraw-Hill Prof Med/Tech. [3] Uckun, S. (1994). Intelligent system in patient monitoring and therapy management.

International Journal of Clinical Monitoring and Computing, 11(4), 241-253. [4] Mora, F. A., Passariello, G., Carrault, G., & Le Pichon, J. P. (1993). Intelligent patient monitoring and management systems:

a review. Engineering in Medicine and Biology Magazine, IEEE, 12(4), 23-33. [5] Suzuki, K. (Ed.). (2011). Artificial neural networks-Methodological advances and biomedical applications. InTech. [6] De Chazal, P., O'Dwyer, M., & Reilly, R. B. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat

interval features. Biomedical Engineering, IEEE Transactions on, 51(7), 1196-1206. [7] Israel, S. A., Irvine, J. M., Cheng, A., Wiederhold, M. D., & Wiederhold, B. K. (2005). ECG to identify individuals. Pattern recognition,

38(1), 133-142. [8] Bazan, V., Bala, R., Garcia, F. C., Sussman, J. S., Gerstenfeld, E. P., Dixit, S., Callans D.J, Zado E, Marchlinski, F. E. (2006). Twelve-lead

ECG features to identify ventricular tachycardia arising from the epicardial right ventricle. Heart Rhythm, 3(10), 1132-1139. [9] Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert Systems

with Applications, 39(8), 7420-7431. [10] Phinyomark, A., Limsakul, C., & Phukpattaranont, P. (2009). A novel feature extraction for robust EMG pattern recognition. arXiv preprint

arXiv:0912.3973. [11] Ahmad Nasrul, N., & Som, M. (2009) Surface Electromyography signal processing and application: a review. Proceedings of

the International Conference on Man-Machine Systems. [12] Chowdhury, R. H., Reaz, M. B., Ali, M. A. B. M., Bakar, A. A., Chellappan, K., & Chang, T. G. (2013). Surface Electromyography Signal

Processing and Classification Techniques. Sensors, 13(9), 12431-12466. [13] Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007). A review of classification algorithms for EEG-based brain

computer interfaces. Journal of neural engineering, 4. [14] Subha, D. P., Joseph, P. K., Acharya, R., & Lim, C. M. (2010). EEG signal analysis: A survey. Journal of medical systems, 34(2), 195-212.

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On-line Monitoring and Analysis of Bioelectrical Signals