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Bio and Electrocardiogram

In: Computers and Technology

Submitted By shivsm
Words 3286
Pages 14
Shivanesan S M. 1, Pradheep M. 1, Sharath K. 1, Aravind Prasad. 1, Manoj M. 1
Ganesan M. 2

Abstract- Electrocardiogram is the recording of the electrical potential of heart versus time. The analysis of ECG signal has great importance in the detection of cardiac abnormalities. In this paper we have dealt about the removal of noises in ECG signals and arrhythmia classification of the signal. The inputs for our analysis is taken from MIT-BIH database (Massachusetts
Institute of Technology Beth Israel Hospital database). The denoising is done through wavelet transform and thresholding.
Confirmatory tools such as Poincare plot and Detrended
Fluctuation Analysis (DFA) are used to find out the healthiness of the signal. Then Support Vector Machine (SVM) is used to find out what type of arrhythmia is present in the signal.
Keywords- Classification, DFA Electrocardiogram, MIT-BIH database, Poincare, SVM , Wavelets.

In today’s environment there has been lot of threats due to heart disease and no proper diagnosis With the recent developments in technology, physicians have powerful tools to observe the working of the heart muscle and thus to establish their diagnosis. Among cardiovascular examinations, electrocardiogram (ECG) analysis is the most commonly used and very effective too. This is due to the fact that ECG presents useful information about the rhythm and the electrical activity of the heart. Thus, it is used for the diagnosis of cardiac arrhythmias worldwide. For effective diagnostics, the study of the ECG signal must be carried out for several hours. For this reason, researchers have been interested in enabling computers to classify the abnormal ECG signals. During the last five decades the analysis of ECG signals evolved from simple visual examinations to totally automated analysis [1, 2, 3].
A noise free signal is necessary in any type of signal analysis and classification [4]. Several algorithms have been proposed for denoising of the biomedical signals, especially
ECG signals. Advancement have been done to various fields for denoising and classification of ECG signals. ECG signal is one of the bio signals that is considered as a non-stationary signal and needs a hard work to denoise [5, 6]. An efficient technique for such a non-stationary signal processing is the wavelet transform. The wavelet transform can be used as a decomposition of a signal in the time frequency scale plane.
There are many applications of wavelet transform such as sub-band coding data compression, characteristic point’s detection and noise reduction. In order to reduce the noise of
ECG signal many techniques are available like digital filters
(FIR or IIR), adaptive method and wavelet transform thresholding methods. However, digital filters and adaptive methods can be applied to signal whose statistical characteristics are stationary in many cases. Recently the

wavelet transform has been proven to be useful tool for nonstationary signal analysis.
Thresholding is used in wavelet domain to smooth out or to remove coefficients of wavelet transform. The denoising method that applies thresholding in wavelet domain has been proposed by Donoho as a powerful method [7].
Before classification of these signals to find out the arrhythmia, the need to know whether the signals is abnormal or not becomes necessary. To find out the healthiness of the signal we used two methods Poincare plot and Detrended
Fluctuation analysis (DFA) [8, 9, 10]. After confirming with the abnormality of the signal simple Support Vector Machine
(SVM) is used to train the signals and find out the exact arrhythmia in the ECG signal. In [11], two classification systems based on the support vector machines (SVM) approach are implemented.
1Shivanesan S M, Pradheep M., Sharath K, Aravind Prasad, Manoj M. are students of ECE department, Amrita Vishwa Vidyapeetham, Coimbatore
641112, India. e-mail:
2Ganesan M., (Assistant Professor) is with Department of ECE, Amrita
e-mail: The signals for analysis are taken from the MIT-BIH database [12]. We have discussed about how wavelet can efficiently denoise the obtained from the database. The
Poincare plot and Detrended fluctuation analysis are done in order to confirm the abnormality of the signal. The classification of the ECG signal is based on the major arrhythmias that causes threats to human life viz. Bradycardia
Tachycardia, cardiac and ventricular. SVM trains, classifies and finds out what type of abnormality is present.
The article is organised as follows: section II tells about the principle behind wavelet transform and thresholding. Section
III describes about the Poincare plot and Detrended fluctuation
Analysis (DFA). Section IV tells about the SVM classification of arrhythmia. Section V discusses about the results obtained and tabulation. We present our conclusions and future work in the section VI. The overall block diagram is depicted in Fig. 1.

A. Discrete Wavelet Transform:
The wavelet transform is similar to the Fourier transform.
For the FFT, the basis functions are sines and cosines. For the wavelet transform, the basis functions are more complicated called wavelets, mother wavelets or analysing wavelets and scaling function. In wavelet analysis, the signal is broken into shifted and scaled versions of the original (or mother) wavelet.
The fact that wavelet transform is a multiresolution analysis

makes it very suitable for analysis of non-stationary signals such as the ECG signal [13].
The discrete wavelet transform (DWT) is an implementation of the wavelet transform using a discrete set of the wavelet scales and translations obeying some defined rules. In other words, this transform decomposes the signal into mutually orthogonal set of wavelets. The DWT can be realized in terms of high pass and low pass filters. The approximation properties of filter banks and their relation to wavelets are presented in the paper [14]. The output of the low pass filter gives the approximation coefficients and the output of the high pass filter gives the detailed coefficients.
MIT-BIH database Denoising by wavelets

Feature extraction PP & DFA

Arrhythmia classification using SVM

Fig. 1. Overall block diagram
The DWT of an discrete signal x(n) of length M-1 is given in the below equation.
() = ∑ ( , ) 0, () + ∑∞
=0 ( , ) , () (1)
Here Wφ( , ) and Wψ( , ) are called the wavelet coefficients. ,() and 0,() are orthogonal to each other.
Hence we can simply take the inner product to obtain the wavelet coefficients.
∑ () 0, [] [0 , ] =


∑ () 0, [] [, ] =

The coefficients Wφ( ,k) are called the approximation coefficients and the coefficients Wφ( , ) are called the detailed coefficients. The DWT can be realized in terms of high pass and low pass filters. The approximation properties of filter banks and their relation to wavelets are presented in the paper
[14]. The output of the low pass filter gives the approximation coefficients and the output of the high pass filter gives the detailed coefficients. Computation of the wavelet coefficients at every possible scale is a fair amount of work and it generates an awful lot of data. Selection of a subset of scales and positions based on powers of two (dyadic scales and positions) results in a more efficient and accurate analysis.
B. Wavelet Decomposition and Thresholding:
The DWT decomposes the signal into approximate and detail information as discussed. The wavelet decomposition process can be iterated, so that one signal is broken down into many lower resolution components. This is called the wavelet decomposition tree. x(n) cA1 cA2 cAn

cD1 cD2 In this proposed method, the corrupted ECG signal x(n) is denoised by taking the DWT of raw and noisy ECG signal. A family of the mother wavelet is available having the energy spectrum concentrated around the low frequencies like the
ECG signal as well as better resembling the QRS complex of the ECG signal. We have used daubaches wavelet, which resembles the ECG wave. In discrete wavelet transform
(DWT), the low and high frequency components in x(n) is analysed by passing it through a series of low-pass and highpass filters with different cut-off frequencies.
This process results in a set of approximate coefficients (cA) and detail coefficients (cD). To remove the power line interference and the high frequency noise, the DWT is computed to level 4 using daubaches16 (DB16) mother wavelet function and scaling function. Then the approximate coefficients at level 5 (cA5) are set to zero. The residue of the raw signal and the approximate noise is obtained to get noise free ECG signal. The method is based on taking the discrete wavelet transform (DWT) of a signal, passing this transform through a threshold, which removes the coefficients below a certain value. = √2 ∗ log()
Where, T is threshold, n is the number of samples and is the noise standard deviation. Thresholding is applied at every loop to smooth out the signals and denoise the raw data
Classification to find out the arrhythmia becomes unnecessary when the ECG signal is normal and does not contain any sort of abnormalities. To visually verify and find out whether the signals are normal or not we use two highly accurate methods.
The Poincare plot and the Detrended Fluctuation analysis
A. Poincare Plot:
The Poincare plot of RR intervals is one of the techniques used in heart rate variability (HRV) analysis. It is both a useful visual tool which is capable of summarizing an entire RR time series derived from an electrocardiogram in one picture, and a quantitative technique which gives information on the longand short-term HRV. A Poincare plot of RR intervals is composed of points ( , +1), that is each point in the plot corresponds to two consecutive RR intervals [15]. The resulting cloud of points is usually characterized by its length
(SD2) along the line of identity and its breadth across this line
(SD1). The visual inspection of the formed shapes of the
Poincare plot of RR intervals is a widely used method for analysing the quality of recorded ECG signals and to identify premature and ectopic beats, as well as technical artefacts. The plot of a healthy person will have a comet shaped structure along the line of identity [16]. Also the ratio of the Poincare descriptors (SD1, SD2) should be high for a healthy person.
Let RR, x and y vectors be defined as = (1, 2 … … . , +1 ), = (1 , 2 , … . . , ) = (1 , 2 , … … , ) = (1 , 2 , … . , , ) = (2 , 3 , … … , +1 ),


Fig. 2. Decomposition tree


1 = √ ∑ =1( 1 )2



2 = √ ∑ =1( 2 )2

1 =

( −̅ )−( − )



2 =

( −̅ )+( − )


The over bar stands for mean.
By calculating SD1 and SD2 from the eq. (6) & (7) and finding the ratio of the descriptors we could comment on the abnormality. But the ECG signals involve more than a b than two classes, so we need a classifier that is more than a binary classifier [16,
17]. The widely practised ones are one-against-all (OAA) and the one-against-one (OAO) strategies. The one against one
constructs 2 decision functions for all the combinations of class pairs. Experimental results in [18] indicate that the oneagainst-one is more suitable for practical use. (More details appeared in [18]). We use OAO for ECG multi class classification. B. Detrended Fluctuation Analysis (DFA):


This is also a plot analysis by which we could visually see and comment on the abnormality of the given ECG signal. The plot here is a double logarithmic plot. Its log F(n) versus log(n). The main objective of DFA is to extract the extrinsic fluctuations in order to allow the analysis of the signal’s variability associated exclusively with autonomic control. The integrated signal y(k) is then segmented into multiple windows of length n. For each of these windows, a least-squares firstorder approximation (a line segment) is calculated, representing the “trend” of that segment of the signal that has been found out. The trend signal yn(k) in Eq. (10), formed by the line segments, is an approximation to the integrated signal y(k). 1

() = √ ∑ =1 ()2 ,


A. Wavelet denoising:
The denoised signal is evaluated on the basis of SNR and correlation factor. Records or samples from MIT-BIH database were used. The tabulation below shows the records used and the SNR of the filters.DB16 was found to denoise effectively. ∑

2 () 2 ()

= 10 ∑ =0


(n): the deformation in reconstructed ECG signal. (n): the original signal.
Table 1. SNR values of various records and filters


Where () is the duration of the i-th RR interval, is the mean interval, and k is the current output sample timeindex. The slope of the line (log F(n)& log (n)) gives us a coefficient called α which gives us the HRV fluctuations. The normal plot of DFA is shown in the Fig 3.





















() = () − ().

() = ∑ =1[() − ]






Table 2. Correlation values

The support vector (SV) machine is a new type of learning machine. It is based on statistical learning theory. Support vector machines (SVMs) are becoming popular in a wide variety of biological applications. The main use of SVM is classification. They were used in cancer cells classification and now SVM’s are used widely in biomedical signal analysis.
Here we have employed SVM to classify the ECG signal and to find out the arrhythmia present in it. SVM finds the optimal separating hyper plane (OSH) with the minimum errors. The linear separation hyper plane is in the form of
() = + .




Fig 3. Normal DFA plot.




















From the Table 1 and Table 2 we found out that DB16 denoised the signal efficiently and can be used for effective denoising of ECG signals.
B. Poincare Plot:
The Fig 4 below shows the Poincare plot of the record 228 of the MIT-BIH database. From the figure we could see that

the shape is not comet shaped along the line of incidence
(LOI) as discussed in section three. So just by looking at graph of the Poincare plot we can tell that the signal is abnormal and contains some kind of artefact.


Table 3. SVM arrhythmia classification
Normal/abnormal Type of arrhythmia










The results shown above are the outputs of the record 228.
Similarly other records from the MIT-BIH database were run and the outputs were verified and type of arrhythmia present in it was found and analysed. The Poincare plot and Detrended
Fluctuation Analysis (DFA) are highly accurate in determining the abnormality of the ECG signal. It’s fast and just by looking
Fig 4. Poincare plot of record 228 at the output plot of the ECG signal we can comment on the abnormality of the signal. This paper throws light on the
SD1= 317.2 method of denoising that can efficiently remove various noises
SD2= 317.0 like baseline wander, power line interference etc. from the ECG
= 1.0062 signal. We have also seen that the best filter for denoising ECG
signals was DB16 which has got the maximum number of
The ratio of SD1 and SD2 is very low and hence the signal is vanishing moments. The higher the vanishing moments higher will be the amount of denoising but not at the cost of losing the abnormal. original data from the signal. SVM has always been an accurate classifier and one against one (OAO) strategy is used for
C. Detrended Fluctuation Analysis: multiclass classification of ECG signals. Keeping in mind that
The fig 5 below shows the DFA plot of the record 228 of the ECG being a non-stationary signal, we have designed the entire
MIT-BIH database. Comparing the Fig 5 with the normal plot block that can handle the non-stationary ECG signal.
The inputs to the whole setup was taken from the MIT-BIH from the Fig 3 we see the plot is disfigured and the abnormality database and not real time ECG signals. In the future ECG is confirmed. signals can be acquired from the human and can be sent as the inputs and the type of arrhythmia can be found. Since it is going to be real time acquisition of data, the amount of noise present will be more, so care must be taken while denoising the real time signals.


The alpha value is found from the slope of the line formed in the plot.

Henzel N., and J. L ski, 1999. “Analiza sygnalu HRV w podpasmach widmowych. Biocybernetyka i In ynieria Biomedyczna,” PAN, pp


Merri M., M. Alberto M. and A. J. Moss, 1993. “Dynamic analysis of ventricular representation Duration from 24-hour recording”. IEEE
Transactions on biomedical engineering, Vol. 40, No. 12.


Luca Mainadri T., A. M. Bianchi and S. Baselli, Cerutti, 1995. “Poletracking algorithms for the extraction of time-variant heart rate variability spectral parameters,” IEEE Transactions on biomedical engineering, Vol.
42, No.3. pp: 20-31.


Fig 5. DFA plot of record 228

Henzel N., and J. Leski, 1999. “Efectywna obliczeniowo metoda analizy acyklicznych zdarza przy pomocy technik wieloczstotliwociowych,” XI konferencja Biocybernetyka i Inzynieria
Biomedyczna, pp 188-122.

Leski J., 1991, “Detectja zespolów QRS dla zaklóconych signalów
EKG,” Post. Fiz. Mid., 26, 3-4 PL ISSN 0137-8465.


Shrouf A. 1994. “The lineal prediction methods analysis and compression”, PhD thesis of lsk Technical University in Gliwice.

α = 0.2783 (Anti-correlated)
D. Support Vector Machine:
The signals are trained and classified by the SVM algorithm as discussed earlier. The table below shows the arrhythmia present in the record 228.


D. L. Donoho, 1991. “De-noising by soft thresholding”, IEEE
Transaction on Information Theory, Vol. 41, pp. 613–627, May 1995


M. Brennan, M. Palaniswami and P. Kamen, IEEE Trans. Biomed.
Eng. 48, 1342 (2001).


M. Brennan, M. Palaniswami and P. Kamen, Am. J. Physiol. Heart
Circ. Physiol. 283, H1873 (2002).


K. Hu, P.C. Ivanov, Z. Chen, P. Carpena and H.E. Stanley, “Effects of trends on detrended fluctuation analysis,” Physical Review E, 2001, vol. 64, 011114.


S. Osowski, T. Linh, and T. Markiewicz, “Support vector machinebased expert system for reliable heartbeat recognition,” IEEE
Transactions On Biomedical Engineering, vol. 51, no. 4, pp. 582–589,
Avril 2004.



Mahmoodabadi and S. Ahmadian, “ECG feature extraction based on multiresolution wavelet transform”, Proceedings of the IEEE 27th
Annual Conference on Engineering in Medicine and Biology, pp.
3902–3905, Shanghai, China, January 2005.


M. Vettereli, “Wavelets, approximation and compression”, IEEE
Signal Processing Magazine, vol. 18, no. 5, pp. 59–73, August 2001.


Brennan M, Palaniswami M and Kamen P 2001 “Do existing measures of Poincar´e plot geometry reflect nonlinear features of heart rate variability?” IEEE Trans. Biomed. Eng. 48 1342–47


F. Melgani and L. Bruzzone, “Classification of hyper spectral remote sensing images with support vector machine,” IEEE Trans. Geosci.
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...INTRODUCTION Biotechnology is one of the innovative branches of science. Biotechnology has created new revolutions in this era by contributing industries, medical sciences, food technologies and genetics. "Biotechnology is basically defined as the use of living organisms, their parts and their biochemical processes for the creation of beneficial products." Bio-technology has its roots in the distant past and has a large, highly profitable, modern industrial outlets of great value to society for e.g. the fermentation, bio-pharmaceutical and food industries. The main reasons must be associated with the rapid advances in molecular biology, in particular, recombinant DNA technology, which is now giving bio-scientists a remarkable understanding and control over biological processes. Some Technologies used in Biotechnology: 1. Bioprocessing technology * The use of bacteria, yeast, mammalian cells and/or enzymes to manufacture products * Large scale fermentation and cell cultures, carried out in huge bioreactors, manufacture useful products * Products: Insulin, vaccines, vitamins, antibiotics, amino acids, etc. 2. Monoclonal antibodies (MCAb) * Definition: Producing antibodies for medicine by cloning a single cell * MCAb are used for Home Pregnancy tests * Used to detect cancer (they bind to tumor cells) * Used to detect diseases in plants and animals and environmental pollutants 3. CELL...

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...Nonvolatile BIOS memory - CMOS [pic] CMOS Battery in a Pico ITX motherboard Non-volatile BIOS memory refers to the memory on a personal computer motherboard containing BIOS settings and sometimes the code used to initialize the computer and load the operating system. The non-volatile memory was historically called CMOS RAM or just CMOS because it traditionally used a low-power CMOS memory chip (the Motorola MC146818, or one of its higher-capacity clones), which was powered by a small battery when the system power was off. The term remains in wide use in this context, but has also grown into a misnomer. The non-volatile BIOS storage in contemporary computers might be in an EEPROM or flash memory chip and not in a volatile CMOS RAM. In these cases, the battery back-up is meant to keep the RTC chip synchronized. The NVRAM normally has a storage capacity of 512 Bytes, which is enough for all BIOS-settings. CMOS mismatch CMOS mismatch errors typically occur if the computer's power-on self-test program: 1. Finds a device that is not recorded in the CMOS. 2. Does not find a device that is recorded in the CMOS. 3. Finds a device that has different settings than those recorded for it in CMOS. 4. Detects a CMOS checksum error. [1] [2] CMOS battery [pic] Type CR2032 button cell, most common CMOS battery. The memory and real-time clock are generally powered by a CR2032 lithium coin cell. These cells last two to ten years, depending on the type of......

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...BIOS 255 Lymphatic System Assignment Student: ________________________________ Date:____________________________________ 1.  Which structure is highlighted?  [pic]    A.  thoracic duct B.  right lymphatic duct C.  cysterna chyli D.  lumbar trunk E.  bronchomediastinal trunk   2.  Which structure is highlighted?  [pic]    A.  stomach B.  spleen C.  pancreas D.  liver E.  thymus   3.  Which structure is highlighted?  [pic]    A.  uvula B.  palatine tonsil C.  pharyngeal tonsil D.  lingual tonsil E.  thymus   4.  Which structure is highlighted?  [pic]    A.  thoracic nodes B.  iliac nodes C.  cervical nodes D.  axillary nodes E.  mediastinal nodes   5.  Which structure is highlighted?  [pic]    A.  thoracic nodes B.  inguinal nodes C.  cervical nodes D.  cysterna chyli E.  mediastinal nodes   6. Which cell type transforms into plasma cells under the influence of cytokines?  A. T-Helper cells B. Antigen presenting cells C. Cytotoxic T-cells D. B-cells   7. What cell type is stimulated by Helper T-cells?  A. B-cell B. Antigen presenting cell C. Cytotoxic T-cell D. Macrophage   8.  Lymph is similar to blood plasma, but very low in   A.  protein. B.  carbon dioxide. C.  metabolic waste. D.  electrolytes. E.  sodium and potassium.   9.  _____________ are the largest of the lymphatic vessels and they empty into the _______________.   A.  Lymphatic trunks; collecting ducts B. ......

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...Introduction To sum of the differences between ROM RAM BIOS and Post I will simply define the basis of each component while defining what they do. ROM is short for (Read-only Memory) Memory which data is prerecorded on a computer. ROM is simply data that cannot be eliminated even when the computer is off. When working in ROM it is difficult or nearly impossible to change. BIOS is short for the acronym (Basic Input Output System) it is a program which stores detailed information and enable the computer to boot. In addition a ROM chip located on the motherboard, it lets you access the basic setup and ensures that the BIOS will readily be available and will not de damaged by disk failures. RAM is (Random Access Memory) Memory that can be access randomly memory that can be access without touching preceding bytes. RAM is the typical memory found in most computers or printers. In addition, RAM is a volatile memory that requires power to keep information accessible. If power is lost, then memory can be lost. Lastly Post is the self-test which is activated by Bios. The post is designed to run checks on the motherboard. It necessary to use ROM for the BIOS because the ROM retains information on the computer even while shut down whereas RAM does not. Whenever a computer is being use the information is being stored on the RAM if the is ever a power shortage or the computer lose power the information is lost. RAM does not store memory when there is no power. POST beep codes make...

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Bio-Medicine need of provision. It can be explained that health and illness are simply biological descriptions of the state of our bodies. The structures of the body have been mapped out through genetics. This is ever closer inspection of the body or as Foucault 1977 would suggest through this ‘medical gaze’ which has brought considerable power to the medical profession. The sociology of health and illness is concerned with the social origins of and influence on disease rather than exploring its organic manifestation in individual bodies. The sociology of medicine is concerned with exploring the social, historical and cultural reasons for the rise of medicine particularly the bio-medicine model in the definition and treatment of illness. A more refined version of this common sense view underlies the long standing bio-medical model of disease based on the following assumptions. Firstly that disease is an organic condition and non-organic factors associated with the human mind are considered unimportant or are ignored altogether in the search for biological causes of pathological symptoms. Secondly that disease is a temporarily an organic state that can be eradicated and cured by medical intervention. Disease is experienced by a sick individual who then becomes the object of treatment. Disease is therefore treated after the symptoms appear and the application of medicine is a reactive healing process. It I treated in a medical environment in a surgery or hospital away from the site......

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