Archit Seminar Report

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    CHAPTER-1

    1 Introduction

    The progress in desktop and portable computing in the past decade has provided the means

    with the PC or customized microcomputer-based instrumentation to develop solutions to

    biomedical problems that could not be approached before. One of our personal interests has

    been the design portable instruments that are light, compact, and battery powered. A typical

    instruments of this type is truly a personal since it is programmed to monitor signals from

    transducers or electrodes mounted on the person who is carrying it around.

    1.1 Portable Microcomputer-Based Instruments

    One example of a portable device is the portable arrhythmia monitor which monitors a

    patients electrocardiogram from chest electrodes and analyzers it in real time to determine ifthere are any heart rhythm abnormalities. We designed a prototype of such a device more

    than a decade ago. Because of the technology available of the time, this device was primitive

    compared with modern commercially portable arrhythmia monitors. The evolution of the

    technology also permits us to think of even more extensions that we can make. Instead of just

    assigning a heart monitoring device to follow a patient after discharge from the hospital, we

    can now think of designing a device that would help diagnose the heart abnormality when the

    patient arrives in the emergency room. With a careful design, the same device might go with

    the patient to monitor the cardiac problem during surgery in the operating room, continuously

    learning the unique characteristics of the patients heart rhythms. The device could follow the

    patients throughout the hospital stay, alerting the hospital staff to possible problems in theintensive care unit, in the regular hospital room, and even in the hallways as the patient walks

    to the cafeteria. The device could them accompany the patient home, providing continuous

    monitoring that is not now practical to do, during the critical times following open heart

    surgery.

    There are many other examples of portable biomedical instruments in the marketplace and in

    the research lab. One other microcomputers-based device that we contributed to developing is

    a calculator size product called the CALTRAC that uses a miniature accelerometer to monitor

    the motion of the body. It then converts this activity measurement to the equivalent number

    of calories and display the cumulative result on an LCD display.

    1.2 PC-Based Medical instrumentsThe economy of mass production has led tothe use of the desktop PC as the central computer for many types of biomedical application.

    Many companies use PCs for such application as sampling and analysing physiological

    signals, maintaining equipment databases in the clinical engineering department of hospitals,

    and simulation and modeling of physiological system.

    CHAPTER-2

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    2Basic Electrocardiography

    The electrocardiogram (ECG) is a graphicalrepresentation of the electrical activity of the

    heart and isobtained by connecting specially designed electrodes to thesurface of the body

    [1]. It has been in use as a diagnostic toolfor over a century and is extremely helpful in

    identifyingcardiac disorders non-invasively. The detection of cardiacdiseases using ECG has benefited from the advent of thecomputer and algorithms for machine identification of

    thesecardiac disorders.

    Fig. 2.1 The placement of the bipolar leads

    A new dimension byintroducing the concept of vectors to represent the ECGvoltages. He is

    also the first individual to standardize theelectrode locations for collecting ECG signals as

    right arm(RA), left arm (LA) and left leg (LL), and these locations areknown after him as the

    standard leads of Einthoven or limbleads, as shown in Figure 1. The limb leads consist of

    sixunipolar chest leads, starting from lead V1 until V6 in anelectrocardiogram.

    Fig. 2.2 The placement of the exploratory electrode for theunipolar chest leads

    Most of the cardiac disease classification algorithms beginwith the separation or delineationof the individual ECG signalcomponents. The ECG signal comprises of the QRS complex,Pand T waves as shown in Figure 3. Occasionally a U-wavemay also be present which lies

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    ofelectronics support and processing unit within the mobilephone, the overall performance ishardly operated in an idealcondition. The display screen of mobile phone is smaller than.

    An ECG signal, according to the American Heart Association, must consist of 3 individual

    leads, each recording 10 bits per sample, and 500 samples per second. Some ECG signals,may require 12 leads, 11 bits per second, 1000 samples per second, and last 24 hours. When

    converted to a digital format, this single ECG record requires a total of 1.36 gigabytes ofcomputer storage! Considering the 10 million ECGs annually recorded for the purposes ofcomparison and analysis in the United States alone, the necessity for effective ECG datacompression techniques is becoming increasingly important. Further more, the growing needfor transmission of ECGs for remote analysis is hindered by capacity of the average analogtelephone line and mobile radio .

    2.2 Percent Mean-Square Difference (PRD)

    Data compression techniques are categorized as those in which the compressed data isreconstructed to form the original signal and techniques in which higher compression ratios

    can be achieved by introducing some error in the reconstructed signal. The effectiveness ofan ECG compression technique is described in terms of compression ratio (CR), a ratio of thesize of the compressed data to the original data; execution time, the computer processing timerequired for compression and reconstruction of ECG data; and a measure of error loss, oftenmeasured as the percent mean-square difference (PRD). The PRD is calculated as follows:

    where ORG is the original signal and REC is the reconstructed signal. The lower the PRD,

    the closer the reconstructed signal is to the original ECG data.

    There are several exact-reconstruction techniques including null suppression, run-length

    encoding, diatomic encoding, pattern substitution, differencing, facsimile techniques, and

    statistical encoding. Null suppression is a data-compression technique that searches for

    strings of empty, or null, data points and replaces them with an indicator and a number

    representing the length of the null sequence. Run-length encoding is a modification of null

    suppression, where the process used to compress null data points is applied to any repeatingdata sequence. For instance a character sequence:

    WGAQQQQQQRBCCCCCHZY

    may be compressed as:WGAQ*6RBC*5HZY,

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    average beat subtraction techniques are commonly applied to ECG records. More complex

    techniques such as the use of wavelet packets, neural networks, and adaptive Fourier

    coefficients are currently being explored with the expectations that they will result in higher

    compression ratios but longer processing time for compression and reconstruction.

    Figure 2.2 ZOP floating aperture.

    Polynomial predictors are a data compression technique in which a polynomial of orderkis

    fit through previously known data samples and then extrapolated to predict future ones. The

    polynomial predictor is in the form:

    y'n = yn-1 + Dyn-1 + D2yn-1 + ... + D

    kyn-1

    where

    y'n = predicted sample point at time tn

    yn-1 = sample value at one sample period prior to tn

    Dyn-1 = yn-1 - yn-2

    Dkyn-1 = D

    k-1yn-1 - D

    k-1yn-2.

    The polynomial predictor with k= 0 is known as the Zero-Order Predictor (ZOP). With the

    ZOP, each data point is predicted to be the same as the previous one, resulting in the

    equation:

    y'n = yn-1

    Most applications using the ZOP, use a form known as a floating aperture, or step method.

    This algorithm records a single data sample and deletes each successive sample that lies

    within a tolerance band e, whose center lies on the saved sample, and replaces them with a

    horizontal line. When a sample is outside the tolerance band, the new sample is saved and the

    process continues.

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    Figure 2.3 the FOP.

    The first-order predictor (FOP) is the use of a polynomial predictor with k= 1. It requires the

    knowledge of the two previous points, yielding the equation:

    y'n = 2yn-1 + yn-2

    Similar to the ZOP, its difference lies in the way the predicting line is drawn. Rather than a

    horizontal line, the two previous points are used to formulate a starting point and a slope for

    the prediction. A tolerance band is still applied, and when a data sample lies outside the

    tolerance band, a new FOP. is formulated .

    Polynomial interpolators differ from polynomial predictors in that previous and future data

    samples are used to create the predicting polynomial. The zero-order interpolator (ZOI)

    modifies the ZOP by allowing the horizontal line to have a height corresponding to the

    average of a set of data rather than simply that of the first point. Although both the ZOI and

    the ZOP ensure that all data samples lie within a tolerance band around the reconstructed

    data, the ZOI results in higher compression since the saved da