Mechanized Overseer Of CVD In Integrate And Fire
Pulse Train Contrivance
R.Sunil Kumar
PG Student, Department of Electronics &
Communication, Vellammal Engineering College,
Anna University, Chennai
Dr.V.Latha
Professor, Department of Electronics &
Communication, Vellammal Engineering College,
Anna University, Chennai
ABSTRACT
The presence of Cardiovascular Disease(CVD) has been identified by QRS frequency bands present in the
Electrocardiogram(ECG) signal. Automatic diagnosing of CVD from ECG is a tedious process, therefore overseer of QRS segment
is a basic to ECG mechanizing. Persistent, portable 24/7 ECG mechanizing requires wireless technology with constraints on power,
bandwidth, area, and resolution. In order to provide continuous remote mechanizing of patients and fast transmission of data to
medical personnel for instantaneous intervention, here methodology was proposed that converts analog inputs into pulses for ultralow
power implementation. The signal encoding scheme is the time-based integrate and fire (IF) sampler from which a set of signal
descriptors in the pulse domain are proposed. Furthermore, a logical decision rule for QRS segment detection based on
morphological checking is derived from which the presence of CVD is detected. The algorithm was evaluated using the
Massachusetts Institute of Technology Beth Israel Hospital(MIT-BIH) arrhythmia database and results show that our algorithm
performance is comparable to the state-of-the art software-based detection.
Key words
Cardiovascular Disease(CVD, Electrocardiogram(ECG), Massachusetts Institute of Technology Beth Israel
Hospital(MIT-BIH), integrate and fire (IF)
I.INTRODUCTION
Cardiovascular disease which has became the great threat for the people to cause death in this fast developing world. The
death rate due to CVD is 1 in every 2.8 deaths[1] also called heart disease is a class of diseases that involve the heart, the blood
vessels (arteries, capillaries, and veins) or both.CVD refers to any disease that affects the cardiovascular system, principally cardiac
disease, vascular diseases of the brain and kidney, and peripheral arterial disease. The heart beat in the cardiac cycle of each
individual can be recorded in the ECG waveform. The CVD can be identified by analysing this recorded ECG waveform[2],[3]. This
analysis is a crucial task . ECG gave a clinical information regarding the heart beat rate, morphology and the proper functioning of
heart at cheap rate and non-invasive test[4]. Automated ECG interpretation is the use of artificial intelligence and pattern recognition
software and knowledge bases to carry out automatically the interpretation, test reporting and computer-aided diagnosis of
electrocardiogram tracings obtained usually from a patient. The first automated ECG programs were developed in the 1970s, when
digital ECG machines became possible by third generation digital signal processing boards.
The cardiac monitoring industry has seen momentous advancements such as event monitors, AF auto trigger monitors and
LOOP recorders with extended memory, and also mechanical atypical event revealing arrhythmic events [5]. The progress that
promoted this power and flexibility is the use of digital signal processing chips that implement algorithms for signal detection. The
hazard of these monitors is that they are still relatively bulky, inconvenient to carry around, and un-comfortable to wear on an
extended basis. They also involve the recurrent stand-in of batteries as power consumption is considerable. To outwit these problems,
the devices should drive with ultra-low power and be incorporated flawlessly in the day to day life of the patient, which involve
advance technology innovation.
For a digital signal processing elucidation, the first step is to sample the data uniformly at the Nyquist rate. This way of
outmoded sampling leads to huge circuits with huge burning up of power. .Additionally, the limited use of DSP chips is inefficient
because only a very small portion of its electronics is used at a given time. Although the power consumption has been progressively
diminishing, one cannot look forward to foremost gains in sliding power trending because the silicon technology is in your prime. To
diminish the problem of bulkier circuits, other novel sampling schemes such as compressive sensing [6] and the finite rate of
originality [7] have been proposed. These methods work directly on the scrubby representations of the input and combine the density
and sampling stages dropping greatly the required data rates for renovation.
To gratify the ultra-low power constraints, time-based analog to pulse converters such as integrate and fire samplers (IF)
have been proposed [8]. This pulse representation is as accurate as conventional A/Ds because it provides an injective mapping
between analog signals and the pulses [8]–[11], i.e., it is an alternative to conventional Nyquist samplers. It also has an wellorganized hardware functioning with tiny form factor [11]. Thus, IF fulfils every bit of the constraints of wearable/portable health
care arrangement such as low power, area, bandwidth, and resolution.
In this paper, we propose a scheme that quantifies the time arrangement of the pulses and can be fully implemented in devoted
combinatory logic, potentially diminishing the power consumption. The anticipated plot builds a set of attributes using finite-state
gadget to map the pulse train into the ECG morphological elements using combinatory logic decision blocks. Thus, our suggestion is
a going away from the signal processing approach based
on numbers, that is enabled by the injective mapping
characteristics of the IF (i.e., there is practically no loss of
information) and guarantees ultralow power consumption.
This provides much better trade-offs between power and
accuracy.
II.ANTICIPATED PROCESS
The implementation of the anticipated scheme can be
explained in six successive steps. The below flow chart
clearly explains the processing steps.
Fig 2 pre-processing of ECG signal
The ECG signal is conceded through a two median
filter which has two different window size one with window
size 200 ms which removes the P waves. Then, an another
one with a window size 600 ms removes the T waves. The
filtered signal represents the baseline which is then subtracted
from the novel ECG recording. Finally a notch filter cantered
at 60 Hz is implemented through a 60 tap finite impulse
response filter to remove power line interference [10].
Fig 1. Flow chart for the anticipated system
Fig 3 The stage after the ECG signal has been passed
through two levels on median filter. Which will remove
the non-QRS region of the signal
A.ECG SIGNAL
All the ECG signal which we have used in this
paper are selected from MIT-BIH Arrhythmia database[12],
which as created in 1980 as a standard reference for
arrhythmia detector[13]. In practice the date are collected
from the 24/7 portable device which is attached with the
patient's body. The database is comprised of 48 files each
containing 30-minutes ECG segments selected from 24 hours
recording of 47 different patients[12].
B.PRE-PROCESSING
Pre-processing involves the 4 steps a)Noise
removal- Electromyographic noise b)Signal Processing- Shaping the Data's Character c)Multiple Removal- Removing
Unwanted Coherent Energy d)Statics Corrections- Removing
Topography and Near-Surface Effects e)Removal of power
line interference
To spot a structure mold in the frequency domain by using
time-domain data then the time-domain data must be preprocessed by estimating the frequency response function
(FRF) is necessary. The pre-processing applied here can be
shown below
C.SAMPLING
Traditional signal processors use analog to digital
converters (ADC) to embody a given signal using
standardized sampling, which results on a worst case
condition which means Nyquist criterion can be called as a
symbol of a band limited signal. This type of traditional input
dependent samplers contemplate on the high-amplitude
regions of interest in the signal and under signify the
relatively low amplitude noise which in turn will reduce the
overall bandwidth to sub-Nyquist rate.The IF model is
stimulated by a simplified biological neuron operation from
computational neuroscience as shown below.
Fig 4 Block diagram of IF sampler
Latest research has made known that the IF model
can be measured a sampler [8]–[11]. The block diagram of
the IF sampler is shown in Fig. 4. The uninterrupted input
signal which is collected from the patient body is convolved
with an averaging function u(t) with the known starting time
and for a particular intervals which describes its threshold
limit. The integrator is retuned as well as assumed at this
situation for a explicit duration given by the refractory period
to avoid two pulses from being too close to each other and
then the process repeats. One of the most important merit of
this sampler is the minimalism of the hardware circuitry [11]
which makes it a apt device for low-power applications.
assessment logic. The logical transformation is composed of
three main components:
1) Morphological checking, where the morphological
descriptors are transformed. A multilevel transformation plot
is done to analyze morphological conditions of the ECG
under different heart rhythms. In an IF sampled ECG signal,
since the slope is encoded in the IPI of the pulses, evaluation
of the minimum IPI of the pulse segment against a revealing
threshold would be enough to determine whether it is a QRS
pulse segment or not. Though it seems good but in practice
due to the natural variability of heart rhythms, if assessment
was done with respect to a single revealing threshold, then
there is a chance of falling into the false winding up, so it is
advisable to replicate the above process for two or more
threshold values..
2) Physiological blanking, where blanking descriptors are
altered to point out the strong incentive. : whenever a QRS
complex has been detected, there is a physiological qualified
refractory period of about to 350 ms prior to the happening of
next QRS complex. However, in practice some rhythms like
ventricular tachycardia has an exception to this condition
[17].
3) search back, where discriminators and the search back
markers are distorted. A quick and a efficient decision is
made in this section which will paves the way to fetch the
conclusion about the QRS region.
Fig 5 The sampled output, here Integrate & Fire pulse
sampling are applied instead of applying the traditional
sampling method, which will convert the input signal into
pulses and then the pulses are grouped together
respective to their polarity.
D.QRS DETECTION
The preprocessed ECG signal is passed into a
sequence of pulses using the IF sampler. The pulse train is
aggregated online into altered pulse segments and each
segment is represented by a set of features which hand out as
descriptors of the pulse train. The anticipated decision logic
is based on morphological checking. The features of the pulse
segment are altered into a deposit of logical values. Based
upon the logical values, different automata-based decision
rules are executed and the QRS complexes are detected.
These steps are grouped under three sections 1) descriptors,
which explain the morphological setting, 2) markers, which
commence a decision rule, and 3) discriminators, which
differentiate between noise and isoelectric deviations.
To calculate the IF pulse train of the ECG signal, a
notion is defined for the pulse segment for the pulse train, in
which each pulse slice is arranged by pulses respective to its
polarity. The pulse segments of positive and negative polarity
correspond to positive and negative waves of ECG,
respectively. The pulse segments of interest in this paper is
chosen as the QRS pulse segments.
E.LOGICAL TRANSFORMATION AND
DECISION ASSIGNMENT
To enumerate the morphological structure of the
ECG waves, the features of each individual pulse segment
are compared against detection thresholds and converted to
logical values either in the form of zeros and ones. The
renovation captures information from a mixture of
morphological configurations of QRS waves and the logical
values serve as indicators of certain morphological
conditions, which plays the major role in the anticipated
Fig 6 Extracted QRS shows that R-peaks are clearly
detected and its denoted by red dots, through which a
further classification of CVD can be made.
III.RESULTS AND DISCUSSION
A.CHOICE OF DETECTION
THRESHOLDS
Choice of detection thresholds is of supreme importance in
judgment making. Due to wide inter- and intra-patient
inconsistency, habitual estimators will yield clear-cut
accuracy only in certain segments of the ECG signal.
Therefore, we used the Minnesota ECG coding manual [14],
which provide logical criteria for happening of cardiac
events, to fix the detection thresholds.
B.ACCURACY
The sample signal is taken from MIT-BIH Arrhythmia
database and the results are compared with living methods.
ACCURACY - It is the major parameter in obtaining the
overall success of the proposed method. this can be defined
as
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦(𝜌) =
𝑁𝐵 − 𝑒
𝑁𝐵
---(1)
Where NB denotes the total number of beats and 'e' denotes
th total number of classification errors.
compassion and specificity - These are the statistical
measures of the performance of a binary classification test,
also known as statistics' as classification function.
𝑐𝑜𝑚𝑝𝑎𝑠𝑠𝑖𝑜𝑛(𝛼) =
𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦(𝛽) =
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 +
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 +
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒
----(02)
---(03)
IV.CONCLUSION AND FUCTURE
WORK
The anticipated method uses a new way of
processing by using the pulse train which is generated by the
IF sampler. The IF representation maps the analog input
signal into a pulse train which assures the trustworthiness
similar to the traditional samplers and at a same time it
allows for a nonnumeric signal processing line of attack
which can be implemented with a few hundred combinatory
logic and flip flop blocks. This paves the way for ultra small
and ultralow power devices that can be encapsulated with the
electrodes
In future it is required to create the first archetype
for clinical trials, this study shows the feasibility of this line
of research and also there is a need to analyze the PQ
segment and ST segment which is non QRS region because a
QRS region is alone not enough to detect the presence of
CVD for a particular person. In a future work, we would like
to explore ECG delineation and beat classification using the
proposed attribute based framework for the entire ECG
signal.
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