an algorithm of st segment classification and detection

7/22/2019 An Algorithm of ST Segment Classification and Detection

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An Algorithm of ST Segment Classification and DetectionZhao Shen1, Chao Hu2, Jingsheng Liao2 Max Q.H Meng3, Fellow IEEE

1. Institute of Automation, Northwestern Polytechnical

University , Xi’an Shaanxi Province, China3.Department of Electronic and Engineering

2. Shenzhen Institutes of Advanced Technology

Shenzhen, Guangdong Province, China*

Chinese University of Hong Kong

[email protected] Hong Kong , China {chao.hu & js.liao}@siat.ac.cn [email protected]

*This work is supported by the grants from National Sc. & Tech. Pillar Program (2008BAI65B21), the Guangdong/CAS Cooperation Project (2009B091300160),

Shenzhen Sc. & Tech. Research Funds, and the Knowledge Innovation Eng. Funds of CAS.

Abstract - In electrocardiogram real-time monitoring, the ST

segment detection is important, it has close relationship with

myocardial ischemia and myocardial infarction. In this paper, ST

detection is divided into two parts, firstly using wavelet and

morphology method to calculate the offset direction, waveform

and summarizing features of ST, eventually divide the ST in 15

types; next analyzing the ST waveform change tendency in about

30 minutes and discovering the rhythm of ischemia or infarction

to help doctor. At last, the first part has been confirmed by MIT-

BIH Arrhythmia database and European ST-T database and the

second is proved by two experts’ conclusion.

Index Terms - ST segment, wavelet, R wave, T wave

I. I NTRODUCTION

In ECG monitoring, ST segment means the change of

electric potential in the period which from the end of

ventricular depolarization to the origin of repolarization. In

normal conditions, ST segment shows horizon level, but in

some heart disease conditions, the ST segment will be affected

and drifted in different direction and shown in various forms,

as is shown in figure1.Because ST segment will show in

various form for different kinds heart disease attack, so that

the change of ST segment is a significant indicator of various

heart disease in ECG clinical care.

During analysis the ST segment, the main contents include

the following two steps: one is determining the type of ST; the

other is analysis the change trend of ST. The first step of ST

type classification is calculate ST offset level, it include J+X,

R+X, regional search, and T wave methods. The J+X method

is firstly determine the end point of QRS, named J point, and

then select the point which several milliseconds after J point

as J+X point, using the value of this point as the offset level,

the R+X method is the same with the above method, just use

R point replace J point because R point is easily determined

than J point. Regional research method means select several

points as a template and uses this template to matching other

part of ST segment and selects the max value or min value as

the offset level. The T wave method is using the original pointof T wave as the offset level.

Fig1.Different Kinds of ST Waveform

The shape recognition of ST segment is another

important. Because ST segment will show various shape for

different heart disease, this feature not can help to analysis the

reason of ST change, but will help people to determine the

different heart disease. In this field, many people have

achieved great success. Mao[1] use other methods to eliminate

the noise and analysis the pattern of ST, has achieved good

result, but the progress is too complex to use in real time

monitor, Shi[2] use wavelet transform method to get the key points of ST, and use straight line to fitting ST segment curve,

and finally divide ST segment into five pattern, because the

complex of ST itself and noise disturbance, the accuracy of

this result is relatively low. Liu[3] and others use neural

network and other algorithm to analysis the ST segment and

achieve good result but it must with lots of data and it can’t

recognize the new pattern which have not been trained in the

past. Other people also have done much work in this field but

most of these methods are too complex to using in real time

monitoring.

In this paper, a new method about ST segment analysis is

proposed. Firstly, using wavelet transform to eliminate the

disturbance and determine the key points of ECG, calculatingthe ST offset, the curve type, and the concave-convex factor or

straight slope. After determine the ST pattern, for help doctor

monitor and diagnose, the ST change trend will be analysis by

using the former method, nearly 30 minutes ECG will be

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monitored continually. By detecting several periods ECG in

every 5 minutes, the change progresses of ST are analyzed and

the period and interval time of heart disease will be analyzed.

Finally, through test the data from the MIT-BIT arrhythmia

and European ST-T database, the ST detection achieve good

result, by comparing two experts decision, the result of

diagnose is consistent with experts.

II. ST SEGMENT CLASSIFICATION

In this paper, the chart of ST segment pattern

classification is shown in figure2. The step is using wavelet

transform to eliminate the noise and baseline drift of the

original ECG, and calculate the key points firstly, and then

determine the offset direction by comparing the ST offset level

with ISO, at last conclude the ST type, if it belong to straight

line, calculating the slope to divide the ST into upward,

downward or horizon, else if it belong to curve, calculating the

concave-convex factor for finishing the ST pattern

classification.

Fig2.The Chart of ST Pattern Classification

A. Wavelet Denoising

Wavelet is widely used in ECG signal; the way of using

wavelet is shown in figure3.Based on Mallat’s method, signal

can be decomposed in different layers and each layer also be

divided into wavelet coefficients part (high frequency) and

approximate coefficients part (low frequency), we can analysis

and process the signal in different layers and parts. Finally, the

signal can be reconstructed, in this progress, we can get rid of

noise, decompose and reconstruction is shown in figure4:

Fig3.The Chart of ST Pattern Classification

After the ECG is from hardware or database, the first step

is to eliminate noises, especially in ST segment. The noise

mainly include two types, one is power interference, which

caused by hardware and another is baseline drift caused by

patient breathe. First step is remove power interference; using

DB6 wavelet decomposed the ECG in 5 layers and designing

soft-threshold to eliminate the high frequency part in 1-3

layers, because these parts are mainly composed by power line

interference. After removing it, a wavelet adaptive filter is

designed for eliminate the baseline, using Meyer wavelet

decompose the ECG in 9 layers and select wavelet coefficients

in 1-9 layers as the reference input of the adaptive filter. The

algorithm of adaptive filter is least mean square (LMS). The

ECG will be compared with the reference and the weight

factor will be adjusted, according the error value to adjust the

weight to let the adaptive works in optimal state, the output is

pure ECG and the baseline will be removed clearly.

Fig4.The Decompose and Reconstruction of Signal

B. Key Points Detection

The key points in ST segment include the R peak point,

ISO point, J point, T peak point and Ton point, the R peak is

the wave peak of QRS wave, the ISO point is the original

point of QRS, the J point is the end of QRS wave, the T peak

point is the wave peak of T wave and Ton point is the original

point of the T wave, as is shown in figure5.

Fig5.Key Points of ST Detection The R peak point is the most important in ST analysis, in

reference of basis method of Li[5], we design the wavelet

difference method to calculate the R peak point. Firstly using

quadratic spline wavelet decomposes ECG in 5 layers and pick

up the low frequency part of 2 layer, and then design a

threshold to compare the value with the ECG in 2 layer, if the

value of ECG is bigger than threshold, reserve them andanother will be give up. Secondly calculate the first order

difference, and get the max and min value of the difference,

find the pass zero point in every max-min couple, and these

pass zero point is just the R peak point.

After the R peak detection, the next step is detecting the

ISO point and J point, the method is same but the direction is

opposite. For ISO point, firstly select the max value point of

one order difference as the reference point (RP), according to

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the Dascalov[6] method, select the [RP-100ms, RP-20ms] as

the ISO exist interval, then calculate slope of this segment, if

there exist more than 20ms points that the absolute value of

slope is less than threshold, the final point of this segment is

regarded as ISO point. If this interval is noised and hard to

find the fit point, we use a curve to make curve fitting to this

interval, the fit method is least square method, and use the fit

curve to calculate and determine the ISO point. As same as the

ISO, firstly select the min value point of one order differenceas the reference point (RP), and then select the

[RP+20,RP+100] as the J exist interval, use the same way and

select first point as the J point in ECG. The threshold is

between 0.05~0.15, this fit for most of the ECG data.

When the ISO and J point have been determined, the next

work is to calculate the T peak point and Ton point. According

to the Fan[7] method of the length of the QT interval, the T

wave end point will be calculate by the formula 1:

R RQonTend −+= *55.0 (1)

The Qon is just the ISO point, and the R-R is RR interval. So

we can determine the T wave end point by formula 1. Then

using former method as the same as R peak point to detect the

T wave peak point, firstly we select the interval from the J point to the Tend point, and using quadratic spline wavelet

decompose it in 5 layers, calculate the pass zero point of one

order difference max-min value couple, the pass zero point is

T wave peak point, named T peak. The Ton point is the end of

the ST segment and the original point of T wave, there are

many papers which analysis how to calculate the Ton point, in

this paper we use the distance method to determine the Ton

point, first select the point which 20ms after the J point ( J

point sometimes will happen offset for wavelet decompose

and call this new point J20 point), and then calculate the

function of the straight line from J20 point to T peak point

which is called L, the function is shown in formula 2:

)2(20

)20*)(*)20(*20

)20()( J Tpeak

J Tpeak yTpeak J yn J Tpeak

J yTpeak y y−

−+

−

−=

After this, then calculate the distance between the ECG

and L from J20 point to T peak point, and get the point which

distance is maximum in all these points, we regard this point is

Ton point, the distance formula is by formula 3:

slope slope

K n yn slope Dis

*1

)(*1*

+

++

= (3)

Dis means the distance from the point to line; K is the constant

of function in formula 2.

C. Determine the ST Offset Level

In this paper the J+X method is used to detect the SToffset level, we detect the X milliseconds after J point and call

this point JX, and compare the value of JX with ISO to

conclude the offset level, the X is determined by formula 4.

⎪⎪

⎩

⎪⎪

⎨

⎧

=

ms

ms

ms

ms

X

60

64

72

80

min/120

min/120110

min/110100

min/100

times HR

times HR

times HR

times HR

>

<<

<<

<

(4)

HR means the heart rate, after determine the JX point,

than compare the value of JX point with the ISO point, to

conclude the offset direction, based on formula 5:

)()( ISO y JX yOffset −= (5)

Offset means the offset level, y means the value of the ECG,

the judgement criterion is: ○1 if the Offset<K1, this means

“Lower”;○2 if the Offset>K2, this means “Higher”; ○3 if the

Offset belong to [K1, K2], this means “Normal”. In this paper

the value of K1 is -0.1 and K2 is 0.1.

D. Determine the ST Type

After determined the offset level of ST segment, the next

step is detect the type of ST, the type include two, one is

straight line ST, the other is curve ST. In this paper, Mao [2]

method is to use, when the offset level is Higher, we select the

points from J20 to T peak as the analysis interval and if the

offset is Lower and Normal, we select the points from J20 to

Ton as the analysis interval.

The original point of this interval is J20, and the end of

this interval is T peak or Ton, here is unified called TE, the

type judge method is distance method. Firstly calculate

function of the straight line which connects the J20 and TE

and will been called KST, the KST function is formula 6:

20

)20*)(*)20(*

20

)20()(

J TE

J TE yTE J yn

J TE

J yTE y y

−

−+

−

−= (6)

Substitute the parameters of (6) to (3), calculate the distance

from the J20 to TE, and select the maximum value of all the

distance as the Distance. Compare the Distance with the

threshold and if the Distance>threshold, this means the ST

segment is curve type, and if the Distance<threshold, this

means the ST segment is straight type. The value of threshold

in this paper is 0.15.

E. Determine Concave or Convex of Curve Type

After the type of ST segment is determined, if the ST

segment belong to curve type, the next step is determine this

ST segment belong to concave curve or convex curve, if the

ST segment belong to straight type, the next step is determine

it belong to upward type, downward type or horizon type.

Through analysis the features of the curve, it is known

that if the curve belong to the concave, all the curve is below

the line which connect the original and end point, this meansin the graph, all the value of the curve is smaller than the line.

On the contrary, if the curve belongs to convex, all the value

of the curve is higher than the line. We can use this feature to

determine the curve type.

Compare the ST segment from J20 to TE with the

corresponding in KST, and design two factors, one is concave,

the other is convex. Suppose the number of points from the

J20 to TE is M, and the number of points in ECG which value

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is higher than the corresponding KST value is P, and the

number of points in ECG which value is lower than the KST is

N, the concave and convex is define as formula 7:

M

P convex

M

N concave

=

=

(7)

If the concave>0.7, it means that more than 70% points is

lower than KST, we can conclude this curve is belong to

concave. At the same method, if the convex>0.7, it means that

more than 70% points is higher than KST, so it belong to

convex. If neither concave nor convex is more than 0.7, it

seems the ST has been noised, in this condition we use a curve

and do curve fitting for the ST segment and repeat former step

again, until to get the final result.

F. Determine Slope Direction of Straight Type

IF the ST belongs to straight type, it needs to determine

the trend of the ST segment. Because ST segment belong to

straight type, it means that ST segment has a little deviation

from the KST, we can use the KST replace ST to analysis, socalculate the slope of KST to determine the ST trend. As is

shown in formula 8:

20

)20()(

J TE

J yTE ySKST

−

−= (8)

If the SKST>k1, it seems that the ST segment is upward, and

if the SKST<k2, it seems that the ST segment is downward,

and if the k2<SKST<k1, it seems the ST is horizon. In this

paper, we select k1=0.15, k2= -0.15.

G. Result of ST Classification

In ST segment pattern classification, finally we give the

ST segment feature include three parts, that include the offset

direction, the ST type determine, if it belong to curve type,

determine the concave or convex, and if it belong to straight

type, we will give the ST direction by calculating the slope of

the fit line. The summary of ST segment pattern classification

is shown in table1.

In this paper, the test data from the MIT-BIH arrhythmia

and European ST-T database, from MIT-BIT arrhythmia, the

100.dat MLII and 105.dat MLII are selected and from

European ST-T database, the e0104.dat MLIII, e0105.dat

MLIII, e0515.dat V2, e0601.datV5,e0614.dat V5and

e0704.dat V5 are selected, the typical waveforms of these data

are shown in figure6. For the test result of data, we select 30

periods of ECG which from the every database to detect. And

all the data have been determined by the two experts, theexperts give the reference advice of the ST type for every

period, it include the offset direction, ST segment type and ST

trend and possible diseases. If the result of this method is as

same as the expert advice, it seem that the method is correct

otherwise it will be false, finally the error rate will be

calculated to prove the effectiveness of this new method.

Besides this, every database is a typical of ST segment pattern,

for example in 105.dat, the ST segment is lower than ISO and

shown in straight and horizon, in e0105.dat, the ST has

elevated and shown in curve and concave, on the contrary, in

e0515, the ST also has elevated but in convex, in e0614 the ST

segment is noised and we will use a curve to make curve

fitting for the ST segment.

Concave-Convex

FeatureSlope DirectionTypeOffset Direction

Higher

Normal

Lower

Curve

Straight

Curve

Straight

Curve

Straight

Concave

Convex

Concave

Convex

Concave

Convex

Upward

Horizon

Downward

Upward

Horizon

Downward

Upward

Horizon

Downward

Table 1. ST Pattern Classification Summarize

Fig6.Typical Waveform of Test Data

We divided ECG data in three groups, the first ten periods

ECG is mainly in the first 10 minutes, and the second ten

periods ECG is between 10~20 minutes, and the last ten

periods ECG is in the 20 minutes later, this will be benefit to

give the change analysis of ST segment in the later and this

will be analyzed detail later. Both the experts’ conclusion of

ST type and real test result are shown in table 2.

In table 2, the maximum error rate of ST pattern detectionis 16.67%, and the minimum is 0.00%. Most of the ST pattern

classification result is consistent with the expert conclusion. It

seems that this recognize method is effective, which will be

suit for most of the ST pattern recognitions.

III. ST MONITORING AND DIAGNOSE

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In part two, the ST waveform has been recognized in 15types, next we will use this conclusion to continuesmonitoring and diagnose myocardial ischemia and infarction.

A. Myocardial Ischemia and Infarction

Myocardial ischemia is one of the most common heart

diseases. The main reason is coronary atherosclerosis

decreased blood flow to the heart, that make oxygen supply

decrease and myocardial metabolism appears abnormal, in thiscondition the heart can not work normally. If this continues, it

means the patient get myocardial ischemia.

Myocardial infarction means myocardial vascular

necrosis, the immediate reason is coronary artery disease

which makes blood fell sharply or interrupted, thus heart will

appears serious and sustainability of ischemia and cause

infarction. The basic reason is plaques and thrombosis which

caused by coronary atherosclerotic limit the blood flow and

make coronary artery disease.

B. The Relationship of ST with Ischemia and Infarction

ST can rapidly and accurately reflects the myocardial

ischemia attack interval, duration and severity, the modern

standard is if ST fit for the following: ○1 at JX point and

nearby, ST shows in horizon or downward type;○2 drop over

1mm or 0.5mV and continue over 1 minute; ○3 the attack

interval is over 1 minute, this we can conclude the myocardial

ischemia has happened. And myocardial infarction can also be

reflected by ST change, when myocardial infarction has

happened, ST will shows in convex type, and be elevated until

exceed T wave, sometimes because of T wave inversion, ST

will become wilder than ever. For the relationship between ST

and myocardial ischemia or infarction is analysed, the

diagnose result is concluded by experts and shown in table2.

C. Analysis the Process of ST Change

In real time monitoring, ST change means the patient has

been attacked by myocardial ischemia or infarction. In table2,

experts have given a reference diagnose result, based on this

we will analysis the rhythm of ST change process, it will help

the doctor to detect the heart disease pathogenesis regularity,

the work mainly include three part: at the beginning the attack

time of ischemia or infarction is detected, the next step is

duration time, finally is the severity. According these test

result doctor will conclude the ischemia or infarction belong to

acute or continually disease, and common or severe. During

the time from 0:00~30:00, firstly we detect the ST waveform

type at 0:00 o’clock, and in every 5 minute we detect ST type

and compare with the type 5 minutes ago, at the same time if

there happens the break out of disease and T wave change,record and give the diagnose result. The ST change process

test result is shown in table 3.

“N means this ST segment with great noise”

100

105

E0104

E0105

E0515

E0601

E0614

E0704

0:00 5:00 10:00 15:00 20:00 25:00 30:00

Lower

Curve

Convex

ST↑ ST↑ ST↓

ST↑

T wave

Notch

ST↑ No

Change

Lower

Curve

Concave

ST

Noise

No

Change

No

Change

ST

NoiseST↑ ST↓

Higher

Curve

Concave

No

Change

ST

Noise

No

Change

No

Change

No

ChangeST↓

Higher

Straight

Upward

No

Change

No

ChangeST↓

ST↑

Burst MI

ST ↓

RevertST↓

Higher

Curve

Concave

ST↑

T↑

MI

ST↑ ST↑ ST↑ ST↑

ST↑

T↑

MI

Lower

Straight

Down

No

Change

ST

Noise

ST

Noise

No

Change

No

Change

No

Change

Normal

Straight

Horizon

No

Change

No

Change

No

Change

No

Change

No

Change No

Change

Lower

Straight

Horizon

No

Change

No

Change

No

Change

ST

Great

Noise

ST

Noise

No

Change

Table 3. ST Change Process

“MI means myocardial infarction”

In table 3 we select two examples to explain the change

process of ST segment, both of two diagnose are consistent

with the experts conclusion, first example in E0105, at the

original time, the patient has signs of myocardial infarction for

ST begin to raise, at about 5:00 o’clock, myocardial infarction

happen and ST is higher than T wave, after the time ST begin

to drop and the symptom begin to gradually release, and

before 30:00 o’clock, ST raise again and the myocardial

infarction happen, this means that the myocardial infarction is

period occur, the period is about every 25~30 minutes, and

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when each happened, it will continue 5~10 minutes, the

disease is more serious. Second example in E0515, at the

beginning time the ST is no obviously change and patient has

no obvious symptom, at about 15:00, ST drop to normal, but

between 15:00~20:00, ST suddenly raise up fast and exceed T

wave, it means the patient has a acute myocardial infarction, it

continues no more than 10 minutes and ST rapidly drop and

myocardial infarction release quickly in about 25:00, it seems

that this belong to acute myocardial infarction and thecondition of heart is very bad. If it happens next, the patient

must be send to emergency. When doctor master these useful

messages, he will take preparation and do some preventive

measurements before the disease happen again.

IV. CONCLUSION

ST segment is an important part in ECG detection,

especially in myocardial ischemia and myocardial infarction.

Because ST usually changes variously, that makes a lot of

difficulties of ST detection. In this paper a new ST detection

and diagnose is proposed, which include two parts, the first

part is recognize the ST type. It include using wavelet to

determine the key points of the ECG, and compare the valueof JX and ISO to confirm the offset direction of the ST; than

use distance method, by calculating the distance from the ECG

to the line connect J20 to TE, to make sure of the ST type,

which belong to curve or straight; at last use morphological

method to determine the concave-convex feature of curve or

use slope to determine the direction of the straight. After the

analysis of ST shape feature, the second part is heart disease

analysis of ST change trend, we detect the ECG in different

time for 30 minutes continually, analysis the process and

different features of ST, propose that regular of some heart

disease can be known from the ST change, this will help

doctor to make decision and diagnose. In the end, using MIT-

BIH arrhythmia and European ST-T database to prove the

effectiveness of this method, and the experts conclusions

confirm the analysis of ST change is useful and can give

support to doctor.

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an algorithm of st segment classification and detection

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