Cardiac Arrhythmias Classification and Compression

using a Hybrid Technique

Asiya M. Al-Busaidi, Lazhar Khriji and Abdulnasir Y. Hossen

Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University,

Muscat, Oman

1 RESEARCH PROBLEM

The electrocardiogram (ECG) is a very important

biomedical signal to assess the cardiac health. The

ECG waveform is a unique signal that can be broken

down into three waves; P, QRS-complex and T, and

three segments PR, ST and RR interval as shown in

Figure 1 (Rangayyan, 2006). Each segment or wave

represents a vital process in the heart. Any

abnormalities or irregular rhythmic activities are

called arrhythmias. Cardiac arrhythmias are

indication of a cardiac disease or heart

abnormalities. According to the World Health

Organization statistics, cardiovascular diseases

(CVD) and ischemic heart diseases are the most

leading causes of mortality around the world (WHO,

2011). Heart diseases are not just the leading cause

of death nowadays but they are also considered as a

modern epidemic. Arrhythmias are usually

investigated visually, which is a very hard and time

consuming procedure in case of dealing with many

patients. Thus, automatic diagnostic methods were

developed to provide fast diagnosis that may lead to

early detection of heart diseases. Early detection of

the heart diseases can prevent the progression of

chronic diseases by proper and immediate treatment.

Generally, there is no fixed algorithm to assess

the ECG signal. There are plenty of ECG

classification methods that produce very sufficient

discrimination results. However, when it comes to

real-time analysis using low-power devices, the

power consumption, complexity of the algorithm

and memory required should be taken into

consideration.

Wearable health monitoring devices are one of

the new interesting fields of reasearch. A miniature

ECG sensor can record, compress and transmit the

data via cellular transform method to a remote base

station or hospital where the data are analysed and

stored. The technology of attaching a group of low-

power, miniaturised, invasive/ non-invasive light-

weight wireless sensors on human body to measure

Figure 1: Schematic diagram of normal sinus rhythm for a

human heart as seen on ECG (Automated ECG

Interpretation, 2014).

the biomedical signals is called Wireless Body Area

Network (WBAN) or Wireless Body Area Sensor

Network (WBASN). Despite the huge number of

published results, WBAN-based healthcare

monitoring is still in its infancy. Therefore, there are

challenges that have to be addressed while designing

such systems. Researchers have to compromise

between high reliability and low power consumption

and this can be achieved in software level by

designing an algorithm with low computational

complexity.

The limited band-width channel and battery

consumption should be taken into account while

real-time transmission. Continuous real-time

transmission can consume a lot of energy. Thus, the

ECG signal has to be compressed to reduce the data-

rate and power consumption. The compression of

ECG signal has to be conducted without distorting

the clinical features used for diagnosis. In fact the

ECG signal has to be processed before compression

and transmission.

The purpose of this work is to design a new

hybrid technique based on Wavelet Transform

(WT). The hybrid algorithm is aiming to combine

Al-Busaidi A., Khriji L. and Y. Hossen A..

Cardiac Arrhythmias Classiﬁcation and Compression using a Hybrid Technique.

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

pre-processing and post-processing tasks which are

de-noising, compression and classification of the

ECG signals. The ultimate goal of this hybrid

algorithm is to obtain sufficient diagnosis results

with low computational loads to overcome the

limitation of low-powered wearable health

monitoring devices. The algorithm will be tested and

validated using standard databases and standard

evaluate matrices.

The motivations behind this research work are

summarized as follows:

 The development of wireless sensor networks

for health care requires more sufficient real-

time analysis methods that satisfy the limitation

of these systems;

 The discrimination of cardiac arrhythmias is

still an open research field and many

classification techniques have not been tested

on ECG signals yet;

 The hybrid compression and classification

techniques showed promising performance

compared to the classical techniques.

2 OUTLINE OF OBJECTIVES

The objective of this research is to design a hybrid

denoising, compression and classification algorithm

for ECG signal. This algorithm should be suitable

for low-powered wearable ECG devices integrated

with wireless technology. This system will consist of

the following specific parts and tasks:

 Pre-processing: denoising and preparing signal

for compression and classification.

 Compression: encoding scheme to compress

the signal before transmission and then decode

the signal.

 Post-processing: feature extraction for

classification and diagnosis.

 Classification of Arrhythmias: automatic

classification of cardiac arrhythmias using a

hybrid technique.

3 STATE OF THE ART

3.1 Background and Related Work

According to American Heart Association (AHA),

an ECG signal recording must consist of at least 3

individual leads, each with 10 bits resolution and

500 Hz sampling time. If these signals were digitally

recorded on an ECG Holter for 24 hours, a huge

memory is required to store them. Thus, ECG

compression is a must in storage and transmission

over a limited bandwidth. In wearable battery-

powered devices the sampling frequency can be

reduced to 250Hz since the ECG data are used for

monitoring and not for deep diagnosis. Since the

wearable medical sensor are preferred to be with

low-cost and low-power consumption, a proper

transmission protocol needs to be followed. Many

optimised and new protocols were developed in

MAC layer (Otal et al., 2009; Lamprinos et al.,

2004), physical layer and application layer (Zhou et

al., 2008; Adibi, 2012; Lu et al., 2013). One another

solution to reduce the data rate is by compressing the

transmitted data to fit into the channel limited

capacity. Here we are going to focus on the proper

compression methods for ECG signals.

For typical biomedical signals using lossless

compression techniques can only achieve

Compression Ratios (CR) in the order of 2 to 1. On

the other hand, lossy techniques may produce CR in

the order of 10 to 1 or more. In lossy methods; there

is some kind of quantization of the input data which

leads to higher CR results at the expense of

reversibility. But this may be acceptable as long as

no clinically significant degradation is introduced to

the encoded signal. The CR levels of 2 to 1 are too

low for most practical applications. Therefore, lossy

coding methods which introduce small

reconstruction errors are preferred in practice. In

other words, the exact reconstruction of the ECG

signal is not required, only the important features are

important. Thus, the main important factors in

compression are: (1) the ability of reconstructing the

original signal or the important features from the

compressed data, (2) the compression ratio, (3)

execution time, and (4) the amount of error between

the original and reconstructed signal.

There are many lossless compression techniques

such as; null suppression, run-length coding,

diatomic coding, pattern substitution, differencing,

facsimile, statistical-Huffman and LZ family. On the

other hand, the lossy compression methods are;

polynomial predictors and interpolators, orthogonal

transforms, Fan, AZTEC, CORTES, TP, DPCM,

ADPCM, broad family of sub-band and wavelet

coding, probabilistic neural networks and adaptive

Fourier coefficient techniques. There are also recent

trials to combine the lossy and lossless techniques

specifically for ECG signal (Abo-Zahhad et al.,

2014).

There are many measures for assessing

techniques for the adequacy of the ECG compressor.

The residual of the signal, which is the difference

CardiacArrhythmiasClassificationandCompressionusingaHybridTechnique

between the original signal and the reconstructed

compressed signal, is one of the widely used

measures. But the low residual doesn’t guarantee

that the reconstructed signal has acceptable quality

for clinical diagnosis. The reconstruction error is

defined as Percent Root Mean Square Difference

(PRD) and it defined as follows:



%100

)(

)()(

=PRD















rec

nxnx

(1)

where N is the total number of samples in the ECG

signal x(n), and x

rec

(n) is the reconstructed ECG

signal.

A new compression measure called “quality

score” (QS) was suggested by Fira and Goras (2008)

to represent the ratio between the compression ratio

(CR) and the PRD as shown in (2). The high quality

score indicates a good compression performance.

PRDCRQS 

(2)

Biomedical signals can be compressed in time

domain, frequency domain, or time-frequency

domain (Cetin et al., 2000). Wavelet Transforms

(WT) are one of the recent transform method that

could produce higher coding results than time-

domain coding methods. The new hybrid

compression techniques combine the compression

and the classification techniques to overcome the

constraints of mobile ECG Holters (bandwidth, area,

power and resolution). Alvarado et al., (2012)

introduced a time-based compression technique

integrated with a classifier. This method can perform

diagnostic analysis (classification) method directly

on the encoded signal without reconstructing it. This

is done by sampling the signal using finite rate of

innovation (FRI) method and performing

compressive sensing (CS) on it. This method allows

real-time analysis, compression, classification and

transmission of the ECG signal. The classification is

done in the pulse domain resulted from FRI sampler

which is in fact a compression technique.

Ahmed et al. (2007) introduced a new hybrid

compression technique for ECG signals using the

singular value decomposition (SVD) combined with

discrete wavelet transform (DWT). The central idea

is to transform the ECG signal to a rectangular

matrix, compute the SVD, and then discard small

singular values of the matrix. The resulting

compressed matrix is wavelet transformed,

thresholded and coded to increase the compression

ratio. The technique has been tested on ECG signals

obtained from MIT-BIH arrhythmia database. The

results showed that data reduction with high signal

fidelity can thus be achieved with average data

compression ratio of 25.2:1 and average PRD of

3.14%. Comparison between the obtained results

and recently published results showed that the

proposed technique gives better performance.

Some works did combine the denoising scheme

with the compression scheme using wavelet

transform and other hybrid techniques. Sayadi et al.

(2008) represented an efficient denoising and lossy

compression schemes for ECG signals based on a

modified extended Kalman filter (EKF) structure.

The signal is reconstructed with regard to the

dynamical equations of the model. The

performances of the proposed method are evaluated

using standard denoising and compression efficiency

measures. Several MIT-BIH ECG databases were

used for performance evaluation and results shown

that both applications can contribute to enhance the

clinical ECG data denoising and compression

performance. For denoising, an average SNR

improvement of 10.16 dB was achieved, which is

1.8 dB more than the next benchmark methods such

as MAB-WT or EKF2. Results showed a typical

average CR of 11.37:1. Consequently, the proposed

framework is suitable for a hybrid system that

integrates these algorithmic approaches for clean

ECG data storage or transmission scenarios with

high output SNRs, high CRs, and low distortions.

We can summarize that compression techniques

can use hybrid techniques to achieve the proper CR,

resolution and execution time. Also, compression

can integrate hybrid functions like denoising, feature

extraction and classification of arrhythmias.

Most of the approaches that have been developed

for classifying cardiac arrhythmias based on surface

ECG (non-invasive).The classification methods can

be off-line methods or on-line (real-time) methods.

The offline technique may not be preferable in case

of emergency cases due the delay in data analysis. A

lot of work has been done based on Artificial Neural

Networks (ANN) techniques in classifying the ECG

signals. In fact, the ANN is a very powerful non-

linear mathematical tool based on training of

multilayer neural networks which are sometimes

called Multi-Layered Perceptron (MLP) and it is

mainly used in the pattern recognition research area

(Duda et al., 2001). ANN showed superior results in

discriminating the ECG signal (Bortolan et al., 1993;

Silipo et al., 1998; Tsipouras et al., 2005). ANN

could combine the pre-processing and post-

processing techniques for arrhythmia classification,

ischemia detection, and recognition of chronic

myocardial diseases (Silipo et al., 1998). Silipo et

al., (1998) also discussed the issue of reducing the

size of the network to get the same results or better.

BIOSTEC2015-DoctoralConsortium

Although, the ANN is a typical classifier used in the

hospital based ECG monitoring devices, a huge

ANN requires a lot of training time and

computational power which make it not a preferable

option for mobile ECG analysers for home-

monitoring.

Wavelet Transform (WT) is mainly used for

extracting features from signals. There are many

powerful wavelet transformation techniques such as,

Continues Wavelet Transform (CWT), Discrete

Wavelet Transform (DWT). For example, Übeyli

used DWT in analysis of ECG changes in partial

epileptic patient (Übeyli, 2008). The choice of the

wavelet family as well as the selection of the

analysing function into these families should be

proper and based on some criteria. According to

Senhadji et al., (1995) the criterion used in the first

case is the correct classification rate, and in the

second case, the correlation coefficient between the

original pattern and the reconstructed one.

Senhadji’s system was capable of discriminating

between normal, premature ventricular contraction,

and ischemic beats. Wavelet transform analysis has

high detection performance but has huge

computation overhead which also consumes a lot of

power. Bulusu (2011) also used DWT to extract

features but utilized Support Vector Machine

Approach (SVM) for arrhythmia classification. The

SVM is a binary classifier method which aims to

find the optimal separating plane and the data points

that determine the position and the orientation of the

plane. Those planes are called the support vectors.

Bulusu extracted two types of the features;

morphological features and DWT features. The

design used 12 morphological features (QS Width,

Pre RR Interval, Post RR Interval, QR Width, RS

Width, Mean Power Spectral Density, Area Under

QR, Area Under RS, Autocorrelation Value, ST

segment Deviation, Slope of ST, Correlation

coefficient with class template) and 191 discrete

wavelet transform coefficients (DWT). The features

extracted are used to train the SVM to classify six

different heart arrhythmias (N: Normal, V:

Premature Ventricular Contraction, A: Premature

Atrial Contraction, R: Right Bundle Branch Block,

L: Left Bundle Branch Block, F: Fusion). This

system resulted in an accuracy of 93.33% for ST-

episode detection and a heartbeat classification

accuracy of 90.66%. It also achieved a 96.35%

accuracy compared with the commercially available

Monebo software, which classified heart beats into

only two classes with an 89.18% accuracy. The

drawback of this system is that it takes a lot of time

during the learning phase. Bulusu recommended the

use of Hierarchical Binary Decision Tree in the

future to reduce the number of classifiers needed

during classification and to overcome the low speed

while training.

Syntactic analysis method has been utilized in

automatic diagnosis. This method depends on the

idea of transforming the original signal into vectors

of strings. The transformed signal is then

syntactically analysed to determine if the

transformed signal characterizes any fault in the

system under analysis. Many syntactic methods have

been proposed for automatic diagnosis (Koski et al.,

1995; Trahanias et al., 1990). For example, the

regular state machines, complex state machines and

fuzzy state machines have been utilized to perform

syntax analysis that can deal with imperfect and

imprecise input signals. Tumer et al. (2003) use two

level automatons (smart detection tools) with the

syntactic analysis method. The main automatons

used to identify the overall signal and the sub-

automatons to identify particular segment in the

signal (i.e. QRS and T-wave). Also, the system

incorporates fuzziness future to add flexibility to the

system in order to identify abnormalities and noises.

The syntactic analysis method showed good results

in diagnosis of nonlinear systems (ECG), but it is

noise-sensitive and has huge computations.

The new classification algorithms integrate more

than one method to have more accurate results with

less power consumption and no huge computations.

These new techniques are called hybrid techniques

and the main objective behind them is to utilize them

for miniature mobile health-care devices. Zhou et

al., (2009) combined the time-domain to detect the

QRS with syntactic method to classify the rhythm.

The rhythm classifier was designed to recognize two

kinds of QRS complex rhythms: sinus and

ventricular. The classifier is simple, fast and can be

implemented into a microprocessor or a DSP chip. It

was implemented into STAR (Système Télé-

Assistance Réparti) which is a real-time remote

continuous cardiac arrhythmia detecting and

monitoring system and it showed effective results.

Kamousi (2011) introduced a new morphology-

based algorithm by employing Dynamic Time

Warping (DTW) to measure the overall similarity in

patterns of different rhythms and distinguished VT

(ventricular tachycardia) from other rhythms such as

SVT (supraventricular tachycardia) based on

morphology differences. DTW is originally used in

automatic speech recognition by measuring the

overall similarity in patterns regardless of their

differences in time or speed. It minimizes the

difference between two given sequences by non-

CardiacArrhythmiasClassificationandCompressionusingaHybridTechnique

linearly warping them in the time dimension. This

method was utilized to improve the classification

rate of current ICD device. Typical Euclidean

distance method (measures distance between two

signals) can't capture the similarities of two similar

shapes with equal point to point distance. On the

other hand, DTW aligns the time axis and calculates

a more efficient distance measure between them and

gives better classification results.

Peng (2011) investigated in his work a low cost,

automatic real-time architecture for ECG arrhythmia

classification. The work integrates the Euclidean

Distance calculator and PCA methods for classifying

the different cardiac rhythms. The main contribution

in Peng’s work is the hardware implementation on

FPGA platform. The on-chip cache memory of the

system was used to store the rhythms used in

classification. As a result, the memory was

optimized and power consumption was reduced.

Since the ECG data may differ from patient to

patient, or differ for the same patient during the day,

the traditional trained classifiers may fail if applied

to the same patient. One of the approaches proposed

by Hu et al., (1997) to overcome this issue is to have

two classifiers. A global classifier that relies on a

database or table of known heart rhythms and a local

classifier that trains on patient’s specific ECG

recordings.

As most of the classification methods depend

significantly on the feature extraction step, the

selection of the best feature extraction method will

depend on the major value considered for training

time, training and testing performance (Khorrami et

al., 2010).

3.2 Motivations

There are many algorithms that are trying to

minimize the computational complexity and increase

the reliability of the analysis results. However, few

proposed algorithms integrate pre-processing,

compression and classification in a hybrid manner

and with minimal computational tasks. Therefore,

we propose a new hybrid techniques method of

compression and classification of cardiac

arrhythmias. This method will combine denoising,

compression and features extraction by utilizing

wavelet transform since it showed promising results

in ECG signal analysis. Thus, this work is aiming to

tackle the challenges of real-time compression and

classification of ECG data using battery-powered

wearable health monitoring devices. The ultimate

goal of this work is to find a novel and more reliable

algorithm to analyse the remotely monitored ECG

by means of less computational complexity

requiring minimal number of processing and

computational stages.

4 METHODOLOGY

The rhythm of the heart is indicated by beats per

minute (bpm). The normal heart rate is about 70

bpm but lower than 60 bpm during activity is

abnormal. The instantaneous heart rate could reach

values as high as 200 bpm during hard exercise or

athletic activity; but higher than this could be due to

illness, disease, or cardiac abnormalities

(Rangayyan, 2006). Thus, the proper measurements

of ECG should be taken to avoid contrary results. In

this study standard ECG data will be considered for

analysis and other real data will be collected to

double check the performance of the algorithms

proposed.

The ECG signal can be analysed directly using

time-domain analysis methods or transform-domain

methods (Manikandan et al., 2014). Unlike the direct

compression or analysis methods, the transform-

methods transform the signal into a frequency-

domain signal which reveals other features like the

frequency and energy distribution of the signal.

Wavelet transform (WT) methods will be

considered in this study due to their powerfulness in

decomposing the different ECG waveforms. The

wavelet-based techniques fit with the standard signal

filtering methods and encoding schemes and thus

producing good compression results (Addison,

2002). The ECG signal can be decomposed into J

decomposition levels as shown in Figure 2, using

lowpass g(n) and highpass h(n) FIR filter banks and

then down-sampling by a factor of 2. The

decomposed signal in each level is divided into low

frequency signal (a

) and high frequency signal (d

The low frequency signal a

is called the

approximation signal and the high frequency signal

is called the detail signal. The low frequency

signal is decomposed again into two signals and so

on up to d

and a

. The filter banks are constructed

from wavelet basis functions such as Haar,

h(n)

g(n)

h(n)

g(n)

↓2

(n)

↓2

Figure 2: DWT with 2 level of decomposition.

BIOSTEC2015-DoctoralConsortium

Daubechies, Biorthogonal, Coiflet, Symmlet,

Morlet, and Mexican Hat. The selection of wavelet

transform function mainly depends on the

application. The decomposed signal can be

reconstructed back again into the original signal

using reconstruction filters, which are the inverse of

the decomposition filters.

The relation between the frequency of the signal

and the decomposed sub-bands is as follows:





(3)







(4)

where, a

is the approximation coefficient, dj is the detail

coefficient, F

is the sampling frequency and j is the

decomposition level. The upper sub-bands hold the

high frequency contents and the frequency sub-band

is decreasing as the signal is decomposed further.

In our research, the algorithm is aiming to

compress and classify the ECG signal using wavelet

transform as illustrated in Figure 3 and described in

the following sub-sections.

4.1 Data Description

To validate our outcomes and results, standard ECG

data will be used from standard databases. The

PhysioNet website is a site dedicated to many

digitized physiological signals. The MIT-BIH

database will be adopted for analysis in this work

since it consists of ten databases for various test

purposes; i.e., the Arrhythmia Database, the Noise

Stress Test Database, the Ventricular

Tachyarrhythmia Database from Creighton

University Cardiac Center, the ST Change Database,

the Malignant Ventricular Arrhythmia Database, the

Atrial Fibrillation/Flutter Database, the ECG

Compression Test Database, the Supraventricular

Arrhythmia Database, the Long-Term Database, and

the Normal Sinus Rhythm Database. Initially we

used the MIT-BIH arrhythmia (mita) database as a

starting point for our analysis. The mita consists of

48 ECG recordings each with 30 minutes duration.

The recordings are sampled with a sampling rate of

360Hz and 11-bits resolution over a ±5mV range.

4.2 Pre-Processing

Like any electric signal, in practice the ECG signal

is corrupted by some artifacts like (Rangayyan,

2006):

 Electromyogram (EMG).

 Respiration and electrodes motion artifacts.

 Power line interference (50/60Hz).

 Base-line wandering.

 Motion artifacts.

Figure 3: Proposed hybrid classification algorithm.

Those noise signals reduce the quality of the

ECG signal and prevent the correct detection and

classification of the different rhythms. Thus, the pre-

processing of the ECG signal is of great importance,

since it contributes significantly to overall

compression and classification results.

For example, Figure 4 shows a typical clean

ECG signal and the same signal correlated with

ECG Si

nal

Discrete Wavelet

Transfor

Thresholding

Wavelet Coefficients

Encoding

Compressed Signal

Transmit

Pre-processing

Compression

Receive

Decoding

Wavelet Coefficients

Decompression

Extract

Features

Vecto

Reconstruction

Inverse

DWT

Reconstructed

Signal

Features

Vecto

Storage/

Display

Classification/

Diagnosis

Post-Processin

CardiacArrhythmiasClassificationandCompressionusingaHybridTechnique

50Hz power line noise. The frequency spectrum

indicates the low frequencies of the clean signal. On

the other hand, the frequency spectrum of the noisy

signal shows a 50Hz noise spectrum. To filter these

noises, digital filters can be applied on the time-

domain signal or thresholds can be applied on the

transform-domain signal. Since digital filtering is a

widely used procedure, we are going to discuss the

de-noising procedure using thresholds.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

-1

-0.5

0.5

Time (s)

ECG Voltage (mV)

Clean ECG Si

nal

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

-1

-0.5

0.5

Time (s)

ECG Voltage (mV)

Noisy ECG Signal

0 50 100 150

0.02

0.04

0.06

0.08

0.1

Frequency (Hz)

Amplitude |X(f)|

Clean ECG

0 50 100 150

0.02

0.04

0.06

0.08

0.1

Frequency (Hz)

Amplitude |X(f)|

Noisy ECG

Figure 4: A typical ECG signal (mita record 100) (above),

with 50Hz interference (middle), and their frequency

spectrums (below).

4.2.1 Threshold

The DWT can decompose the ECG signal into sub-

bands of different waveforms. By assessing the

decomposed signal in Figure 5, we can see the

different waveforms of the ECG signal and the noise

signals. The high frequency noise signals are located

in the upper sub-bands d

and d

. The power line

periodic noise is clear in d

sub-band. The advantage

of the wavelet decomposed coefficients is that the

noise signal can be easily removed using a threshold.

The threshold can be fixed for all sub-bands or

adaptive as in (5).

log2





(5)

where,  is the standard deviation of the Gaussian

noise of each sub-band and N is the number of

samples in the same sub-band (Quotb et al., 2011).

The Gaussian noise  is calculated by (6).

6795.0

])[( ndjmedian





(6)

where d

[n] is the sub-band signal. However, the

median operation requires a sorting procedure of the

sub-band coefficients. Sorting process may take time

and increases the algorithm’s overhead, and this is

not desired in our design. Replacing the median by

the average value of the sub-band coefficients will

not give us the desired results. A quickselect

algorithm can replace the typical sorting algorithm

(Quickselect, 2014). In fact quickselect is a quick

sorting algorithm that reduces the averaging

complexity from O(n log n) to O(n). The complexity

reduction of other tasks will be investigated during

the project.

100 200 300 400 500

-50

0 100 180

|X(f)|

50 100 150 200 250

-200

200

0 100 180

100

|X(f)|

20 40 60 80 100 120

-500

500

0 100 180

100

|X(f)|

10 20 30 40 50 60

-500

500

0 100 180

|X(f)|

5 10 15 20 25 30

-500

500

0 100 180

100

|X(f)|

2 4 6 8 10 12 14 16

-200

200

0 100 180

100

|X(f)|

1 2 3 4 5 6 7 8

-200

200

0 100 180

100

200

|X(f)|

1 2 3 4 5 6 7 8

-200

200

Samples

0 100 180

100

Frequency (Hz)

|X(f)|

0 200 400 600 800 1000

Original

Signal

0 100 180

0.05

0.1

|X(f)|

Figure 5: DWT sub-band coefficients of the noisy ECG

signal (mita record 100) and the frequency spectrum of

each sub-band.

Other fact about thresholds is that they can be

soft or hard. In hard thresholding, the values less

than the predetermined threshold are set to zero and

the values higher than the threshold are kept. On the

other hand, in soft thresholding the predetermined

threshold is subtracted from the values greater than

the threshold, while the values lower than threshold

are set to zero. Practically, the soft threshold

produces smooth and continuous data which make it

suitable for denoising, while the hard threshold is

preferable in case of compression.

4.3 Compression/Decompression

The compression of the ECG signal is going to be

BIOSTEC2015-DoctoralConsortium

conducted on transformed-domain signal, since

experimenting compression on the transformed

signal showed better compression performance than

time-domain compression.

Lifting wavelet transforms (LWT), wavelet

packet transforms (WPT) and discrete wavelet

transforms (DWT) will be utilized individually or in

combination in this study to produce the best real-

time performance in terms of less complexity and

fast real-time performance. Compression is

conducted on the sub-band coefficients by encoding

them and removing the redundant data. This step

depends mainly on the thresholding procedure. The

threshold indicates the critical data to be preserved

and the non-critical data that can be discarded. The

thresholding procedure will significantly affect the

compression ratio (CR) and quality of the

reconstructed signal. The following subsections will

describe the thresholding and encoding procedures

proposed to be adopted.

4.3.1 Thresholding

The thresholding procedure for compression can be

different than the filtering threshold. Unlike the

adaptive threshold, in this work the threshold

(Thres

) in (7) was calculated based on the bit-

depth (B

) of each sub-band and the desired

preserved bit-length (I

). The sub-band bit-depth

is the most significant bit of the maximum-

magnitude coefficient in the sub-band. While, the

preserved-length I

is controlled according to the

desired compression performance where Sb stands

for the sub-band coefficients d

, d

,.., d

and a





sbsb

Thres

(7)

A round-off mechanism is applied to the DWT

coefficients before thresholding and encoding by

adding 2

Bn-Isb

to all coefficients to reduce the

truncation error (Chan et al., 2008). Where, B

is the

bit depth before round-off mechanism and B

after

round-off.

4.3.2 Encoding

Encoding the DWT coefficients showed sufficient

results since the upper sub-bands mainly contain

noise-like signals which are not vital and can be

discarded. Thus, the higher sub-bands can be

encoded using fewer bits.

Before encoding the coefficients, the mean of the

approximation coefficient a

is subtracted and it will

be added later on at the reconstruction stage. To

encode the coefficients, first they are compared to

the calculated sub-band threshold Thres

in (7). If

the magnitude of the coefficient is greater than or

equal to the sub-band threshold, it is considered as

significant; otherwise it is considered as

insignificant. The desired bits of interest of the

significant coefficient will be sent to the bits-of-

interest (BOI) packet and a one will be sent to the

significant map (SM) stream. The SM stream

indicates the sequence of significant and

insignificant coefficients by ones and zeros,

respectively. The BOI are the extracted bits from

+1 to B

-I

+1, which represent BOI range,

including the sign bit (B

+1). In this works, each

BOI is stored into one byte and the same for BOI

range. Thus, I

is no more than 6 (i.e. bits 0 to 6

hold the extracted bits and bit 7 for the sign bit).

To reduce the redundant zeros in SM stream and

increase the compression ratio, it is divided into

bytes and then running length encoding (RLE) is

applied on the SM bytes. The RLE is a well-known

method that replaces the consecutive bytes with their

value followed by their number of copies (e.g. x=1 1

0 0 0 5 0 0 0 9 0 0 0 0 0 3 3 3, will be x

enc

=1 2 0 3 5

1 0 3 9 1 0 5 3 3). The SM can be easily encoded

(SMe) by encoding the consecutive zeroes. One byte

is enough to represent the number of consecutive

zeros up to 255 zeros. The last two sub-bands (a

and

) have fewer samples and less consecutive zeros

and thus RLE method was not applied to them.

In order to decode the encoded coefficients,

headers have to be designed properly to indicate the

content and the length of each sub-band. However,

the headers have to be designed to be as short as

possible to avoid increasing the length of the

encoded packets. In fact, the headers can be

designed with secret keys to encrypt the private data

of the patient while transmission over public

networks (Miaou et al., 2002).

4.4 Feature Extraction

Based on our initial review, we decided to extract

the features from the wavelet coefficients to feed the

classifier. Features should be selected carefully to fit

the designed classifier. The possible features can be

detected are:

 QRS complex: or the R-R interval which is

used to calculate the heart rate (HR) and heart

rate variability (HRV).

 P-wave and T-wave.

 Power and energy.

 Other parameters: such as minimum,

maximum, mean and standard deviation.

 The wavelet coefficients.

CardiacArrhythmiasClassificationandCompressionusingaHybridTechnique

QRS is considered as an entry point for

classification schemes since HR and HRV are

derived from the RR-intervals. Thus, it has to be

estimated accurately. So far, Pan-Tompkins

detection method (Pan and Tompkins, 1985) was

tested to detect the QRS and calculate the HRV of

the ECG signal over time. Detection using wavelet

transform will be investigated during the PhD period

since it showed better results than Pan-Tompkins

detection algorithm (Köhler et al., 2002).

4.5 Classification

ECG classification in this project describes the

automated ECG interpretation or diagnosis process.

In order to classify an ECG signal; first it should be

pre-processed and then features are extracted from

it. The possible extracted features were discussed in

the previous sub-section. The features in this project

will be extracted from a single-channel lead.

Artificial neural networks (ANN), cluster

analysis, fuzzy logics and many other methods were

utilized to classify the ECG arrhythmias. However,

most of these methods are complex and require high

computational loads. Therefore, the main goal of this

research is to find a classifier that gives sufficient

diagnosis results with low computational loads by

modifying some existing methods or integrating two

methods.

4.6 Clinical Approvals

Although there are many publications about ECG

arrhythmias classification, just few of them had

clinical approvals. Our aim is to get clinical approval

for the diagnostic results from cardiologists at Sultan

Qaboos University Hospital (SQUH). This step will

be done since low reconstruction error doesn’t

guarantee that the reconstructed signal has

acceptable quality for clinical diagnosis.

Cardiologists are expected to interpret the signal

with different arrhythmias and judge our automatic

analysis results correspond to each arrhythmia. Real

ECG data from the hospital will be collected as well

for analysis.

5 EXPECTED OUTCOME

We expect the following scientific contributions: (1)

a new compression algorithm using wavelet

transforms but with less computational overheads;

(2) a novel hybrid compression and classification

algorithm that can contribute to improve the real-

time diagnosis for low-powered wearable devices;

and (3) explore new features that can be utilized for

classification of specific cardiac diseases.

6 STAGE OF THE RESEARCH

This research is divided into three broad phases.

Some phases are done in parallel and final

integration requires a remarkable care. Some of

these phases are generalisation of the proposed

algorithm and there are several unanswered

questions regarding the final architecture and

performance of the hybrid classification algorithm.

6.1 Phase 1: Pre-Processing and

Compression using WT

This phase involves the preparation of raw ECG

signals by processing them for compression and

classification. The pre-processing will be conducted

using WT and the results obtained will be compared

to the results led by digital filters. The compression

of ECG signal using a modified discrete wavelet

transform (DWT), bit-field preserving (BFP) and

running-length encoding (RLE) method was

conducted and it showed superior results compared

to other well-known methods. The parameters of the

method may be tuned later on in the integration

phase. More investigation on the compression

scheme using WT will be conducted.

6.2 Phase 2: Classification and Feature

Extraction

In classification, the most important step is obtaining

the best features. So far, different features were

extracted from the DWT decomposed signal. Those

features were studied by plotting them correspond to

different arrhythmias. Initially, some distinguished

results can be visually spotted. However, a

sophisticated classifier has to be utilized for

automatic ECG classification. The proper classifier

that is going to fulfil the limitation and desired

requirements is still under investigation.

6.3 Phase 3: Integration

This task is significantly contributing in the novelty

of this work. The main goal is to integrate the

compression and classification algorithms with less

number of steps to reduce the computational load

and complexity. Initial prospective is to apply

BIOSTEC2015-DoctoralConsortium

compression on the decomposed coefficients and

then after decompressing those coefficients, features

are extracted from them and used for classification.

REFERENCES

Abo-Zahhad, M. M., Abdel-Hamid, T. K., Mohamed, A.

M., 2014. Compression of ECG signals based on

DWT and exploiting the correlation between ECG

signal samples. Int'l J. of Comm., Network and System

Sciences, 7: 53-70.

Addison, P. S., 2002. The illustrated wavelet transform

handbook: introductory theory and applications in

science, engineering, medicine and finance. CRC.

Ahmed, S.M., Al-Zoubi, Q., Abo-Zahhad, M., 2007. A

hybrid ECG compression algorithm based on singular

value decomposition and discrete wavelet transform.

Journal of Medical Engineering and Technology,

31(1): 54-61.

Alvarado, A.S., Lakshminarayan, C., Principe, J.C., 2012.

Time-based compression and classification of heart-

beats. IEEE Transactions on Biomedical Engineering,

59(6): 1641-1648.

Automated ECG Interpretation, In Wikipedia. Retrieved:

November 4, 2014, from: http://en.wikipedia.org/wiki/

File:SinusRhythmLabels.svg.

Bortolan, G., Willems, J.L., 1993. Diagnostic ECG

classification based on neural networks, Journal of

Electrocardiology, 26: 75-79.

Bulusu, S.C., 2011. Detection of ECG transient ST-

segment episodes and machine learning based heart

beat classification, M.sc Thesis, The University of

Texas at Dallas, May 2010.

Cetin, A.E., Köymen, H., 2000. Compression of Digital

Biomedical Signals. The Biomedical Engineering

Handbook: Second Edition, Ed. J. D. Bronzino, Boca

Raton. CRC Press LLC.

Chan, H.L., Siao, Y.C., Chen, S.W., Yu, S.F., 2008.

Wavelet-based ECG compression by bit-field

preserving and running length encoding. Computer

methods and programs in biomedicine, 90(1): 1-8.

Duda, R.O., Hart, P.E., Stork, D.G., 2001. Pattern

Classification, 2

Edition, John Wiley & Sons, Inc.

Hu, Y.H., Palreddy, S., Tompkins, W.J., 1997. A patient-

adaptable ECG beat classifier using a mixture of

experts approach, IEEE Transactions on Biomedical

Engineering, 44(9): 891-900.

Kamousi, B., 2011. Detection and Classification of

Cardiac Arrhythmias. PhD Thesis, University of

Minnesota, Minneapolis, MN, USA.

Khorrami, H., Moavenian, M., 2010. A comparative study

of DWT, CWT and DCT transformations in ECG

arrhythmias classification. Expert systems with

Applications, 37(8): 5751-5757.

Köhler, B., Hennig, C., Orglmeister, R., 2002. The

principles of QRS detection, IEEE Engineering in

Medicine and Biology, 21(1): 42-57.

Koski, A., Juhola, M., Meriste, M., 1995. Syntactic

recognition of ECG signals by attributed finite

automata, Pattern Recognition, 28(12): 1927-1940.

Manikandan, M.S., Dandapat, S., 2014. Wavelet-based

electrocardiogram signal compression methods and

their performances: A prospective review. Biomedical

Signal Processing and Control, Elsevier, 14: 73-107.

Marcovecchio, A.F., 2001. U.S. Patent No. 6,223,078.

Washington, DC: U.S. Patent and Trademark Office.

Massachusetts Institute of Technology. MIT-BIH ECG

database. Available: http://ecg.mit.edu/.

Miaou, S.G., Chen, S.T., Lin, C.L. 2002. An integration

design of compression and encryption for biomedical

signals. Journal of Medical and Biological

Engineering, 22(4): 183-192.

Pan, J., Tompkins, W.J., 1985, A real-time QRS detection

algorithm, Biomedical Engineering (BME), IEEE

Transactions, 32(3): 230-236.

Peng, C.C., 2011. A Memory-optimized architecture for

ECG signal processing, PhD Thesis, University of

Florida, Florida, United States.

Portet, F., Hernandez, A.I., Carrault, G., 2005. Evaluation

of real-time QRS detection algorithms in variable

contexts, Med. Biol. Eng. Comput., 43(3): 379-385.

Quickselect. In Wikipedia. Retrieved: November 4, 2014,

from: http://en.wikipedia.org/wiki/Quickselect.

Quotb, A., Bornat, Y., Renaud, S., 2011. Wavelet

transform for real-time detection of action potentials in

neural signals. Frontiers in Neuroengineering,

4(Article 7): 1-10.

Rangayyan, R.M., 2006. Biomedical signal analysis: a

case-study approach, Wiley-Interscience, 1

edition.

Reid, S., 2010. Model combination in multiclass

classification, PhD Thesis, University of Colorado at

Boulder, Colorado, United States.

Sayadi, O., Shamsollahi, M.B., 2008. ECG denoising and

compression using a modified extended Kalman filter

structure. IEEE Transactions on Biomedical

Engineering, 55(9): 2240-2248.

Senhadji, L., Carrault, G., Bellanger, J.J., Passariello, G.,

1995. Comparing wavelet transforms for recognizing

cardiac patterns, Engineering in Medicine and Biology

Magazine, IEEE, 14(2): 167-173.

Silipo, R., Marchesi, C., 1998. Artificial neural networks

for automatic ECG analysis, IEEE T Transactions on

Signal Processing, 46(5).

Tompkins, W.J., 1995. Biomedical Digital Signal

Processing, Prentice-Hall, Upper Saddle River, NJ.

Trahanias, P., Skordalakis, E., 1990. Syntactic pattern

recognition of the ECG, IEEE Transactions on Pattern

Analysis and Machine Intelligence, 12(7): 648,657.

Tsipouras, M.G., Fotiadis, D.I., Sideris, D., 2005. An

arrhythmia classification system based on the RR-

interval signal, Artificial Intelligence in Medicine,

33(3): 237-250.

Tumer, M.B., Belfore, L.A., Ropella, K.M., 2003. A

syntactic methodology for automatic diagnosis by

analysis of continuous time measurements using

hierarchical signal representations, IEEE Transactions

on Systems, Man, and Cybernetics, Part B:

Cybernetics, ,33(6):951-965.

CardiacArrhythmiasClassificationandCompressionusingaHybridTechnique

Übeyli, E.D., 2008. Support vector machines for detection

of electrocardiographic changes in partial epileptic

patients, Engineering Applications of Artificial

Intelligence, 21(8): 1196-1203.

World Health Organization (WHO), 2008. The 10 leading

causes of death by broad income group of 2008, Fact

sheet No310. Last Updated: June 2011. Geneva: WHO

available at:

http://www.who.int/mediacentre/factsheets/fs310/en/index

.html.

Zhou, H., Hou, K., Zuo, D., 2009. Real-time automatic

ECG diagnosis method dedicated to pervasive cardiac

care, Wireless Sensor Network, 1(4): 276-283.

BIOSTEC2015-DoctoralConsortium