27M-05

個人差を考慮した高齢化社会における交通事故防止システムの構築
Automated Prediction of Sudden Cardiac Death Risk Using Kolmogorov
complexity and Recurrence Quantification Analysis Features Extracted
from HRV Signals
藤田ハミド（岩手県立大学、教授）、羽倉淳（岩手県立大学、准教授）、
R. Udyavara Acharya（Ngee Ann Polytech, Singapore）、Gajo Petrovic(岩手県立大学,大学院後期課程 D2)、 田中舘涼（ 岩手県立大学, H27 年度学部卒業）＜要旨＞
本研究では、本研究は、体調、精神状態、性格といったドライバー個人の内面に大きく関わった交通事故要因と、ドライバーの年齢、性別、自動車の運行目的といったドライバーの属性、事故現場の天候、事故発生時刻といった環境要因といった客観的に判別可能な交通事故要因を考慮した交通事故のリスク予測を実現する手法の構築を試みるものである。本リスク予測手法は、刻々と変化する状況をリアルタイムで検出し、数十秒後の事故リスクを計算可能にすることを目標とする。本技術が確立すれば、個々のドライバーの状況や特性に応じた危険予測が可能になり、警告システム等に応用することで高齢者を含む交通事故防止が予測できることが期待できる。ここでは、脳波測定と分析に基づく、心臓発作・癲癇発作の予兆を検出手法の構築と、それを利用した交通事故防止手法の構築を行う。
さらに、ドライバーの心理状態状態（特に、焦り）と脳波の関係性について調査し、心理状態の検出方法の構築を行う。

declines approximately by 10% per minute for patients
after the VF. VF triggered by VT is a standard
mechanism of cardiac arrest leading to SCD. Other
triggers of SCD include Coronary Artery Disease (CAD),
transient ischemia, myocardial infarction (MI) andheart failure. Multiple strategies have evolved to
predict and prevent SCD. One strategy among theclasses of risk predictors is clinical marker, suchas electrocardiogram (ECG) measures. Experts
worldwide are working on these cardiac problems withan intension to predict the SCD before its onset using
ECG signals].
Analysis of nonlinear and complex ECG or HRV signals
using linear methods might not yield complete
information necessary for predicting the SCD onset.
In order to unearth the hidden information from these
bio-signals nonlinear methods are used. Patients athigh risk of SCD exhibit nonlinear heart rate (HR)dynamics like rapid spectral alterations andsustained low frequency HR oscillations. Summary of

１ 研究の概要
The driver condition is important parameters
to examine that provide safe environment on to thedrivers him/her self and surroundings. In thisresearch project we have examined fatal parameters in
predicting heart sickness and warn the driver on such
related situation before it trigging a threat to thedriver and also to the surroundings. SCD(Sudden
Cardiac Death：心臓突然死) is an unexpected sudden
death due to cardiovascular problems and with orwithout history of cardiac diseases. Generally SCD
occur within one hour post symptoms onset, even though
the person has no previous fatal cardiac condition.
SCD due to cardiac arrest is the most familiar causeof death worldwide, accounting to more than 50% of alldeaths from cardiovascular diseases. SCD is a vitalchallenge for the clinicians as it occurs inindividuals without any previously known cardiac
disease. Ventricular fibrillation (VF) is themechanism underlying most SCD episodes. Survival rate28

Normal and

HRV

SCD ECG

extraction

signalPreprocessing

signal&FeatureFeatureextraction

Ranking

Classification

Figure 1 Block diagram of the proposed method.

in Figure 1.
This paper is organized as follows. Section
II discusses the dataset used and different methods.
Section III presents the experimental results andSection IV provides discussion on results. The
concluding remarks are presented in Section V.

research work conducted in identifying the SCD before
its onset using HRV signals is tabulated in Table 3.
Few researchers examined the ability ofdifferent nonlinear methods to find distinctive
pattern in HRV signals which cannot be found by
conventional linear methods. We have reported[2~4]the characteristics of ECG signals by means of anon-linear short-term fractal scaling exponent as a
useful independent risk predicting factor for SCD inpatients who had episode of acute myocardial
infarction. In addition to the time and frequency
domain parameters, Shen et al. (2007) 1 also usednonlinear method to study personal cardiac homecare
system for SCD event detection and prediction using
ECG signals. They reported an accuracy of 87.5% using
wavelet analysis for SCD detection.
Various research studies are conducted on theprediction of SCD onset using time, frequency andnonlinear methods using HRV signals [Table 3]. The
current work proposes an efficient algorithm using
nonlinear RQA parameters are extracted from therecurrence plots (RPs) of normal and SCD prone HRV
signals and Kolmogorov complexity parameters.
Features with significant information are selectedusing t-test and ranked using t-value. These ranked
features are fed to classifiers such as k-Nearest
Neighbors (k-NN), Decision tree (DT), Support vector
machine (SVM) and Probabilistic Neural Network (PNN).
The entire process is applied to one minute intervals
up to four minutes of HRV signal segments separately.
The block diagram of the proposed method is shown3rd 1 min interval

The block diagram of the proposed method is shown in
Figure 1.

A. Dataset Used
The ECG signals (SCD and normal) necessary for theexperiment were obtained from MIT-BIH SCD Holter open
access database and the Normal Sinus Rhythm database2.
The above mentioned open source database has 41 ECG
signals acquired from 23 SCD patients and 18 normal
subjects. The age of SCD patients range from 18-89 years
and control subjects from 20-50 years. The sampling rateof SCD and normal ECG signals were 256 Hz and 128Hz
respectively. In this work, to maintain uniformity
between SCD and normal groups, the SCD ECG signals were
sampled at 128 Hz. Only 20 SCD ECG signals were used foranalysis and the remaining three ECG signals were notused, as they did not have the VF episodes.

B. Pre-Processing
In this paper, the ECG signals four minutes
before the SCD occurrence (onset) were acquired fromthe 24 hours of ECG recordings of SCD patients. From
the ECG recordings, HRV signals of 10 minutes were

2nd 1 min interval

1st 1 min interval

RR interval

4th 1 min interval

I. Materials and Methods

Figure 2: The four one minute intervals of normal HRV signal.

1 [T.W. Shen, H.P. Shen, C. Lin, Y. Ou. Detection and prediction of
Sudden Cardiac Death (SCD) for personal healthcare. 29th Annual
International Conference of the IEEE, Buenos Aires, 22-26 August,2575-2578, 2007.]

2 A.L. Goldberger, L.A. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov

et al., PhysioBank, PhysioToolKit, and PhysioNet: components of a newresearch resource for complex physiologic signals. Circulation, 101,
e215-220, 2000.

29

extracted using Pan-Tompkins algorithm3 and only thefour minutes duration HRV is used for further analysis.
The obtained HRV signals of four minutes duration were
further divided into four one minute intervals. The
details of this process used for ECG signal is
discussed in our previous paper [3]. The normal HRV
signal of four one minutes’ intervals is shown in
Figure 2.

Kolmogorov complexity5 are used in this paper. RQA isused to extract significant parameters from the, .
The parameters obtained from RP describe theunderlying dynamic system. RQA quantifies andprovides valuable information about the nonlinearity
and complexity of the non-stationary data. RQA
parameters are extracted from the Recurrence Plot
(RP) depicts the nonlinear behavior of the HRV
signals.
The entropy is a nonlinear feature that reflects thedegree of chaos within a system. It is often used toanalyze the bio-signals for detecting the presence ofabnormalities. Therefore, in addition to RQA
parameters, Kolmogorov complexity is also extracted
from the HRV signals to improve the classification
accuracy. Kolmogorov complexity [19, 20] is used toquantify the complexity and irregularity of HRV
signals for normal and SCD subjects.
RQA parameters (Recurrence Rate (RR), Determinant(DET), Mean diagonal line length (meanLen), entropy(Ent), transitivity (Trans), Recurrence time entropy(RTE), Laminarity (LAM), Longest vertical line (Vmax),Longest diagonal line (Lmax) and Recurrence Times (T1,
T2)) are extracted from RP of HRV signals. These RQAparameters together with Kolmogorov complexity
feature are subjected to ranking followed byclassification.

C. Recurrence Plot (RP)Recurrence plots4 (RPs) are useful for evaluating
the geometry of system by utilizing non-linear
behavior in non-stationary time-series. It is usefulto study the behavior of physiological data. RP plots
reveal the distance relationships between points ona dynamical system. This is a graphical tool helps to
identify the shift in the dynamical system andinvisible periodicities which are unnoticeable in thetime evolution. A brief explanation about theconstruction of RPs is described below.
as thedot on the range inConsiderm-dimensional space. The RP is an array of points in× matrix, in which a point is placed at , if
is adequately near
. To obtain the RP fromtime-series, m-dimensional orbit ofisconstructed where an embedding dimension (m) is
chosen by method of delays. Further, radius r is
selected such that the ball of radius r is centeredatin ℝ holds adequate number of other dots .
In the end, a point is marked for every , wherefalls in the projectile of radius r and center .
The plot acquired is the RP and in this work, the delay= 1 and embedding dimension = 10 are used forgenerating the RPs]. RP is calculated forpre-processed HRV signals.

E. Feature Ranking
In order to select the features with significant
information the feature ranking method is used. The
feature ranking methods rank the extracted features
according to their statistical significance. In thiswork, the features are ranked based on their t- value[23].

D. Feature extraction

F. Classification

The feature extraction process is
significantly crucial step in developing an automatedalgorithm. The extraction of features is challenging
for HRV signals as it is complex, and non-stationery
in nature. In order to overcome this, a new–
Recurrence Quantification Analysis (RQA) [17] andIn order to classify normal and SCD subjects using
HRV signals k-Nearest Neighbour (KNN), Decision Tree
(DT), Support Vector Machine (SVM) and Probabilistic
Neural Network (PNN) classifiers are used. In thiswork, the ten times ten-fold cross-validation method
is used to test the performance of all classifiers.

II. Results
3 J. Pan, W.J. Tompkins. A real time QRS detection algorithm. IEEE Trans

Figure 3 and 4 shows typical RPs for (a) first one

on Biomed Eng, 3, 230-236, 1985.
4 J.P. Eckmann, S.O. Kamphorst, D. Ruelle D. Recurrence plots ofdynamical systems. Europhys Lett, 4, 973-977, 1987.

5 A. Kolmogorov. On tables of random numbers. Sankhya: The Indianjournal of statistics, Series A, 25, 369-376, 1963.

30

minute, (b) second one minute, (c) third one minute,and (d) fourth one minute of normal and SCD HRV
segments respectively. Figure 3 (a) – (d) RP of normal
HRV segments exhibits the continuous variation, andthe dots are scattered throughout the plot; thus
indicating the variation in normal HRV signals.
Figure RP of SCD cases has diagonal line and small
squares indicating less variation in the signals
which in turn indicates more uniformity in the HRV
signal. It can be seen from the figure that HRV
exhibits periodicity in some places and looks morerhythmic; the dots are concentrated in few places in
the plots. In addition, Figure 4 shows continuous
periodicity (regularity) in the plot from (a) – (d);thus indicating the high rhythmicity in the signal.
Results (mean and SD) of all the features extracted
and ranked four minutes before SCD onset are shownin Table 1. It can be seen from this table that thefirst three features are clinically significant (p <0.05) for four minutes before SCD onset. The meanvalues of Kolmogorov complexity, Lmax, Ent, RTE,
meanLen, Vmax, RR are more for SCD as compared tonormal HRV signals. The HRV signal varies more rapidly
but rhythmically for SCD as compared to normal
subjects.
Table 2 shows the classification results of normal and
SCD HRV signals using different classifiers for four
minute before SCD onset. The k-NN and PNN classifiers
performed better than rest of the classifiers for four
minutes before the SCD. PNN classifier yielded thehighest average classification accuracy of 86.8%,
sensitivity of 80% and specificity of 94.4%. The k-NN
classifier yielded 86.8%, sensitivity of 85% andspecificity of 88.8%.

Table 1: Range of features extracted from normal
and four minute prior SCD HRV signals.
Features(RQA

andNormalp-value

SCD

t-val
ue

Mean

SD

Mean

SD

K-Complex

3.270

0.472

4.184

0.646

< 0.0001

4.925

Trans

0.839

0.085

0.754

0.109

0.0120

2.645

Ent

0.265

0.189

0.754

0.109

0.0208

2.417

Lmax

4.222

2.556

6.75

5.045

0.0635

1.913

RTE

0.606

0.180

0.695

0.114

0.0751

1.832

meanLen

2.256

0.367

2.532

0.541

0.0773

1.818

Vmax

4.055

3.152

6.65

5.412

0.0837

1.778

RR

0.086

0.048

0.153

0.151

0.0837

1.778

DET

0.296

0.166

0.394

0.236

0.1552

1.451

T2

10.52

3.521

9.131

2.864

0.1861

1.347

T1

8.233

3.438

6.675

3.837

0.1977

1.312

LAM

0.361

0.162

0.447

0.263

0.24171.190

entropies)

Table 2: Results of automated classification for four
minutes before SCD HRV signals.
Classifiers

No ofAccuracy

Sensitivity

Features31

Specificit
y

DT

2

76.3%

75.0%

77.7%

k-NN

3

86.8%

80.0%

94.4%

PNN-0.03

3

86.8%

85.0%

88.8%

SVM RBF -0.3

3

84.2%

85.0%

83.3%

SVM Poly 1

2

84.2%

80.0%

88.8%

SVM Poly 2

2

78.9%

75.0%

83.3%

SVM Poly 3

2

78.9%

75.0%

83.3%

Figure 3 RPs for (a) 1st one minute, (b) 2nd one minute, (c) 3rd one minute, andFigure 4 RPs for HRV signals of (a) one minute, (b) two minutes, (c) three(d) 4th one minute of normal HRV segments.

minutes, and (d) four minutes before SCD onset.

It can be seen from Table 3 that, the efficiency
of SCD risk prediction is improved significantly
using RQA and Kolmogorov complexity features extracted
from HRV signals. This paper proposes uniquerecurrence plots for 1min, 2min, 3min and 4 min before
SCD onset. This algorithm is able to effectively
predict the SCD even four minutes prior to the onsetwith an accuracy of 86.84% using only three features
obtained from HRV signals. This result can be used in
the efficient prediction of SCD occurrence risk four
minutes prior to its onset using HRV signals. Thissystem will help to provide immediate medical
treatment and save life as it is able to predict SCD
accurately 4 minutes early.
Our method obtained the highest accuracy using only
3 features with HRV signals. Hence our method
computationally less complex and fast, and can beinstalled in vehicle for elderly SCD prediction. The
system is robust as we have performed ten-fold crossvalidation.
The novelty of this work is the SCD prediction four
minutes before its onset using just three HRV features
extracted from HRV signals and proposed unique RPs foreach class. In future, authors will be exploring thepossibility of predicting SCD still earlier (earlier
than 4 minutes) using more subjects. In addition, theintegration of ECG and HRV features for the predictionof SCD risks.

III. Discussion
Many researchers have developed algorithm for SCD
prediction before its onset using HRV signals. The
unique feature of our work is the improved efficiency
for the prediction of SCD risk four minutes before its
onset using novel nonlinear features namely
Kolmogorov complexity and RQA features extracted fromthe RPs.
Recently, we have developed a novel SCD index forthe prediction of SCD four minutes earlier by using
a single number derived from nonlinear features
extracted from DWT coefficients obtained from ECG
signals [3].Our study used nonlinear features namely
fractal dimension, Hurst’s exponent, detrended
fluctuation analysis, correlation dimension,
approximate entropy, sample entropy extracted fromDWT coefficients. These features combined with SVM
classifier are able to differentiate normal and SCD
subjects with an accuracy of 92.11% four minutes
before SCD onset. The highlight of our previous workis the formulation of a novel Sudden Cardiac Death
Index (SCDI), based on the combination of selectedfeatures to enable the clinician to easily andeffectively determine the SCD risk of a patient byusing a single number.
32

IV. Conclusion[５] U. Rajendra Acharya, Hamido Fujita,et.al "Decision support system for fatty liver disease using
GIST descriptors extracted from ultrasound images,”
Information Fusion, Volume 29, May 2016, Pages 32–
39,
http://www.sciencedirect.com/science/article/pii/S
1566253515000895

In spite of the advances in signal processing andmachine learning in modern technology, accurate
identification of SCD is the biggest challenge in
cardiology. In this paper, an efficient algorithm is
proposed for an automated prediction of SCD four
minutes prior to its onset using HRV signals. We havealso proposed unique recurrence plots for normal and
SCD classes to differentiate them visually. Ouralgorithm has achieved the highest performance in theprediction of SCD compared to all the reported
previous works (Table 3). The proposed method can betested using huge diverse database and employed inreal world scenario.

[7] #SMC2016, IEEE SMC 2016, Budapest, Hungary,
October 9, 2016,
http://smc2016.org/sites/default/files/program/Tentative%20program%20_%203%20_%20REV.pdf
The below to accepted papers are to be presented inIEEE SMC conference in Budapest, October 2016. These
could modify the results of [1]. However, these aresupported by Kakenhi-ippan(C).
(1) #1142 Automated Characterization of Arrhythmias
Using Nonlinear Features from Tachycardia ECG Beats(2) #1144 Automated Diagnosis of Coronary Artery
Disease using Nonlinear Features Extracted from ECG
Signals５ 論文・学会発表等の実績
研究成果を、タイヤ A ジャーナル（トムソン・ロイタ
ーの Science Citation Index（SCI）の高インパクトファ
クタークラスジャーナル）で公開した[2] [3] [4] [5][1] U. Rajendra Acharya; Hamido Fujita, et.al“Automated Prediction of Sudden Cardiac Death Risk
Using Kolmogorov Complexity and Recurrence
Quantification Analysis Features Extracted from HRV
Signals” Systems, Man, and Cybernetics (SMC), pp.
1110-1115, 2015 IEEE International Conference on,
DOI:10.1109/SMC.2015.199[2] Hamido Fujita, U Rajendra Acharya, et.al “Sudden
cardiac death (SCD) prediction based on nonlinear
heart rate variability features and SCD index”,
Applied Soft Computing Journal (Elsevier), Volume 43,
June 2016, Pages 510–519.
http://www.sciencedirect.com/science/article/pii/S
1568494616301016[3] U. Rajendra Acharya, Hamido Fujita, et.al“Anintegrated index for detection of Sudden Cardiac
Death using Discrete Wavelet Transform and nonlinear
features, Knowledge-Based Systems
Volume 83, July 2015, Pages 149–158,
http://www.sciencedirect.com/science/article/pii/
S0950705115001094[４] YING-TSANG LO, HAMIDO FUJITA, et.al“PREDICTION
OF CORONARY ARTERY DISEASE BASED ON ENSEMBLE LEARNINGAPPROACHES AND CO-EXPRESSED OBSERVATIONS”, Journalof Mechanics in Medicine and Biology, Vol. 16, No. 1(2016) 1640010 (10 pages)
doi:10.1142/S0219519416400108
http://www.worldscientific.com/doi/abs/10.1142/S02
19519416400108

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