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Automated health detection of congestive heart failure subject using rank multiresolution wavelet packet attributes and 1-norm linear programming ELM

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Abstract

As far as the mortality of the global population is concerned, it is cardiovascular diseases which cause the highest death rate worldwide, mostly due to the Congestive Heart Failure (CHF). Therefore, an initial detection and diagnosis of CHF becomes essential. This manuscript presents a novel approach to detect health of \(\mathrm{CHF}\) subject which is based on Multiresolution Wavelet Packet (MRWP) decomposition method, attributes ranking approach, kernel principle component analysis \((\mathrm{KPCA})\) and \(1-\mathrm{Norm Linear}\) \(\mathrm{Programming}\) \(\mathrm{Extreme}\) \(\mathrm{Learning}\) Machine \((1-\mathrm{NLPELM}).\) For this investigation, the heart rate variability (HRV) signal has been decomposed up to 5-level using MRWP decomposition method. The sixty three log root mean square (LRMS) attributes were extracted from the decomposed HRV signal. The top ten attributes are selected by ranking approaches such as\(\mathrm{Fisher}\), Wilcoxon,\(\mathrm{Entropy}\),\(\mathrm{Bhattacharya}\), and receiver operating characteristic\((\mathrm{ROC})\). The ten ranked attributes were then mapped to one new feature by KPCA and fed to\(1-\mathrm{NLPELM}\). The \(\mathrm{HRV}\) database of normal subjects (normal sinus rhythm\((\mathrm{NSR})\), age 22–45 years old and elderly (ELY), age 60–82 years old) and CHF subjects (age 32–71 years old) were obtained from PhysioNet ATM. The simulation results demonstrated that \(\mathrm{Bhatacharya}+\mathrm{ KPCA with }1-\mathrm{NLPELM}\) approach achieved an accuracy of\(98.44\pm 1.4\mathrm{\%}\), \(99.13\pm 1.85\mathrm{ \%}\) for \(\mathrm{NSR}-\mathrm{CHF}\) and \(\mathrm{ELY}-\mathrm{CHF}\) respectively. Out of all ranking methods, \(\mathrm{Bhatacharya}\) combined with \(\mathrm{KPCA}+1-\mathrm{NLPELM}\) provided the highest degree of accuracy for all datasets. In addition, the proposed method has also achieved very good generalization performance and less execution time as compared to\(1-\mathrm{NLPELM}\),\(\mathrm{KPCA}+\mathrm{PNN}\), \(\mathrm{KPCA}+\mathrm{SVM}\), probabilistic neural network (\(\mathrm{PNN}\)) and support vector machine (\(\mathrm{SVM})\).

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Abbreviations

AI:

Artificial intelligence

ANS:

Autonomic nervous system

CRD:

Cardiac resynchronization-defibrillator

CVDS:

Cardio vascular diseases

CHF:

Congestive heart failure

GDA:

Generalized discriminant analysis

HRV:

Heart rate variability

1-NLPELM:

1-Norm linear programming extreme learning machine

ELY:

Elderly

ELM:

Extreme learning machine

ECG:

Electrocardiogram

KPCA:

Kernel principle component analysis

LRMSF:

Log root mean square features

ML:

Machine learning

μ1 and μ2:

Mean of 1st and 2nd group of Jth attributes (features)

MRWP:

Multiresolution wavelet packet

NSR:

Normal sinus rhythm

N1:

Number of first group attributes

N2:

Number of second group attributes

PNN:

Probabilistic neural network

RBF:

Radial basis function

ROC:

Receiver operating characteristic

SCA:

Sudden cardiac arrest

SVM:

Support vector machine

β:

The weight between hidden node and output

V1 and V2:

Variance of 1st and 2nd group of Jth attributes

W:

Wavelet functions

WP:

Wavelet packet

G(W,B,X):

Activation function(wight, biase, input attribute)

L:

Class label for given attributes

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Authors and Affiliations

  1. Electronics & Communication Eng. Department (SIG), School of Electrical Engineering & Computing, Adama Science & Technology University, 1888, Adama, Ethiopia

    Demissie J. Gelmecha, Ram S. Singh & Dereje Tekilu

  2. Mechanical Design & Manufacturing Eng. Department, Center of Excellence in Advanced Manufacturing Engg., School of Mechanical, Chemical & Material Engineering, Adama Science & Technology University, Adama, Ethiopia

    Devendra K. Sinha

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  1. Demissie J. Gelmecha
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Correspondence to Ram S. Singh.

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Gelmecha, D.J., Singh, R.S., Sinha, D.K. et al. Automated health detection of congestive heart failure subject using rank multiresolution wavelet packet attributes and 1-norm linear programming ELM. Multimed Tools Appl 81, 19587–19608 (2022). https://doi.org/10.1007/s11042-021-11562-z

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