Electrocardiogram Signal Classification Using Higher-Order Complexity of Hjorth Descriptor

Hadiyoso, Sugondo; Rizal, Achmad

doi:10.1166/asl.2017.8251

Cited by 9 publications

(27 citation statements)

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“…Initially, it was used to analyze EEG signal characteristics. But in the research [15], [16] this method proved to have good performance in the case of processing ECG signals. Therefore, we use the Hjorth method on this proposed system.…”

Section: B Hjorth Descriptormentioning

confidence: 94%

“…This proposed study focuses on time series analysis methods using Hjorth Descriptor and Sample Entropy for ECG biometrics. These methods have been selected for having good performance based upon some previous research to classify ECG and Epileptic EEG signals [15]- [17]. Both of these methods are basically used for analysis of signal complexity.…”

Section: Introductionmentioning

confidence: 99%

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One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

Hadiyoso¹,

Aulia²,

Rizal³

2019

ijacsa

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In this research, a person identification system has been simulated using electrocardiogram (ECG) signals as biometrics. Ten adult people were participated as the subjects in this research taken from their signal ECG using the one-lead ECG machine. A total of 65 raw ECG waves from the 10 subjects were analyzed. This raw signal is then processed using the Hjorth Descriptor and Sample Entropy (SampEn) to get the signal features. Support Vector Machine (SVM) algorithm was used as the classifier for the subject authentication based upon the record of ECG signal. The results of the research showed that the highest accuracy value of 93.8% was found in Hjorth Descriptor. Compared to SampEn, this method is quite promising to be implemented for having a good performance and fewer features.

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Section: B Hjorth Descriptormentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

Hadiyoso¹,

Aulia²,

Rizal³

2019

ijacsa

Self Cite

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“…Hasilnya didapat empat ciri terbaik dan akurasi meningkat menjadi 87.33% [18]. Dua penelitian berikutnya menggunakan Hjorth descriptor sebagai ciri, yaitu activity, mobility, dan complexity serta complexity pada orde yang lebih tinggi [19], [20]. Penggunaan tiga parameter Hjorth descriptor menghasilkan akurasi 100% sedangkan Complexity orde tinggi hanya menghasilkan akurasi 94%.…”

Section: Hasil Dan Diskusiunclassified

Klasifikasi Sinyal Elektrokardiogram Menggunakan Renyi Entropy

Estananto

Rizal²

2018

ELEMENTER

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AbstrakSinyal Elektrokardiogram (EKG) adalah salah satu indikator penting tentang aktivitas kelistrikan jantung. Sinyal ini dapat menjadi basis analisa kerja jantung sehingga menjadi penting. Sampai saat ini sinyal EKG tidak dapat digambarkan dengan persamaan matematis yang tepat karena sinyal EKG berulang tapi tidak tepat periodik. Untuk itu digunakan signal complexity untuk menjelaskan perilaku sinyal EKG.Renyl entropy adalah salah satu matrik untuk menganalisa kompleksitas sinyal, dan merupakan bentuk umum dari entropy lain. Dengan demikian akurasi yang dihasilkan lebih tinggi. Pada penelitian ini menggunakan Renyi Entropy dihasilkan akurasi 100% untuk tiga kelas data EKG. Digunakan Support Vector Machine (SVM) linear, qubic, dan quadratic sebagai pembanding satu sama lain. Keunggulan metode ini adalah akurasi yang tinggi dengan menggunakan jumlah ciri yang jauh lebih sedikit. AbstractElectrocardiogram (ECG) signals are one of the important indicators of heart electrical activity. This signal can be the basis for analyzing the work of the heart so that it becomes important. Until now the ECG signal cannot be described with the right mathematical equation because the ECG signal is repeated but not periodically correct. For this reason, complexity signals are used to explain the behavior of ECG signals.Renyl entropy is a matrix for analyzing signal complexity, and is a common form of other entropy. Thus the resulting accuracy is higher. In this study using Renyi Entropy produced 100% accuracy for three classes of ECG data. Linear, Qubic, and Quadratic Support Vector Machine (SVM) are used to be compared to each other. The advantage of this method is high accuracy by using a number of traits that are far less.

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“…Electrocardiogram (ECG) signals are extremely important in diagnosing abnormalities, such as cardiac arrhythmia [6] and heart failure. Therefore, in recent decades, there has been a significant increase in interest in the field of automatic classification of cardiovascular disorders, including AF and CHF, based on ECG signals and machine learning approaches [7][8][9][10][11][12][13]. Rizal et al used the Hjorth descriptor approach to evaluate ECG signals based on activity, mobility, and complexity features [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in recent decades, there has been a significant increase in interest in the field of automatic classification of cardiovascular disorders, including AF and CHF, based on ECG signals and machine learning approaches [7][8][9][10][11][12][13]. Rizal et al used the Hjorth descriptor approach to evaluate ECG signals based on activity, mobility, and complexity features [7,8]. Several classifier algorithms used in the classification process included k-mean clustering, k-nearest neighbor, and multilayer perceptron and obtained 88.67 %, 99.3 %, and 99.3 % accuracy, respectively, in 2015 and obtained 94 % accuracy using the k-NN classifier in 2017 [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning

Fu’adah¹,

Lim²

2021

Preprint

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Cardiovascular disorders, including atrial fibrillation (AF) and congestive heart failure (CHF), are the major causes of mortality worldwide. The diagnosis of cardiovascular disorders is heavily reliant on electrocardiogram (ECG) signals. Therefore, extracting significant features from ECG signals is the most challenging aspect to represent each condition of the ECG signals. Earlier studies have claimed that the Hjorth descriptor is assigned as a simple feature extraction algorithm that has the capability of class separation among AF, CHF, and normal sinus rhythm (NSR) conditions. However, owing to noise interference, certain features do not represent the characteristics of the ECG signals. This study addressed this important gap by applying a discrete wavelet transform (DWT) prior to applying the Hjorth descriptor as a feature extraction method. Furthermore, the feature selection process and optimization of various classifier algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), and artificial neural network (ANN), were investigated to provide the best system performance. This study obtained accuracies of 95 %, 92 %, and 95 % for the k-NN, SVM, and ANN classifiers, respectively. The results demonstrated that the optimization of the classifier algorithm could improve the classification accuracy of AF, CHF, and NSR conditions, compared to earlier studies.

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Electrocardiogram Signal Classification Using Higher-Order Complexity of Hjorth Descriptor

Cited by 9 publications

References 0 publications

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

Klasifikasi Sinyal Elektrokardiogram Menggunakan Renyi Entropy

Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning

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