Feature extraction of electrocardiogram signal using  machine  learning  classification

Kuila, Sumanta; Dhanda, Namrata; Joardar, Subhankar

doi:10.11591/ijece.v10i6.pp6598-6605

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…The UCI machine learning repository dataset [19] for epileptic seizure detection is considered for testing the proposed model. The results obtained are compared with the state of the art models like KNN, logistic regression and decision tree and Gaussian Naïve Bayes classifiers [24]- [26]. Gradient boosted decision trees (GBDT) is an ensemble learning methodology that combines many decision trees in series.…”

Section: Resultsmentioning

confidence: 99%

Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier

Panigrahi

Behera²,

Patra³

2022

IJEECS

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Epilepsy causes repeated seizures in an individual's life, which causes transient irregularities in the brain's electrical activity. It results in different physical symptoms that are abnormal. Various antiepileptic drugs fail to minimize repeated patient seizures. The electroencephalogram (EEG) signal recordings provide us with time-series data set for epileptic seizure detection and analysis. These signals are highly nonlinear and inconsistent, and they are recorded over time. Predicting the ictal period (seizure period at the time of epilepsy) is thus a challenging task in the naked eye for the medical practitioners. Various machine learning techniques are applied to identify the seizure's occurrence and its classification in multiple domains. A classification model based on extreme gradient boosting (SCLXGB) is proposed here for the classification of the EEG signals. The SCLXGB model implements binary seizure classification on the benchmark dataset. Compared with K-nearest neighbor, linear regression, and Decision treebased models, the proposed model achieves the best area under receiver operating curve (AUC) of 0.9462 and an accuracy of 96% which signifies accurate prediction of seizure and non seizure period. The proposed model SCLXGB was validated by taking different performance metrics to indicate the occurrence and non-occurrence of seizures in patients more appropriately.

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Section: Resultsmentioning

confidence: 99%

Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier

Panigrahi

Behera²,

Patra³

2022

IJEECS

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“…The ECG is made up of potential fluctuations that are represented as an algebraic sum of cardiac fiber action potentials that can be computed from the body's skin surface [5]. The typical structure of normal ECG signals aids in the detection of heart abnormalities, which are referred to as heart disorders [6]. Abnormalities in the ECG signal indicate the presence of an illness.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiograph signal recognition using wavelet transform based on optimized neural network

Jawad¹,

Abdul-Zahra²,

Gheni

et al. 2022

IJECE

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<span lang="EN-US">Due to the growing number of cardiac patients, an automatic detection that detects various heart abnormalities has been developed to relieve and share physicians’ workload. Many of the depolarization of ventricles complex waves (QRS) detection algorithms with multiple properties have recently been presented; nevertheless, real-time implementations in low-cost systems remain a challenge due to limited hardware resources. The proposed algorithm finds a solution for the delay in processing by minimizing the input vector’s dimension and, as a result, the classifier’s complexity. In this paper, the wavelet transform is employed for feature extraction. The optimized neural network is used for classification with 8-classes for the electrocardiogram (ECG) signal this data is taken from two ECG signals (ST-T and MIT-BIH database). The wavelet transform coefficients are used for the artificial neural network’s training process and optimized by using the invasive weed optimization (IWO) algorithm. The suggested system has a sensitivity of over 70%, a specificity of over 94%, a positive predictive of over 65%, a negative predictive of more than 93%, and a classification accuracy of more than 80%. The performance of the classifier improves when the number of neurons in the hidden layer is increased.</span>

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“…Fingerprints are very special, and the details of the fingerprint are imperishable, even if they may tentatively change by little cuts and bruise on the skin or weather effects [9], [12], [35]- [41]. It is typically used in security systems and is compared to other biometrics such as face recognition systems [42]- [62].…”

Section: Introductionmentioning

confidence: 99%

User identification system for inked fingerprint pattern based on central moments

Baker

Alazawi

Ahmed

et al. 2021

IJEECS

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The <span>use of the fingerprint recognition has been and remains very important in many security applications and licensing systems. Fingerprint recognition is required in many areas such as licensing access to networks, corporate computers and organizations. In this paper, the system of fingerprint recognition that can be used in several cases of fingerprint such as being rounded at an angle by a randomly inked fingerprint on paper. So, fingerprint image is tooked at a different angle in order to identify the owner of the ink fingerprint. This method involves two working levels. The first one, the fingerprint pattern's shape features are calculated based on the central moments of each image being listed on a regular basis with three states rotation. Each image is rotated at a specified angle. In the second level, the fingerprint holder entered is identified using the previously extracted shape features and compared to the three local databases content of three rotation states. When applied the method for several persons by taken their inked fingerprint on the paper, the accuracy of the system in identifying the owner of the fingerprint after rotation states were close to 83.71.</span>

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Feature extraction of electrocardiogram signal using machine learning classification

Cited by 9 publications

References 23 publications

Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier

Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier

Electrocardiograph signal recognition using wavelet transform based on optimized neural network

User identification system for inked fingerprint pattern based on central moments

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