AUTOMATED CHARACTERIZATION OF CARDIOVASCULAR DISEASES USING WAVELET TRANSFORM FEATURES EXTRACTED FROM ECG SIGNALS

2019 ◽  
Vol 19 (01) ◽  
pp. 1940009 ◽  
Author(s):  
AHMAD MOHSIN ◽  
OLIVER FAUST
Keyword(s):  
Wavelet Transform ◽  
Computer Program ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Disease Diagnosis ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Training Set ◽  
Ecg Signals

Cardiovascular disease has been the leading cause of death worldwide. Electrocardiogram (ECG)-based heart disease diagnosis is simple, fast, cost effective and non-invasive. However, interpreting ECG waveforms can be taxing for a clinician who has to deal with hundreds of patients during a day. We propose computing machinery to reduce the workload of clinicians and to streamline the clinical work processes. Replacing human labor with machine work can lead to cost savings. Furthermore, it is possible to improve the diagnosis quality by reducing inter- and intra-observer variability. To support that claim, we created a computer program that recognizes normal, Dilated Cardiomyopathy (DCM), Hypertrophic Cardiomyopathy (HCM) or Myocardial Infarction (MI) ECG signals. The computer program combined Discrete Wavelet Transform (DWT) based feature extraction and K-Nearest Neighbor (K-NN) classification for discriminating the signal classes. The system was verified with tenfold cross validation based on labeled data from the PTB diagnostic ECG database. During the validation, we adjusted the number of neighbors [Formula: see text] for the machine learning algorithm. For [Formula: see text], training set has an accuracy and cross validation of 98.33% and 95%, respectively. However, when [Formula: see text], it showed constant for training set but dropped drastically to 80% for cross-validation. Hence, training set [Formula: see text] prevails. Furthermore, a confusion matrix proved that normal data was identified with 96.7% accuracy, 99.6% sensitivity and 99.4% specificity. This means an error of 3.3% will occur. For every 30 normal signals, the classifier will mislabel only 1 of the them as HCM. With these results, we are confident that the proposed system can improve the speed and accuracy with which normal and diseased subjects are identified. Diseased subjects can be treated earlier which improves their probability of survival.

scholarly journals EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm

2021 ◽  
Vol 17 (2) ◽  
pp. 38-45
Author(s):  
Samaa Abdulwahab ◽  
Hussain Khleaf ◽  
Manal Jassim
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Low Cost ◽  
Brain Diseases ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Brain Connection

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.

The Existence of A Priori Distinctions Between Learning Algorithms

1996 ◽  
Vol 8 (7) ◽  
pp. 1391-1420 ◽  
Author(s):  
David H. Wolpert
Keyword(s):  
Cross Validation ◽  
Learning Algorithm ◽  
A Priori ◽  
Learning Algorithms ◽  
Loss Functions ◽  
Average Error ◽  
Quadratic Loss ◽  
Training Set ◽  
Natural Restriction ◽  
Cross Validation Error

This is the second of two papers that use off-training set (OTS) error to investigate the assumption-free relationship between learning algorithms. The first paper discusses a particular set of ways to compare learning algorithms, according to which there are no distinctions between learning algorithms. This second paper concentrates on different ways of comparing learning algorithms from those used in the first paper. In particular this second paper discusses the associated a priori distinctions that do exist between learning algorithms. In this second paper it is shown, loosely speaking, that for loss functions other than zero-one (e.g., quadratic loss), there are a priori distinctions between algorithms. However, even for such loss functions, it is shown here that any algorithm is equivalent on average to its “randomized” version, and in this still has no first principles justification in terms of average error. Nonetheless, as this paper discusses, it may be that (for example) cross-validation has better head-to-head minimax properties than “anti-cross-validation” (choose the learning algorithm with the largest cross-validation error). This may be true even for zero-one loss, a loss function for which the notion of “randomization” would not be relevant. This paper also analyzes averages over hypotheses rather than targets. Such analyses hold for all possible priors over targets. Accordingly they prove, as a particular example, that cross-validation cannot be justified as a Bayesian procedure. In fact, for a very natural restriction of the class of learning algorithms, one should use anti-cross-validation rather than cross-validation (!).

DISCRETE WAVELET TRANSFORM APPLIED ON PERSONAL IDENTITY VERIFICATION WITH ECG SIGNAL

2009 ◽  
Vol 07 (03) ◽  
pp. 341-355 ◽  
Author(s):  
CHUANG-CHIEN CHIU ◽  
CHOU-MIN CHUANG ◽  
CHIH-YU HSU
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Personal Identity ◽  
Rejection Rate ◽  
Normal Subjects ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Identity Verification ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

The main purpose of this study is to present a novel personal authentication approach with the electrocardiogram (ECG) signal. The electrocardiogram is a recording of the electrical activity of the heart and the recorded signals can be used for individual verification because ECG signals of one person are never the same as those of others. The discrete wavelet transform was applied for extracting features that are the wavelet coefficients derived from digitized signals sampled from one-lead ECG signal. By the proposed approach applied on 35 normal subjects and 10 arrhythmia patients, the verification rate was 100% for normal subjects and 81% for arrhythmia patients. Furthermore, the performance of the ECG verification system was evaluated by the false acceptance rate (FAR) and false rejection rate (FRR). The FAR was 0.83% and FRR was 0.86% for a database containing only 35 normal subjects. When 10 arrhythmia patients were added into the database, FAR was 12.50% and FRR was 5.11%. The experimental results demonstrated that the proposed approach worked well for normal subjects. For this reason, it can be concluded that ECG used as a biometric measure for personal identity verification is feasible.

scholarly journals K-NN supervised learning algorithm in the predictive analysis of the quality of the university administrative service in the virtual environment

2022 ◽  
Vol 25 (1) ◽  
pp. 521
Author(s):  
Omar Freddy Chamorro-Atalaya ◽  
Guillermo Morales Romero ◽  
Adrián Quispe Andía ◽  
Beatriz Caycho Salas ◽  
Elizabeth Katerin Auqui Ramos ◽  
...  
Keyword(s):  
Predictive Model ◽  
Engineering Students ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Operational Characteristic ◽  
K Nearest Neighbor ◽  
Administrative Service

The objective of this study is to analyze and discuss the metrics of the predictive model using the K-nearest neighbor (K-NN) learning algorithm, which will be applied to the data on the perception of engineering students on the quality of the virtual administrative service, such as part of the methodology was analyzed the indicators of accuracy, precision, sensitivity and specificity, from the obtaining of the confusion matrix and the receiver operational characteristic (ROC) curve. The collected data were validated through Cronbach's Alpha, finding consistency values higher than 0.9, which allows to continue with the analysis. Through the predictive model through the Matlab R2021a software, it was concluded that the average metrics for all classes are optimal, presenting a precision of 92.77%, sensitivity 86.62%, and specificity 94.7%; with a total accuracy of 85.5%. In turn, the highest level of the area under the curve (AUC) is 0.98, which is why it is considered an optimal predictive model. Having carried out this study, it is possible to contribute significantly to the decision-making of the higher institution in relation to the improvement of the quality of the virtual administrative service.

scholarly journals Automatic ECG Diagnosis Using Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 951 ◽  
Author(s):  
Roberta Avanzato ◽  
Francesco Beritelli
Keyword(s):  
Confusion Matrix ◽  
Premature Ventricular Contraction ◽  
Disease Diagnosis ◽  
Ecg Signals ◽  
Testing Dataset ◽  
Life Threatening ◽  
Causes Of Mortality ◽  
Atrial Premature Beat ◽  
Heart Disease Diagnosis ◽  
Electrocardiogram Ecg

Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution for the development of automatic heart disease diagnosis systems using electrocardiogram (ECG) signals. More specifically, ECG signals were passed directly to a properly trained CNN network. The database consisted of more than 4000 ECG signal instances extracted from outpatient ECG examinations obtained from 47 subjects: 25 males and 22 females. The confusion matrix derived from the testing dataset indicated 99% accuracy for the “normal” class. For the “atrial premature beat” class, ECG segments were correctly classified 100% of the time. Finally, for the “premature ventricular contraction” class, ECG segments were correctly classified 96% of the time. In total, there was an average classification accuracy of 98.33%. The sensitivity (SNS) and the specificity (SPC) were, respectively, 98.33% and 98.35%. The new approach based on deep learning and, in particular, on a CNN network guaranteed excellent performance in automatic recognition and, therefore, prevention of cardiovascular diseases.

ekspresi Mengidentifikasi Mood Mahasiswa Berdasarkan Ekspresi Wajah dengan Menggunakan Discrete Wavelet Transform dan Fuzzy K-Nearest Neighbor

2019 ◽  
Vol 11 (1) ◽  
pp. 34-38
Author(s):  
Nur Inzani Reski Amalia ◽  
Jayanti Yusmah Sari
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor

Mood adalah keadaan emosional yang bersifat sementara. Mood biasanya memiliki nilai kualitas positif atau negatif. Kecerdasan emosional memiliki peran lebih dari 80% dalam mencapai kesuksesan hidup dan menjadi salah satu faktor yang mempengaruhi daya tangkap mahasiswa dalam proses perkuliahan. Dengan mengetahui emosi-emosi mahasiswa, kita dapat membantu daya tangkap mahasiswa saat proses perkuliahan, serta dibutuhkannya sistem yang dapat mengidentifikasi emosi-emosi yang terbentuk saat perkuliahan berlangsung. Sistem ini dibangun menggunakan  Discrete Wavelet Transform yang mentransformasikan citra menjadi 4 sub-image. Citra hasil Discrete Wavelet Transform tampak kasar atau membentuk wajah yang dapat membedakan ekspresi mahasiswa. Hasil pengolahan citra Discrete Wavelet Transform di klasifikasikan dengan menggunakan Fuzzy K-nearest neighbor. Pengklasifikasian dibagi kedalam tiga ekspresi yaitu : Marah, Senang dan Sedih dengan akurasi 77,49%

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