Analysis and classification of heart rate using CatBoost feature ranking model

Identifying the Mislabeled Training Samples of ECG Signals using Machine Learning

Classification of cardiac disorders using 1D local ternary patterns based on pulse plethysmograph signals

The classification accuracy of electrocardiogram signal is often affected by diverse factors in which mislabeled training samples issue is one of the most influential problems. In order to mitigate this negative effect, the method of cross validation is introduced to identify the mislabeled samples. The method utilizes the cooperative advantages of different classifiers to act as a filter for the training samples. The filter removes the mislabeled training samples and retains the correctly labeled ones with the help of 10-fold cross validation. Consequently, a new training set is provided to the final classifiers to acquire higher classification accuracies. Finally, we numerically show the effectiveness of the proposed method with the MIT-BIH arrhythmia database.

Due to advancement of new edge medical technologies, many methods have been applied to solve medical issues including machine learning approach. Cardiac Arrhythmia is one of the common diseases which can be solved using various machine learning approaches. There are many approaches which have already been introduced to classify arrhythmia and abnormality detection. This paper has a solution, introduces supervised and unsupervised models in which supervised models generate a good classification result. However, in this paper, we have also introduced a deep neural network classifier and used for prediction of arrhythmia if present based on some predefined value. In this paper, we have also connected it to the user interface to which the native users can check the level of arrhythmia.

Prediction and Classification of Cardiac Arrhythmia

Electrocardiogram (ECG) gives useful information about morphological and functional details of heart which is used to predict various cardiac diseases. In this paper a method of detecting cardiac diseases using support vector machine (SVM) is proposed. In this proposed method diseases are modeled using the time domain features of ECG signal which are extracted using BIOPAC AcqKnowledge software. Raw ECG signal contains these useful features which can be used to detect cardiac arrhythmia. The various ECG parameters like heart rate, QRS complex, PR interval, ST segment elevation, ST interval of ECG signal are used for analysis. Based on these parameters of ECG signal, different heart disease like atrial fibrillation, sinus tachycardia, myocardial infarction and apnea are detected. The individual accuracy of tachycardia arrhythmia, MI arrhythmia, atrial fibrillation arrhythmia and apnea proposed by SVM are 83.3%, 86.4%, 88% and 85.7% respectively.

An approach of cardiac disease prediction by analyzing ECG signal

Now a day heart diseases are the major cause of death throughout the world So, early prediction of heart diseases is essential to reduce premature death In this paper a non-invasive technique is proposed to detect the heart rate from photoplethysmography (PPG) signal Non-invasive measurement of heart rate plays a vital role in medical diagnosis due to less discomfort to patients because tissues can be damaged due to traditional sensors during the extraction of cardiac signal Photoplethysmogram is the blood volume measurement technique which is based on optic principle The blood volume changes in accordance with heart rate In this work, a convenient heart diseases estimation technique is proposed from PPG which is extracted from human facial video It will overcome the high cost of conventional expensive ECG machine for cardiac diseases prediction The heart rate is identified from FFT analysis and verified with the conventional heart rate measurement system using ECG The percentage error is 23%, 32% and 58% for green, red and blue channels respectively From heart rate the cardiac diseases have been predicted which has great potential for cardiac heart rate monitoring for computing the heart rate variability (HRV) without any contact of traditional sensors

Tahmida Tabassum

Papers

An approach of cardiac disease prediction by analyzing ECG signal

A non-invasive technique of early heart diseases prediction from photoplethysmography signal