Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features

Heart patient health monitoring system using invasive and non-invasive measurement

The abnormal heart conduction, known as arrhythmia, can contribute to cardiac diseases that carry the risk of fatal consequences. Healthcare professionals typically use electrocardiogram (ECG) signals and certain preliminary tests to identify abnormal patterns in a patient's cardiac activity. To assess the overall cardiac health condition, cardiac specialists monitor these activities separately. This procedure may be arduous and time-intensive, potentially impacting the patient's well-being. This study automates and introduces a novel solution for predicting the cardiac health conditions, specifically identifying cardiac morbidity and arrhythmia in patients by using invasive and non-invasive measurements. The experimental analyses conducted in medical studies entail extremely sensitive data and any partial or biased diagnoses in this field are deemed unacceptable. Therefore, this research aims to introduce a new concept of determining the uncertainty level of machine learning algorithms using information entropy. To assess the effectiveness of machine learning algorithms information entropy can be considered as a unique performance evaluator of the machine learning algorithm which is not selected previously any studies within the realm of bio-computational research. This experiment was conducted on arrhythmia and heart disease datasets collected from Massachusetts Institute of Technology-Berth Israel Hospital-arrhythmia (DB-1) and Cleveland Heart Disease (DB-2), respectively. Our framework consists of four significant steps: 1) Data acquisition, 2) Feature preprocessing approach, 3) Implementation of learning algorithms, and 4) Information Entropy. The results demonstrate the average performance in terms of accuracy achieved by the classification algorithms: Neural Network (NN) achieved 99.74%, K-Nearest Neighbor (KNN) 98.98%, Support Vector Machine (SVM) 99.37%, Random Forest (RF) 99.76 % and Naïve Bayes (NB) 98.66% respectively. We believe that this study paves the way for further research, offering a framework for identifying cardiac health conditions through machine learning techniques.

Analysis of ECG Signals by Dynamic Mode Decomposition

OBJECTIVE

Based on cybernetics, a large system can be divided into subsystems, and the stability of each can determine the overall properties of the system. However, this stability analysis perspective has not yet been employed in electrocardiogram (ECG) signals. This is the first study to attempt to evaluate whether the stability of decomposed ECG subsystems can be analyzed in order to effectively investigate the overall performance of ECG signals, and aid in disease diagnosis.

METHODS

We used seven different cardiac pathologies (myocardial infarction, cardiomyopathy, bundle branch block, dysrhythmia, hypertrophy, myocarditis, and valvular heart disease) to illustrate our method. Dynamic mode decomposition (DMD) was first used to decompose ECG signals into dynamic modes (DMs) which can be regarded as ECG subsystems. Then, the features related to the DMs stabilities were extracted, and nine common classifiers were implemented for classification of these pathologies.

RESULTS

Most features were significant for differentiating the above-mentioned groups (value<0.05 after Bonferroni correction). In addition, our method outperformed all existing methods for cardiac pathology classification.

CONCLUSION

We have provided a new spatial and temporal decomposition method, namely DMD, to study ECG signals.

SIGNIFICANCE

Our method can reveal new cardiac mechanisms, which can contribute to the comprehensive understanding of its underlying mechanisms and disease diagnosis, and thus, can be widely used for ECG signal analysis in the future.

Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

Evidence Based Recommendations for Designing Heart Rate Variability Studies

Heart rate variability (HRV) analysis is a powerful instrument that provides information about the heart conditions. However, there exist some limitations in the use of HRV in the clinical practice. Examples are the lack of reference values for healthy populations, different HR (Heart Rate) acquisition systems, and varying software packages. Other factors that affect HRV values are the influence of lifestyle, drugs and alcohol consumption, and pollution. In this work, recommendations to perform HRV-based experiments were established. These suggestions refer to best moment of the day to record data, the optimal body position, and the quality and duration of the recorded signals. In this way, HR data from 6 healthy subjects (2 women, 4 men), with median age of 50 years old, were recorded during 15 days, 3 times a day. Recordings were performed in the following situations: both supine and sitting body positions, in the morning, in the afternoon and at night. Data were processed and HRV analysis was performed. Distorting factors affecting HRV have been determined. The most stable HRV indexes (less variation over the days) have also been established. For this task, a variation coefficient was calculated for each parameter, as the ratio between the standard deviation and the mean value. Results indicated that HR data should be recorded in the morning, the sitting position. Related to signals duration, when comparing HR signals, they should be of equal length (same recording time). In addition, HRVi (HRV triangular index) and MADRR (median of the absolute differences between adjacent RR intervals) resulted in the most robust indexes in both low and high frequency domains. For global indexes, the ApEn (approximate entropy) measure emerged as the most stable one. As a conclusion, researchers must be extremely cautious in studies involving HRV analysis; the moment of the day to record data, the body position, or the quality of recorded data will produce different HR signals, and thus, the values of the HRV parameters will be different in each case. This may clearly bias the conclusions of the study.

Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques.