Myocardial ischemia analysis based on electrocardiogram QRS complex

Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram

OBJECTIVES

Coronary artery diseases (CADs) are the leading cause of death worldwide and early diagnosis is crucial for timely treatment. To address this, our study presents a novel automated Artificial Intelligence (AI)-based Hybrid Anomaly Detection (AIHAD) technique that combines various signal processing, feature extraction, supervised, and unsupervised machine learning methods. By jointly and simultaneously analyzing 12-lead cardiac sympathetic nerve activity (CSNA) and electrocardiogram (ECG) data, the automated AIHAD technique performs fast, early, and accurate diagnosis of CADs.

METHODS

In order to develop and evaluate the proposed automated AIHAD technique, we utilized the fully labeled STAFF III and PTBD databases, which contain the 12-lead wideband raw recordings non-invasively acquired from 260 subjects. Using these wideband raw recordings, we developed a signal processing technique that simultaneously detects the 12-lead CSNA and ECG signals of all subjects. Using the pre-processed 12-lead CSNA and ECG signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of CADs. Using the extracted discriminative features, we developed a supervised classification technique based on Artificial Neural Networks (ANNs) that simultaneously detects anomalies in the 12-lead CSNA and ECG data. Furthermore, we developed an unsupervised clustering technique based on Gaussian mixture models (GMMs) and Neyman-Pearson criterion, which robustly detects outliers corresponding to CADs.

RESULTS

Using the automated AIHAD technique, we have, for the first time, demonstrated a significant association between the increase in CSNA signals and anomalies in ECG signals during CADs. The AIHAD technique achieved highly reliable detection of CADs with a sensitivity of 98.48 %, specificity of 97.73 %, accuracy of 98.11 %, positive predictive value of 97.74 %, negative predictive value of 98.47 %, and F1-score of 98.11 %. Hence, the automated AIHAD technique demonstrates superior performance compared to the gold standard diagnostic test ECG in the diagnosis of CADs. Additionally, it outperforms other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it significantly increases the detection performance of CADs by taking advantage of the diversity in different data types and leveraging their strengths. Furthermore, its performance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or classify CADs. Additionally, it has a very low implementation time, which is highly desirable for real-time detection of CADs.

CONCLUSIONS

The proposed automated AIHAD technique may serve as an efficient decision-support system to increase physicians' success in fast, early, and accurate diagnosis of CADs. It may be highly beneficial and valuable, particularly for asymptomatic patients, for whom the diagnostic information provided by ECG alone is not sufficient to reliably diagnose the disease. Hence, it may significantly improve patient outcomes by enabling timely treatments and considerably reducing the mortality of cardiovascular diseases (CVDs).

Protective effects of Xinji'erkang on myocardial infarction induced cardiac injury in mice

BACKGROUND

Myocardial infarction (MI) is a major risk factor responsible for morbidity and mortality. Xinji'erkang (XJEK) has been clinically used as an effective medication in the treatment of coronary heart disease and myocarditis. The purpose of this study was to investigate the cardioprotective effect of Xinji'erkang on MI mice.

METHODS

Forty male mice were randomly assigned into four groups as follows (n = 10): sham, model, MI with administration of XJEK and fosinopril for four weeks. At the end of studies, hemodynamic parameters and electrocardiography (ECG) were recorded. Heart and body mass were measured and heart weight/body weight (HW/BW) ratio was calculated as index of hypertrophy. The hypertrophy of heart and aorta was examined using the hematoxylin and eosin (HE) staining, and the collagen deposition was evaluated using Van Gieson (VG) staining. Serum nitric oxide level (NO), superoxide dismutase (SOD) activity and malondialdehyde (MDA) concentration were assayed by colorimetric analysis. The expressions of endothelial NO synthetase (eNOS) expression in serum and cardiac tissues were determined using ELISA assay and immunohistochemistry. Angiotensin II (Ang II) in serum and cardiac tissues was measured using ELISA assay. Besides, tumor necrosis factor-α (TNF-α), interleukin1β (IL-1β) and interleukin10 (IL-10) were observed in cardiac tissues with ELISA assay as well.

RESULTS

The administration of XJEK significantly improved cardiac dysfunction and abnormal ECG with reduced HW/BW ratio and ameliorated cardiomyocyte hypertrophy and collagen deposition compared to MI, which was partly due to the decreased SOD and increased MDA in serum. Moreover, XJEK treatment also improved endothelial dysfunction (ED) with not only enhanced eNOS activities in serum and cardiac tissues and elevated NO levels in serum, but also decreased Ang II content in serum and cardiac tissues. Finally, protein expressions of pro-inflammation cytokines, TNF-α and IL-1β in the cardiac tissues with XJEK treatment were significantly decreased compared to model. On the contrary, IL-10, an anti-inflammatory cytokine concentrated in cardiac tissues was significantly enhanced compared to model.

CONCLUSION

Xinji'erkang exerts cardioprotective effect on myocardial infarction in mice, which may be due to the improvement of endothelial dysfunction and the reduction of oxidative stress and inflammation response.

The history, hotspots, and trends of electrocardiogram

The electrocardiogram (ECG) has broad applications in clinical diagnosis and prognosis of cardiovascular disease. Many researchers have contributed to its progressive development. To commemorate those pioneers, and to better study and promote the use of ECG, we reviewed and present here a systematic introduction about the history, hotspots, and trends of ECG. In the historical part, information including the invention, improvement, and extensive applications of ECG, such as in long QT syndrome (LQTS), angina, and myocardial infarction (MI), are chronologically presented. New technologies and applications from the 1990s are also introduced. In the second part, we use the bibliometric analysis method to analyze the hotspots in the field of ECG-related research. By using total citations and year-specific total citations as our main criteria, four key hotspots in ECG-related research were identified from 11 articles, including atrial fibrillation, LQTS, angina and MI, and heart rate variability. Recent studies in those four areas are also reported. In the final part, we discuss the future trends concerning ECG-related research. The authors believe that improvement of the ECG instrumentation, big data mining for ECG, and the accuracy of diagnosis and application will be areas of continuous concern.