Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient’s ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.

Myocardial ischemia-reperfusion (I/R) injury is associated with systemic oxidative stress, cardiac mitochondrial homeostasis, and cardiomyocyte apoptosis. Metformin was recognized to attenuate cardiomyocyte apoptosis. However, the longitudinal effects and pathomechanism of metformin on the regulation of myocardial mitohormesis following I/R treatment remain unclear. The aim of this study was to investigate the longitudinal effects and mechanism of metformin in regulating cardiac mitochondrial homeostasis by serial imaging with the 18-kDa translocator protein (TSPO)-targeted positron emission tomography (PET) tracer 18 F-FDPA. MethodsMyocardial I/R injury was established in Sprague-Dawley rats, which were treated with or without metformin (150 mg/kg per day). Serial gated 18 F-FDG and 18 F-FDPA PET imaging were performed at 1, 4, and 8 weeks after surgery, followed by analysis of ventricular remodelling and cardiac mitochondrial homeostasis. After PET imaging, the activity of antioxidant enzymes, immunostaining, and western blot analysis were performed to analyse the spatio-temporal effects and pathomechanism of metformin for cardiac protection after myocardial I/R injury. ResultsOxidative stress and apoptosis increased one week after myocardial I/R injury (before signi cant progression of ventricular remodelling). TSPO co-localized with in ammatory CD68 + macrophages in the infarct area, and upregulation of AMPK-p/AMPK and down-regulation of Bcl-2/Bax were observed. However, these effects were reversed with metformin treatment. Eight weeks after myocardial I/R injury (representing the advanced stage of heart failure), 18 F-FDPA uptake activity in myocardial cells in the distal non-infarct area increased without CD68 + expression, whereas the activity decreased with metformin treatment. ConclusionTaken together, these results show that prolonged metformin treatment has pleiotropic protective effects against myocardial I/R injury associated with a regional and temporal dynamic balance between mitochondrial homeostasis and cardiac outcome, which were assessed by TSPO-targeted imaging during cardiac remodelling.

Objectives Antidepressants are first-line treatments for major depressive disorder (MDD), but 40–60% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict treatment outcomes based on clinical symptoms and episode features. We sought to independently replicate recent machine learning methodology predicting antidepressant outcomes using the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, and then externally validate these methods to train models using data from the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) dataset. Methods We replicated methodology from Nie et al (2018) using common algorithms based on linear regressions and decision trees to predict treatment-resistant depression (TRD, defined as failing to respond to 2 or more antidepressants) in the STAR*D dataset. We then trained and externally validated models using the clinical features found in both datasets to predict response (≥50% reduction on the Quick Inventory for Depressive Symptomatology, Self-Rated [QIDS-SR]) and remission (endpoint QIDS-SR score ≤5) in the CAN-BIND-1 dataset. We evaluated additional models to investigate how different outcomes and features may affect prediction performance. Results Our replicated models predicted TRD in the STAR*D dataset with slightly better balanced accuracy than Nie et al (70%-73% versus 64%-71%, respectively). Prediction performance on our external methodology validation on the CAN-BIND-1 dataset varied depending on outcome; performance was worse for response (best balanced accuracy 65%) compared to remission (77%). Using the smaller set of features found in both datasets generally improved prediction performance when evaluated on the STAR*D dataset. Conclusion We successfully replicated prior work predicting antidepressant treatment outcomes using machine learning methods and clinical data. We found similar prediction performance using these methods on an external database, although prediction of remission was better than prediction of response. Future work is needed to improve prediction performance to be clinically useful.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.

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