Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Heart rate variability and target organ damage in hypertensive patients

Background: We evaluated the association between linear standard Heart Rate Variability (HRV) measures and vascular, renal and cardiac target organ damage (TOD). Methods: A retrospective analysis was performed including 200 patients registered in the Regione Campania network (aged 62.4 ± 12, male 64%). HRV analysis was performed by 24-h holter ECG. Renal damage was assessed by estimated glomerular filtration rate (eGFR), vascular damage by carotid intima-media thickness (IMT), and cardiac damage by left ventricular mass index. Results: Significantly lower values of the ratio of low to high frequency power (LF/HF) were found in the patients with moderate or severe eGFR (p-value < 0.001). Similarly, depressed values of indexes of the overall autonomic modulation on heart were found in patients with plaque compared to those with a normal IMT (p-value <0.05). These associations remained significant after adjustment for other factors known to contribute to the development of target organ damage, such as age. Moreover, depressed LF/HF was found also in patients with left ventricular hypertrophy but this association was not significant after adjustment for other factors. Conclusions: Depressed HRV appeared to be associated with vascular and renal TOD, suggesting the involvement of autonomic imbalance in the TOD. However, as the mechanisms by which abnormal autonomic balance may lead to TOD, and, particularly, to renal organ damage are not clearly known, further prospective studies with longitudinal design are needed to determine the association between HRV and the development of TOD

A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure

Hypertension or high blood pressure is a leading cause of death throughout the world and a critical factor for increasing the risk of serious diseases, including cardiovascular diseases such as stroke and heart failure. Blood pressure is a primary vital sign that must be monitored regularly for the early detection, prevention and treatment of cardiovascular diseases. Traditional blood pressure measurement techniques are either invasive or cuff-based, which are impractical, intermittent, and uncomfortable for patients. Over the past few decades, several indirect approaches using photoplethysmogram (PPG) have been investigated, namely, pulse transit time, pulse wave velocity, pulse arrival time and pulse wave analysis, in an effort to utilise PPG for estimating blood pressure. Recent advancements in signal processing techniques, including machine learning and artificial intelligence, have also opened up exciting new horizons for PPG-based cuff less and continuous monitoring of blood pressure. Such a device will have a significant and transformative impact in monitoring patients’ vital signs, especially those at risk of cardiovascular disease. This paper provides a comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations

Review of sensors for remote patient monitoring

Remote patient monitoring (RPM) of physiological measurements can provide an efficient method and high quality care to patients. The physiological signals measurement is the initial and the most important factor in RPM. This paper discusses the characteristics of the most popular sensors, which are used to obtain vital clinical signals in prevalent RPM systems. The sensors discussed in this paper are used to measure ECG, heart sound, pulse rate, oxygen saturation, blood pressure and respiration rate, which are treated as the most important vital data in patient monitoring and medical examination

IAEA Atlas of Cardiac PET/CT

This open access book presents a wide portfolio of examples of positron emission tomography coupled with computer tomography (PET/CT) studies in various cardiac conditions in order to provide a rationale for the implementation of this technology in an array of clinical conditions. Cardiovascular diseases are a major contributor to premature morbidity and mortality worldwide. Low- and middle-income countries (LMICs) are particularly affected by cardiovascular diseases (CVDs), with more than 75% of all CVDs deaths occurring in these countries. For this reason, target 3.4 of the United Nations (UN) Sustainable Development Goals (SDGs) agenda aims at a 30% reduction in premature mortality due to non-communicable diseases (NCDs), which include CVDs, by 2030. Among CVDs, ischemic heart disease (IHD) plays an important role and, according to the Institute for Health Metrics and Evaluation (IHME), it was responsible for 15.96% of global deaths in 2017. Between 2000 and 2017, the number of IHD deaths worldwide increased by 0.26% per year. Several imaging tools help to non-invasively diagnose, stratify risk and guide management in cardiac disease. They include nuclear cardiology techniques, using either SPECT (single photon emission computed tomography) or PET/CT. While myocardial imaging with SPECT has been fully embraced by the cardiology community and is widely available worldwide, PET/CT introduction has been slower, due not only to its higher costs, but also to the limited availability of PET/VCT scanners, mostly utilized for oncological applications. This book is an invaluable tool for nuclear medicine physicians, cardiologists and radiologists

IAEA Atlas of Cardiac PET/CT

This open access book presents a wide portfolio of examples of positron emission tomography coupled with computer tomography (PET/CT) studies in various cardiac conditions in order to provide a rationale for the implementation of this technology in an array of clinical conditions. Cardiovascular diseases are a major contributor to premature morbidity and mortality worldwide. Low- and middle-income countries (LMICs) are particularly affected by cardiovascular diseases (CVDs), with more than 75% of all CVDs deaths occurring in these countries. For this reason, target 3.4 of the United Nations (UN) Sustainable Development Goals (SDGs) agenda aims at a 30% reduction in premature mortality due to non-communicable diseases (NCDs), which include CVDs, by 2030. Among CVDs, ischemic heart disease (IHD) plays an important role and, according to the Institute for Health Metrics and Evaluation (IHME), it was responsible for 15.96% of global deaths in 2017. Between 2000 and 2017, the number of IHD deaths worldwide increased by 0.26% per year. Several imaging tools help to non-invasively diagnose, stratify risk and guide management in cardiac disease. They include nuclear cardiology techniques, using either SPECT (single photon emission computed tomography) or PET/CT. While myocardial imaging with SPECT has been fully embraced by the cardiology community and is widely available worldwide, PET/CT introduction has been slower, due not only to its higher costs, but also to the limited availability of PET/VCT scanners, mostly utilized for oncological applications. This book is an invaluable tool for nuclear medicine physicians, cardiologists and radiologists

Non-invasive cardiac imaging techniques and vascular tools for the assessment of cardiovascular disease in type 2 diabetes mellitus

Cardiovascular disease is the major cause of mortality in type 2 diabetes mellitus. The criteria for the selection of those asymptomatic patients with type 2 diabetes who should undergo cardiac screening and the therapeutic consequences of screening remain controversial. Non-invasive techniques as markers of atherosclerosis and myocardial ischaemia may aid risk stratification and the implementation of tailored therapy for the patient with type 2 diabetes. In the present article we review the literature on the implementation of non-invasive vascular tools and cardiac imaging techniques in this patient group. The value of these techniques as endpoints in clinical trials and as risk estimators in asymptomatic diabetic patients is discussed. Carotid intima–media thickness, arterial stiffness and flow-mediated dilation are abnormal long before the onset of type 2 diabetes. These vascular tools are therefore most likely to be useful for the identification of ‘at risk’ patients during the early stages of atherosclerotic disease. The additional value of these tools in risk stratification and tailored therapy in type 2 diabetes remains to be proven. Cardiac imaging techniques are more justified in individuals with a strong clinical suspicion of advanced coronary heart disease (CHD). Asymptomatic myocardial ischaemia can be detected by stress echocardiography and myocardial perfusion imaging. The more recently developed non-invasive multi-slice computed tomography angiography is recommended for exclusion of CHD, and can therefore be used to screen asymptomatic patients with type 2 diabetes, but has the associated disadvantages of high radiation exposure and costs. Therefore, we propose an algorithm for the screening of asymptomatic diabetic patients, the first step of which consists of coronary artery calcium score assessment and exercise ECG

Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01