Autonomic and brain morphological predictors of stress resilience

Stressful life events are an important cause of psychopathology. Humans exposed to aversive or stressful experiences show considerable inter-individual heterogeneity in their responses. However, the majority does not develop stress-related psychiatric disorders. The dynamic processes encompassing positive and functional adaptation in the face of significant adversity have been broadly defined as resilience. Traditionally, the assessment of resilience has been confined to self-report measures, both within the general community and putative high-risk populations. Although this approach has value, it is highly susceptible to subjective bias and may not capture the dynamic nature of resilience, as underlying construct. Recognizing the obvious benefits of more objective measures of resilience, research in the field has just started investigating the predictive value of several potential biological markers. This review provides an overview of theoretical views and empirical evidence suggesting that individual differences in heart rate variability (HRV), a surrogate index of resting cardiac vagal outflow, may underlie different levels of resilience toward the development of stress-related psychiatric disorders. Following this line of thought, recent studies describing associations between regional brain morphometric characteristics and resting state vagally-mediated HRV are summarized. Existing studies suggest that the structural morphology of the anterior cingulated cortex (ACC), particularly its cortical thickness, is implicated in the expression of individual differences in HRV. These findings are discussed in light of emerging structural neuroimaging research, linking morphological characteristics of the ACC to psychological traits ascribed to a high-resilient profile and abnormal structural integrity of the ACC to the psychophysiological expression of stress-related mental health consequences. We conclude that a multidisciplinary approach integrating brain structural imaging with HRV monitoring could offer novel perspectives about brain-body pathways in resilience and adaptation to psychological stres

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Influence of autonomic nervous system in the inducibility of atrial fibrillation.

Cílem této práce je zjištění změn předcházejícím fibrilaci síní. Pozorována je rovnováha mezi sympatikem a parasympatikem. Do experimentu výzkumného ústavu Cleavlendské kliniky bylo zapojeno šest psů různých ras. Signály EKG byly získány Holterovským 24hodinovým monitorováním. Pomocí 40 vysokofrekvenčních impulsů (TI) byla každých 30 minut vyvolávána AF. Z 24hodinového signálu byly extrahovány kratší epizody. Každá z těchto epizod obsahovala 10 minut předcházejících TI a 3 minuty následující po TI. Desetiminutové epizody byly zpracovány automaticky, byly detekovány QRS komplexy a RR intervaly a vypočteny HRV parametry. Přítomnost a délka trvání AF byly zjištěny manuálně z tříminutových intervalů následujících po TI. Byla-li vyvolána AF o délce trvání kratší než 30 sekund došlo ve srovnání s epizodami bez výskytu AF k významným změnám tří HRV parametrů. HF parametr poklesl pro epizody s výskytem AF. LF parametr byl naopak vyšší v epizodách s AF. Pro AF delší než 30 sekund nebyly významné změny pozorovány. Změny v epizodách s krátkou AF mohly být způsobeny změnami vlivu sympatiku a parasympatiku. Ke vzniku dlouhých AF je pravděpodobně zapotřebí i jiného vlivu, který nemusí nutně souviset s nervovým systémem. K dalším analýzám je zapotřebí většího množství signálů.The aim of this study is to investigate changes in sympatho-vagal balance before the initiation of AF. Six mongrel dogs from the Cleveland Clinic foundation were included in this study. ECG was recorded for 24 hours using telemetric Holter monitoring. AF was periodically induced every 30 min. by applying brief bursts of 40 high-frequency atrial train impulses (TI). From the 24 hours signals' traces shorter data episodes were extracted. Each episode consisted of 10 minutes preceding the atrial burst, and 3 minutes following the (TI). The 10 minutes episodes were processed automatically to determine the QRS complexes and RR intervals, and to calculate the HRV parameters. The presence and the duration of AF were determined by manual examination in each of the 3 minutes intervals following the delivery of TI. When the AF was generated, but episodes of AF were shorter than 30 seconds, three HRV parameters were significantly different than when AF was not generated. The HF component was lower in episodes that generated AF. The LF component was higher in episodes that generated AF. No significant differences were found when episodes of AF were longer than 30 seconds. Short episodes of AF could be generated when a certain disorder between sympathetic and parasympathetic tone is present. However in order to be able to generate longer AF episodes it is necessary another component not necessary related to the nervous system. Further analysis with a higher number of dogs should be needed.

A pilot study of patch Holter electrocardiograph recordings in healthy cats

Background: A patch Holter electrocardiograph (P-Holter) is cordless, making it lightweight, unlike the conventional Holter electrocardiograph (C-Holter). A P-Holter can also take continuous measurements for up to 14 days without replacing the battery or SD card.Aim: To compare the performance of the P-Holter and the C-Holter in healthy cats. Additionally, we aimed to investigate whether multiday recordings with the P-Holter decrease sympathetic nerve activity or improve the accuracy of arrhythmia detection.Methods: Five healthy domestic short-haired cats were used for this study. Both a P-Holter and C-Holter were used on the first day, but only the P-Holter was used on days 2–6. The evaluated variables were the analyzable time of both Holter types, heart rate (HR), HR variability (HRV), and the number of arrhythmia occurrences.Results: For two out of the five cats, measurement of P-Holter was interrupted. Eventually, continuous recordings using the P-Holters were able to be collected from all individuals for 6 days. The 24 hours analyzable time from the P-Holter and C-Holter was almost identical (p = 0.94). The 24 hours mean HR did not differ across Holter types (p = 0.67). In addition, the timing of the occurrences of arrhythmias was almost identical to the P-Holter and C-Holter. Results of HRV suggested that sympathetic nerve activity was likely to decrease and vagal nerve activity was likely to increase after 4–5 days of measurement, compared to the second day of measurement (p < 0.05). When only the P-Holter was installed, the number of arrhythmia occurrences was similar on days 2–6.Conclusion: In this study, the P-Holter may be as useful as the C-Holter in cats with suspected intermittent arrhythmias, although the P-Holters were placed on cats without a clinical indication. However, cats may have individual differences in their adaptation to the device. P-Holter recordings taken for more than 4–5 days may allow the cat to acclimate to the device and reduce sympathetic nerve activity. The accuracy of arrhythmia detection across multiday P-Holter recordings requires further investigation using clinical cases

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data

Lifestyle and atrial fibrillation

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Wearable technology and the cardiovascular system: the future of patient assessment

The past decade has seen a dramatic rise in consumer technologies able to monitor a variety of cardiovascular parameters. Such devices initially recorded markers of exercise, but now include physiological and health-care focused measurements. The public are keen to adopt these devices in the belief that they are useful to identify and monitor cardiovascular disease. Clinicians are therefore often presented with health app data accompanied by a diverse range of concerns and queries. Herein, we assess whether these devices are accurate, their outputs validated, and whether they are suitable for professionals to make management decisions. We review underpinning methods and technologies and explore the evidence supporting the use of these devices as diagnostic and monitoring tools in hypertension, arrhythmia, heart failure, coronary artery disease, pulmonary hypertension, and valvular heart disease. Used correctly, they might improve health care and support research