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

Blood Pressure Estimation from Electrocardiogram and Photoplethysmography Signals Using Continuous Wavelet Transform and Convolutional Neural Network

Cuff-less and continuous blood pressure (BP) measurement has recently become an active research area in the field of remote healthcare monitoring. There is a growing demand for automated BP estimation and monitoring for various long-term and chronic conditions. Automated BP monitoring can produce a good amount of rich health data, which increases the chance of early diagnosis and treatments that are critical for a long-term condition such as hypertension and Cardiovascular diseases (CVDs). However, mining and processing this vast amount of data is challenging, which is aimed to address in this research. We employed a continuous wavelet transform (CWT) and a deep convolutional neural network (CNN) to estimate the BP. The electrocardiogram (ECG), photoplethysmography (PPG) and arterial blood pressure (ABP) signals were extracted from the online Medical Information Mart for Intensive Care (MIMIC III) database. The scalogram of each signal was created and used for training and testing our proposed CNN model that can implicitly learn to extract the descriptive features from the training data. This study achieved a promising BP estimation approach has been achieved without employing engineered feature extraction that is comparable with previous works. Experimental results demonstrated a low root mean squere error (RMSE) rate of 3.36 mmHg and a high accuracy of 86.3% for BP estimations. The proposed CNN-based model can be considered as a reliable and feasible approach to estimate BP for continuous remote healthcare monitoring

Soft Transducer for Patient’s Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection

This work addresses the design, development and implementation of a 4.0-based wearable soft transducer for patient-centered vitals telemonitoring. In particular, first, the soft transducer measures hypertension-related vitals (heart rate, oxygen saturation and systolic/diastolic pressure) and sends the data to a remote database (which can be easily consulted both by the patient and the physician). In addition to this, a dedicated deep learning algorithm, based on a Long-Short-Term-Memory Autoencoder, was designed, implemented and tested for providing an alert when the patient’s vitals exceed certain thresholds, which are automatically personalized for the specific patient. Furthermore, a mobile application (EcO2u) was developed to manage the entire data flow and facilitate the data fruition; this application also implements an innovative face-detection algorithm that ensures the identity of the patient. The robustness of the proposed soft transducer was validated experimentally on five individuals, who used the system for 30 days. The experimental results demonstrated an accuracy in anomaly detection greater than 93%, with a true positive rate of more than 94

Triggering of Ambulatory Blood Pressure Measurement Based on Patient Status: Software Architecture and Implementation

Blood pressure is an important physiological parameter that is used for both assessing immediate health status of hospitalized patients and providing indications of various cardiovascular diseases. Invasive blood pressure measurement has stayed as gold standard of blood pressure measurement while oscillometric measurement has established its position as the primary measurement method in hospital wards and home care. However, research around continuous non-invasive blood pressure measurement (CNIBP) methodologies have been growing, and blood pressure monitoring devices using CNIBP have developed recently. Applied CNIBP methods include, but are not limited to, pulse wave velocity and pulse wave analysis. In this thesis, a prototype software system for detecting significant and sustained changes in a patient’s blood pressure was designed and implemented. The system is based on pulse wave analysis based continuous blood pressure measurement algorithm. The goal was to either trigger a cuff-based measurement automatically or to prompt the user to take a new cuff measurement when needed. Characteristics of the applied CNIBP method set requirements for the system. CNIBP measurement is affected by the patient’s posture as well as movement, and therefore, information about the activity of the patient was needed. Furthermore, the ambulatory patient monitoring system, in which the prototype was integrated, set architectural requirements for the developed system. Signal fault conditions were essential to recognize and handle by the implemented software. The implemented system consists of four parts: continuous blood pressure estimation, patient activity detection, evaluation of the need for the blood pressure measurement, and notifier. The system uses a photoplethysmographic signal from an oxygen saturation sensor as an input for the blood pressure estimator. Accelerometer signals from the patient’s chest and wrist are used to detect the patient’s posture and activity. Continuous blood pressure estimate and patient activity information are used in assessing the need for a cuff-based blood pressure measurement. The system is designed to operate alongside auto-cycling ambulatory blood pressure monitoring. The algorithm that estimates blood pressure changes was provided by an external partner while the algorithm classifying the patient’s activity was developed in GE Healthcare. The algorithm that estimates the need for the blood pressure measurement was developed in a collaboration with a team of engineers working on the project. The parts of the system mentioned above were combined into the functional system and integrated into the ambulatory monitoring system. It was demonstrated that the system can detect significant and sustained blood pressure changes reliably, while at the same time discarding false readings in continuous blood pressure, as well as the blood pressure changes caused by the subject’s activity. Therefore, the system can provide actionable information about the changes in patient blood pressure and adds new value to patient monitoring

Sparsely Activated Networks: A new method for decomposing and compressing data

Recent literature on unsupervised learning focused on designing structural priors with the aim of learning meaningful features, but without considering the description length of the representations. In this thesis, first we introduce the{\phi}metric that evaluates unsupervised models based on their reconstruction accuracy and the degree of compression of their internal representations. We then present and define two activation functions (Identity, ReLU) as base of reference and three sparse activation functions (top-k absolutes, Extrema-Pool indices, Extrema) as candidate structures that minimize the previously defined metric $\varphi$. We lastly present Sparsely Activated Networks (SANs) that consist of kernels with shared weights that, during encoding, are convolved with the input and then passed through a sparse activation function. During decoding, the same weights are convolved with the sparse activation map and subsequently the partial reconstructions from each weight are summed to reconstruct the input. We compare SANs using the five previously defined activation functions on a variety of datasets (Physionet, UCI-epilepsy, MNIST, FMNIST) and show that models that are selected using $\varphi$ have small description representation length and consist of interpretable kernels.Comment: PhD Thesis in Greek, 158 pages for the main text, 23 supplementary pages for presentation, arXiv:1907.06592, arXiv:1904.13216, arXiv:1902.1112

Electronic devices and systems for monitoring of diabetes and cardiovascular diseases

Diabetes is a serious chronic disease which causes a high rate of morbidity and mortality all over the world. In 2007, more than 246 million people suffered from diabetes worldwide and unfortunately the incidence of diabetes is increasing at alarming rates. The number of people with diabetes is expected to double within the next 25 years due to a combination of population ageing, unhealthy diets, obesity and sedentary lifestyles. It can lead to blindness, heart disease, stroke, kidney failure, amputations and nerve damage. In women, diabetes can cause problems during pregnancy and make it more likely for the baby to be born with birth defects. Moreover, statistical analysis shows that 75% of diabetic patients die prematurely of cardiovascular disease (CVD). The absolute risk of cardiovascular disease in patients with type 1 (insulin-dependent) diabetes is lower than that in patients with type 2 (non-insulin-dependent) diabetes, in part because of their younger age and the lower prevalence of CVD risk factors, and in part because of the different pathophysiology of the two diseases. Unfortunately, about 9 out of 10 people with diabetes have type 2 diabetes. For these reasons, cardiopathes and diabetic patients need to be frequently monitored and in some cases they could easily perform at home the requested physiological measurements (i.e. glycemia, heart rate, blood pressure, body weight, and so on) sending the measured data to the care staff in the hospital. Several researches have been presented over the last years to address these issues by means of digital communication systems. The largest part of such works uses a PC or complex hardware/software systems for this purpose. Beyond the cost of such systems, it should be noted that they can be quite accessible by relatively young people but the same does not hold for elderly patients more accustomed to traditional equipments for personal entertainment such as TV sets. Wearable devices can permit continuous cardiovascular monitoring both in clinical settings and at home. Benefits may be realized in the diagnosis and treatment of a number of major 15 diseases. In conjunction with appropriate alarm algorithms, they can increase surveillance capabilities for CVD catastrophe for high-risk subjects. Moreover, they could play an important role in the wireless surveillance of people during hazardous operations (military, fire-fighting, etc.) or during sport activities. For patients with chronic cardiovascular disease, such as heart failure, home monitoring employing wearable device and tele-home care systems may detect exacerbations in very early stages or at dangerous levels that necessitate an emergency room visit and an immediate hospital admission. Taking into account mains principles for the design of good wearable devices and friendly tele-home care systems, such as safety, compactness, motion and other disturbance rejection, data storage and transmission, low power consumption, no direct doctor supervision, it is imperative that these systems are easy to use and comfortable to wear for long periods of time. The aim of this work is to develop an easy to use tele-home care system for diabetes and cardiovascular monitoring, well exploitable even by elderly people, which are the main target of a telemedicine system, and wearable devices for long term measuring of some parameters related to sleep apnoea, heart attack, atrial fibrillation and deep vein thrombosis. Since set-top boxes for Digital Video Broadcast Terrestrial (DVB-T) are in simple computers with their Operating System, a Java Virtual Machine, a modem for the uplink connection and a set of standard ports for the interfacing with external devices, elderly, diabetics and cardiopathes could easily send their self-made exam to the care staff placed elsewhere. The wearable devices developed are based on the well known photopletysmographic method which uses a led source/detector pair applied on the skin in order to obtain a biomedical signal related to the volume and percentage of oxygen in blood. Such devices investigate the possibility to obtain more information to those usually obtained by this technique (heart rate and percentage of oxygen saturation) in order to discover new algorithms for the continuous and remote or in ambulatory monitoring and screening of sleep apnoea, heart attack, atrial fibrillation and deep vein thrombosis