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

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Estimating blood pressure trends and the nocturnal dip from photoplethysmography

Objective: Evaluate a method for the estimation of the nocturnal systolic blood pressure dip from 24-hour blood pressure trends using a wrist-worn Photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines. Approach: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 days during which 5111 reference values for blood pressure were obtained with a 24-hour ambulatory blood pressure monitor as ground truth and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Machine learning models (linear regression, random forests, dense neural networks and long- and short-term memory neural networks) were then trained and evaluated in their capability of tracking trends in systolic and diastolic blood pressure, as well as the estimation of the nocturnal systolic blood pressure dip. Main results Best performance was obtained with a deep long- and shortterm memory neural network with a Root Mean Squared Error (RMSE) of 3.12±2.20 ∆mmHg and a correlation of 0.69 (p = 3 ∗ 10−5) with the ground truth Systolic Blood Pressure (SBP) dip. This dip was derived from trend estimates of blood pressure which had an RMSE of 8.22±1.49 mmHg for systolic and 6.55±1.39 mmHg for diastolic blood pressure. The random forest model showed slightly lower average error magnitude for SBP trends (7.86±1.57 mmHg), however Bland-Altmann analysis revealed systematic problems in its predictions that were less present in the long- and short-term memory model. Significance The work provides first evidence for the unobtrusive estimation of the nocturnal blood pressure dip, a highly prognostic clinical parameter. It is also the first to evaluate unobtrusive blood pressure measurement in a large data set of unconstrained 24-hour measurements in free-living individuals and provides evidence for the utility of long- and short-term models in this domain

Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders

Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health. The aim of this study is to analyze the impact of systolic blood pressure (SBP) on cardiac function while preserving the interpretability of the model using known clinical biomarkers in a large cohort of the UK Biobank population. We propose a novel framework that combines deep learning based estimation of interpretable clinical biomarkers from cardiac cine MR data with a variational autoencoder (VAE). The VAE architecture integrates a regression loss in the latent space, which enables the progression of cardiac health with SBP to be learnt. Results on 3,600 subjects from the UK Biobank show that the proposed model allows us to gain important insight into the deterioration of cardiac function with increasing SBP, identify key interpretable factors involved in this process, and lastly exploit the model to understand patterns of positive and adverse adaptation of cardiac function

Analysis of Machine Learning Models for Heart Disease Prediction using Different Algorithms: A Review

Now a days the heart diseases are growing very rapidly making it an important and apprehensive task of prediction of these kinds of diseases in advance. The diagnosis is also a tough chore because it has to be performed in a precise and efficient manner. The emerging technology in modern life style integrated with internet of thing which having sensors and huge amount of data is sent to various clouds for further investigation using different algorithms to fetch out precise information for various domains. Across the world approximately 3 quintillion bytes/day information generated and this data stored for further examination. As data is in huge quantity therefore, appropriate methods applied to examine the perfect analysis so that prediction can be carried out optimally. Clinical decision making is dominant to all patient care happenings which includes choosing a deed, between replacements. These days emerging field like Machine Learning play prime role in healthcare to analyze and predict the diseases. After investigating numerous research article on Machine Learning, it was found that for same data set accuracy was different for various algorithms. In our research work different machine learning techniques will be implemented and will be tested for various parameters like accuracy, precision, recall on validated dataset. ML and Neural Networks are more capable in supporting deciding and predicting from the enormous data formed by health care systems