2,108 research outputs found
Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery
The electrocardiogram or ECG has been in use for over 100 years and remains
the most widely performed diagnostic test to characterize cardiac structure and
electrical activity. We hypothesized that parallel advances in computing power,
innovations in machine learning algorithms, and availability of large-scale
digitized ECG data would enable extending the utility of the ECG beyond its
current limitations, while at the same time preserving interpretability, which
is fundamental to medical decision-making. We identified 36,186 ECGs from the
UCSF database that were 1) in normal sinus rhythm and 2) would enable training
of specific models for estimation of cardiac structure or function or detection
of disease. We derived a novel model for ECG segmentation using convolutional
neural networks (CNN) and Hidden Markov Models (HMM) and evaluated its output
by comparing electrical interval estimates to 141,864 measurements from the
clinical workflow. We built a 725-element patient-level ECG profile using
downsampled segmentation data and trained machine learning models to estimate
left ventricular mass, left atrial volume, mitral annulus e' and to detect and
track four diseases: pulmonary arterial hypertension (PAH), hypertrophic
cardiomyopathy (HCM), cardiac amyloid (CA), and mitral valve prolapse (MVP).
CNN-HMM derived ECG segmentation agreed with clinical estimates, with median
absolute deviations (MAD) as a fraction of observed value of 0.6% for heart
rate and 4% for QT interval. Patient-level ECG profiles enabled quantitative
estimates of left ventricular and mitral annulus e' velocity with good
discrimination in binary classification models of left ventricular hypertrophy
and diastolic function. Models for disease detection ranged from AUROC of 0.94
to 0.77 for MVP. Top-ranked variables for all models included known ECG
characteristics along with novel predictors of these traits/diseases.Comment: 13 pages, 6 figures, 1 Table + Supplemen
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
A Markov-Switching Model Approach to Heart Sound Segmentation and Classification
Objective: This paper considers challenges in developing algorithms for
accurate segmentation and classification of heart sound (HS) signals. Methods:
We propose an approach based on Markov switching autoregressive model (MSAR) to
segmenting the HS into four fundamental components each with distinct
second-order structure. The identified boundaries are then utilized for
automated classification of pathological HS using the continuous density hidden
Markov model (CD-HMM). The MSAR formulated in a state-space form is able to
capture simultaneously both the continuous hidden dynamics in HS, and the
regime switching in the dynamics using a discrete Markov chain. This overcomes
the limitation of HMM which uses a single-layer of discrete states. We
introduce three schemes for model estimation: (1.) switching Kalman filter
(SKF); (2.) refined SKF; (3.) fusion of SKF and the duration-dependent Viterbi
algorithm (SKF-Viterbi). Results: The proposed methods are evaluated on
Physionet/CinC Challenge 2016 database. The SKF-Viterbi significantly
outperforms SKF by improvement of segmentation accuracy from 71% to 84.2%. The
use of CD-HMM as a classifier and Mel-frequency cepstral coefficients (MFCCs)
as features can characterize not only the normal and abnormal morphologies of
HS signals but also morphologies considered as unclassifiable (denoted as
X-Factor). It gives classification rates with best gross F1 score of 90.19
(without X-Factor) and 82.7 (with X-Factor) for abnormal beats. Conclusion: The
proposed MSAR approach for automatic localization and detection of pathological
HS shows a noticeable performance on large HS dataset. Significance: It has
potential applications in heart monitoring systems to assist cardiologists for
pre-screening of heart pathologies
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
Detection of Bundle Branch Blocks using Machine Learning Techniques
The most effective method used for the diagnosis of heart diseases is the Electrocardiogram (ECG). The shape of the ECG signal and the time interval between its various components gives useful details about any underlying heart disease. Any dysfunction of the heart is called as cardiac arrhythmia. The electrical impulses of the heart are blocked due to the cardiac arrhythmia called Bundle Branch Block (BBB) which can be observed as an irregular ECG wave. The BBB beats can indicate serious heart disease. The precise and quick detection of cardiac arrhythmias from the ECG signal can save lives and can also reduce the diagnostics cost. This study presents a machine learning technique for the automatic detection of BBB. In this method both morphological and statistical features were calculated from the ECG signals available in the standard MIT BIH database to classify them as normal, Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB). ECG records in the MIT- BIH arrhythmia database containing Normal sinus rhythm, RBBB, and LBBB were used in the study. The suitability of the features extracted was evaluated using three classifiers, support vector machine, k-nearest neighbours and linear discriminant analysis. The accuracy of the technique is highly promising for all the three classifiers with k-nearest neighbours giving the highest accuracy of 98.2%. Since the ECG waveforms of patients with the same cardiac disorder is similar in shape, the proposed method is subject independent. The proposed technique is thus a reliable and simple method involving less computational complexity for the automatic detection of bundle branch block. This system can reduce the effort of cardiologists thereby enabling them to concentrate more on treatment of the patients
An Interoperable System For Automated Diagnosis Of Cardiac Abnormalities From Electrocardiogram Data
Electrocardiogram (ECG) data are stored and analyzed in different formats, devices, and computer platforms. As a result, ECG data from different monitoring devices cannot be displayed unless the user has access to the proprietary software of each particular device. This research describes an ontology and encoding for representation of ECG data that allows open exchange and display of ECG data in a web browser. The ontology is based on the Health Level Seven (HL7) medical device communication standard. It integrates ECG waveform data, HL7 standard ECG data descriptions, and cardiac diagnosis rules, providing a capability to both represent ECG waveforms as well as perform automated diagnosis of 37 different cardiac abnormalities. The ECG ontology is encoded in XML, thus allowing ECG data from any digital ECG device that maps to it to be displayed in a general-purpose Internet browser. An experiment was conducted to test the interoperability of the system (ability to openly share ECG data without error in a web browser) and also to assess the accuracy of the diagnosis model. Results showed 100% interoperability using 276 ECG data files and 93% accuracy in diagnosis of abnormal cardiac conditions
Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection
Cardiac auscultation involves expert interpretation of abnormalities in heart
sounds using stethoscope. Deep learning based cardiac auscultation is of
significant interest to the healthcare community as it can help reducing the
burden of manual auscultation with automated detection of abnormal heartbeats.
However, the problem of automatic cardiac auscultation is complicated due to
the requirement of reliability and high accuracy, and due to the presence of
background noise in the heartbeat sound. In this work, we propose a Recurrent
Neural Networks (RNNs) based automated cardiac auscultation solution. Our
choice of RNNs is motivated by the great success of deep learning in medical
applications and by the observation that RNNs represent the deep learning
configuration most suitable for dealing with sequential or temporal data even
in the presence of noise. We explore the use of various RNN models, and
demonstrate that these models deliver the abnormal heartbeat classification
score with significant improvement. Our proposed approach using RNNs can be
potentially be used for real-time abnormal heartbeat detection in the Internet
of Medical Things for remote monitoring applications
ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems
Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%)
Converting ECG Signals to Images for Efficient Image-text Retrieval via Encoding
Automated interpretation of electrocardiograms (ECG) has garnered significant
attention with the advancements in machine learning methodologies. Despite the
growing interest in automated ECG interpretation using machine learning, most
current studies focus solely on classification or regression tasks and overlook
a crucial aspect of clinical cardio-disease diagnosis: the diagnostic report
generated by experienced human clinicians. In this paper, we introduce a novel
approach to ECG interpretation, leveraging recent breakthroughs in Large
Language Models (LLMs) and Vision-Transformer (ViT) models. Rather than
treating ECG diagnosis as a classification or regression task, we propose an
alternative method of automatically identifying the most similar clinical cases
based on the input ECG data. Also, since interpreting ECG as images are more
affordable and accessible, we process ECG as encoded images and adopt a
vision-language learning paradigm to jointly learn vision-language alignment
between encoded ECG images and ECG diagnosis reports. Encoding ECG into images
can result in an efficient ECG retrieval system, which will be highly practical
and useful in clinical applications. More importantly, our findings could serve
as a crucial resource for providing diagnostic services in regions where only
paper-printed ECG images are accessible due to past underdevelopment.Comment: 26 page
A Hidden Markov Model for Seismocardiography
This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers (IEEE) via the DOI in this record.We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and 9 [ms], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services
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