67 research outputs found
Transforming ECG Diagnosis:An In-depth Review of Transformer-based DeepLearning Models in Cardiovascular Disease Detection
The emergence of deep learning has significantly enhanced the analysis of
electrocardiograms (ECGs), a non-invasive method that is essential for
assessing heart health. Despite the complexity of ECG interpretation, advanced
deep learning models outperform traditional methods. However, the increasing
complexity of ECG data and the need for real-time and accurate diagnosis
necessitate exploring more robust architectures, such as transformers. Here, we
present an in-depth review of transformer architectures that are applied to ECG
classification. Originally developed for natural language processing, these
models capture complex temporal relationships in ECG signals that other models
might overlook. We conducted an extensive search of the latest
transformer-based models and summarize them to discuss the advances and
challenges in their application and suggest potential future improvements. This
review serves as a valuable resource for researchers and practitioners and aims
to shed light on this innovative application in ECG interpretation
Low-dimensional Denoising Embedding Transformer for ECG Classification
The transformer based model (e.g., FusingTF) has been employed recently for
Electrocardiogram (ECG) signal classification. However, the high-dimensional
embedding obtained via 1-D convolution and positional encoding can lead to the
loss of the signal's own temporal information and a large amount of training
parameters. In this paper, we propose a new method for ECG classification,
called low-dimensional denoising embedding transformer (LDTF), which contains
two components, i.e., low-dimensional denoising embedding (LDE) and transformer
learning. In the LDE component, a low-dimensional representation of the signal
is obtained in the time-frequency domain while preserving its own temporal
information. And with the low dimensional embedding, the transformer learning
is then used to obtain a deeper and narrower structure with fewer training
parameters than that of the FusingTF. Experiments conducted on the MIT-BIH
dataset demonstrates the effectiveness and the superior performance of our
proposed method, as compared with state-of-the-art methods.Comment: To appear at ICASSP 202
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
Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
Recent advancements in Large Language Models (LLMs) have drawn increasing
attention since the learned embeddings pretrained on large-scale datasets have
shown powerful ability in various downstream applications. However, whether the
learned knowledge by LLMs can be transferred to clinical cardiology remains
unknown. In this work, we aim to bridge this gap by transferring the knowledge
of LLMs to clinical Electrocardiography (ECG). We propose an approach for
cardiovascular disease diagnosis and automatic ECG diagnosis report generation.
We also introduce an additional loss function by Optimal Transport (OT) to
align the distribution between ECG and language embedding. The learned
embeddings are evaluated on two downstream tasks: (1) automatic ECG diagnosis
report generation, and (2) zero-shot cardiovascular disease detection. Our
approach is able to generate high-quality cardiac diagnosis reports and also
achieves competitive zero-shot classification performance even compared with
supervised baselines, which proves the feasibility of transferring knowledge
from LLMs to the cardiac domain.Comment: EACL 202
Deep Learning Algorithms for Time Series Analysis of Cardiovascular Monitoring Systems
This thesis investigates and develops methods to enable ubiquitous monitoring of the most examined cardiovascular signs, blood pressure, and heart rate. Their continuous measurement can help improve health outcomes, such as the detection of hypertension, heart attack, or stroke, which are the leading causes of death and disability. Recent research into wearable blood pressure monitors sought predominately to utilise a hypothesised relationship with pulse transit time, relying on quasiperiodic pulse event extractions from photoplethysmography local signal characteristics and often used only a fraction of typically bivariate time series. This limitation has been addressed in this thesis by developing methods to acquire and utilise fused multivariate time series without the need for manual feature engineering by leveraging recent advances in data science and deep learning methods that showed great data analysis potential in other domains
Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals
In this paper, we address the problem of multimodal emotion recognition from
multiple physiological signals. We demonstrate that a Transformer-based
approach is suitable for this task. In addition, we present how such models may
be pretrained in a multimodal scenario to improve emotion recognition
performances. We evaluate the benefits of using multimodal inputs and
pre-training with our approach on a state-ofthe-art dataset
Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals
Many recent studies have focused on the automatic classification of electrocardiogram (ECG) signals using deep learning (DL) methods. Most rely on existing complex DL methods, such as transfer learning or providing the models with carefully designed extracted features based on domain knowledge. A common assumption is that the deeper and more complex the DL model is, the better it learns. In this study, we propose two different DL models for automatic feature extraction from ECG signals for classification tasks: A CNN-LSTM hybrid model and an attention/transformer-based model with wavelet transform for the dimensional embedding. Both of the models extract the features from time series at the initial layers of the neural networks and can obtain performance at least equal to, if not greater than, many contemporary deep neural networks. To validate our hypothesis, we used three publicly available data-sets to evaluate the proposed models. Our model achieved a benchmark accuracy of 99.92% for fall detection and 99.93% for the PTB database for myocardial infarction versus normal heartbeat classification
Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination
Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enhancement.Methods: In this study, we have developed a new Multi-layer Perceptron (MLP) block and have used a Weight Capsule (WCapsule) network with MLP combined with sequence-to-sequence (Seq2Seq) network to classify arrhythmias. Our work is based on the MIT-BIH arrhythmia database, the original electrocardiogram (ECG) data is classified according to the criteria recommended by the American Association for Medical Instrumentation (AAMI). Also, our method’s performance is further evaluated.Results: The proposed model is evaluated using the inter-patient paradigm. Our proposed method shows an accuracy (ACC) of 99.88% under sample imbalance. For Class N, sensitivity (SEN) is 99.79%, positive predictive value (PPV) is 99.90%, and specificity (SPEC) is 99.19%. For Class S, SEN is 97.66%, PPV is 96.14%, and SPEC is 99.85%. For Class V, SEN is 99.97%, PPV is 99.07%, and SPEC is 99.94%. For Class F, SEN is 97.94%, PPV is 98.70%, and SPEC is 99.99%. When using only half of the training sample, our method shows that the SEN of Class N and V is 0.97% and 5.27% higher than the traditional machine learning algorithm.Conclusion: The proposed method combines MLP, weight capsule network with Seq2seq network, effectively addresses the problem of sample imbalance in arrhythmia classification, and produces good performance. Our method also shows promising potential in less samples
Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/
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
- …