Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis.

BACKGROUND: Long-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting. METHODS: We constructed a large-scale Chinese ECG benchmark dataset using data from 272,753 patients collected from January 2017 to December 2021. The dataset contains ECG recordings from all common arrhythmias present in the Chinese population. Several experienced cardiologists from Shanghai First People's Hospital labeled the dataset. We then developed a deep learning-based multi-label interpretable diagnostic model from the ECG recordings. We utilized Accuracy, F1 score and AUC-ROC to compare the performance of our model with that of the cardiologists, as well as with six comparison models, using testing and hidden data sets. RESULTS: The results show that our approach achieves an F1 score of 83.51%, an average AUC ROC score of 0.977, and 93.74% mean accuracy for 6 common arrhythmias. Results from the hidden dataset demonstrate the performance of our approach exceeds that of cardiologists. Our approach also highlights the diagnostic process. CONCLUSIONS: Our diagnosis system has superior diagnostic performance over that of clinicians. It also has the potential to help clinicians rapidly identify abnormal regions on ECG recordings, thus improving efficiency and accuracy of clinical ECG diagnosis in China. This approach could therefore potentially improve the productivity of out-of-hospital ECG diagnosis and provides a promising prospect for telemedicine.

Arrhythmia, also known as an irregular heartbeat, is a common cardiovascular disease. Sometimes the presence of an arrhythmia can increase the risk of more serious heart conditions. Long-term monitoring of the heartbeat enables arrhythmia to be more easily diagnosed. To accurately detect arrhythmia, we developed a computational model that was able to detect six common types of arrhythmias from readings of the heart rate obtained using a device connected to a mobile phone. We showed that our model could diagnose these arrhythmias in over 270,000 people living in China. Our diagnostic system could enable arrhythmias to be diagnosed more easily outside of hospitals and therefore improve access to healthcare, particularly for those in remote settings.

TAG: Teacher-Advice Mechanism With Gaussian Process for Reinforcement Learning.

Reinforcement learning (RL) still suffers from the problem of sample inefficiency and struggles with the exploration issue, particularly in situations with long-delayed rewards, sparse rewards, and deep local optimum. Recently, learning from demonstration (LfD) paradigm was proposed to tackle this problem. However, these methods usually require a large number of demonstrations. In this study, we present a sample efficient teacher-advice mechanism with Gaussian process (TAG) by leveraging a few expert demonstrations. In TAG, a teacher model is built to provide both an advice action and its associated confidence value. Then, a guided policy is formulated to guide the agent in the exploration phase via the defined criteria. Through the TAG mechanism, the agent is capable of exploring the environment more intentionally. Moreover, with the confidence value, the guided policy can guide the agent precisely. Also, due to the strong generalization ability of Gaussian process, the teacher model can utilize the demonstrations more effectively. Therefore, substantial improvement in performance and sample efficiency can be attained. Considerable experiments on sparse reward environments demonstrate that the TAG mechanism can help typical RL algorithms achieve significant performance gains. In addition, the TAG mechanism with soft actor-critic algorithm (TAG-SAC) attains the state-of-the-art performance over other LfD counterparts on several delayed reward and complicated continuous control environments.

Weighted Heterogeneous Graph-Based Incremental Automatic Disease Diagnosis Method.

The objective of this study is to construct a multi-department symptom-based automatic diagnosis model. However, it is difficult to establish a model to classify plenty of diseases and collect thousands of disease-symptom datasets simultaneously. Inspired by the thought of "knowledge graph is model", this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data, and incrementally injecting it into the knowledge graph. Therefore, incremental learning and injection are used to solve the data collection problem, and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems. First, an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion. Then, an adaptive neural network model is constructed for each dataset, and the data is used for statistical initialization and model training. Finally, the weights and biases of the learned neural network model are updated to the knowledge graph. It is worth noting that for the incremental process, we consider both the data and class increments. We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets. Compared with the classical model, the proposed model improves the diagnostic accuracy of the three datasets by 5%, 2%, and 15% on average, respectively. Meanwhile, the model under incremental learning has a better ability to resist forgetting.

Multiple high-regional-incidence cardiac disease diagnosis with deep learning and its potential to elevate cardiologist performance.

Currently, due to lack of large-scale datasets containing multiple arrhythmias and acute coronary syndrome-related diseases, AI-aided diagnosis for cardiac diseases is limited in clinical scenarios. Whether AI-based ECG diagnosis can assist cardiologists to improve performance has not been reported. We constructed a large-scale dataset containing multiple high-regional-incidence arrhythmias and ACS-related diseases, including 162,622 12-lead ECGs collected between January 2018 and March 2021. We presented a deep learning model for clinical ECG diagnosis of multiple cardiac diseases. Results show that our model for diagnosing 15 cardiac abnormalities achieved 88.216% accuracy, and its average AUC ROC score reached 0.961. On the board-certified re-annotated dataset, its performance surpasses that of cardiologists in non-reference group. Moreover, with aid of labels given by our model, accuracy and efficiency for cardiologist increased by 13.5% and 69.9% than non-reference group. Our approach provides solutions for AI-aided diagnosis systems of cardiac diseases in applications.

A novel P-QRS-T wave localization method in ECG signals based on hybrid neural networks.

As the number of people suffering from cardiovascular diseases increases every year, it becomes essential to have an accurate automatic electrocardiogram (ECG) diagnosis system. Researchers have adopted different methods, such as deep learning, to investigate arrhythmias classification. However, the importance of ECG waveform features is generally ignored when deep learning approaches are applied to classification tasks. P-wave, QRS-wave, and T-wave, containing plenty of physiological information, are three critical waves in the ECG heartbeat. The accurate localization of these critical ECG wave components is a prerequisite for ECG classification and diagnosis. In this study, a novel P-QRS-T wave localization method based on hybrid neural networks is proposed. The raw ECG signal is preprocessed sequentially by filtering, heartbeat extraction, and data standardization. The hybrid neural network is constructed by combining the residual neural network (ResNet) and the Long Short-Term Memory (LSTM). It predicts the relative positions of the P-peak, QRS-peak, and T-peak for each heartbeat. The proposed algorithm was validated on four ECG databases with input noise of different signal-to-noise ratio (SNR) levels. The results show that the proposed method can accurately predict the positions of the three key waves. The proposed P-QRS-T localization approach can improve the efficiency of ECG delineation. Integrated with cardiac disease classification methods, it can contribute to the development of advanced automatic ECG diagnosis systems.

The Ecological Consciousness of Natural Writing in British and American Romantic Literature.

The ecological crisis made the British and American ecological literature develop rapidly in the 20th century ecological thought. As a unique literary style that expresses the relationship between nature and man, British and American ecological literature has a far-reaching romantic tradition, and returning to nature is its eternal theme and dream. The study of the romantic tradition of British and American ecological literature has important implications for the development of ecological literature and ecological criticism. British and American romantic writers wrote their own ecological consciousness from three aspects: natural aesthetic and spiritual significance, simple ecological environmental protection consciousness, and life community. They reveal the true meaning of beauty in nature, interpret the beauty of harmony in harmony with nature, advocate returning to nature and the beautiful nature of human beings, and open up a natural path leading to truth, goodness, and beauty for people to pursue their spiritual home. In addition, they also expressed their deep concern for natural resources and the natural environment and called on people to respect life and protect and rationally use natural resources. It highlights that people are not the real masters of the nature, but as an inseparable member of the nature, they form an equal community of destiny with other creatures in the world. The value of British and American romantic literature lies in revealing the deep relationship and mutual influence between human beings and nature and prompting people to comprehend the importance of protecting the ecological environment and living in harmony with nature.

A review of arrhythmia detection based on electrocardiogram with artificial intelligence.

INTRODUCTION: With the widespread availability of portable electrocardiogram (ECG) devices, there will be a surge in ECG diagnoses. Traditional computer-aided diagnosis of arrhythmia mainly relies on the rules of medical knowledge, which are insufficient due to the limitations of data quality and human expert knowledge. The research of arrhythmia detection methods based on artificial intelligence (AI) techniques can assist physicians in high-precision arrhythmia diagnosis. AI algorithms can also be embedded in smart ECG devices to help more people perform early screening for arrhythmia. AREAS COVERED: The primary objective of this paper is to describe the application of AI methods in the process of arrhythmia detection. Meanwhile, the advantages and limitations of various approaches in different applications are summarized to provide guidance and reference for future research work. EXPERT OPINION: Machine learning (ML) and deep learning (DL) algorithms can be more effectively employed to handle ECG signal denoising and quality assessment, wave detection and delineation, and arrhythmia classification problems. The DL approach can automatically learn deep representation features and temporal features of the ECG signal for heartbeat or rhythm classification. The application of AI methods for arrhythmia detection systems will significantly relieve the pressure on physicians to analyze ECGs.

[A heart sound classification method based on joint decision of extreme gradient boosting and deep neural network].

Heart sound is one of the common medical signals for diagnosing cardiovascular diseases. This paper studies the binary classification between normal or abnormal heart sounds, and proposes a heart sound classification algorithm based on the joint decision of extreme gradient boosting (XGBoost) and deep neural network, achieving a further improvement in feature extraction and model accuracy. First, the preprocessed heart sound recordings are segmented into four status, and five categories of features are extracted from the signals based on segmentation. The first four categories of features are sieved through recursive feature elimination, which is used as the input of the XGBoost classifier. The last category is the Mel-frequency cepstral coefficient (MFCC), which is used as the input of long short-term memory network (LSTM). Considering the imbalance of the data set, these two classifiers are both improved with weights. Finally, the heterogeneous integrated decision method is adopted to obtain the prediction. The algorithm was applied to the open heart sound database of the PhysioNet Computing in Cardiology(CINC) Challenge in 2016 on the PhysioNet website, to test the sensitivity, specificity, modified accuracy and F score. The results were 93%, 89.4%, 91.2% and 91.3% respectively. Compared with the results of machine learning, convolutional neural networks (CNN) and other methods used by other researchers, the accuracy and sensibility have been obviously improved, which proves that the method in this paper could effectively improve the accuracy of heart sound signal classification, and has great potential in the clinical auxiliary diagnosis application of some cardiovascular diseases.