MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals

Hong, Shenda; Xiao, Cao; Ma, Tengfei; Li, Hongyan; Sun, Jimeng

doi:10.24963/ijcai.2019/816

Cited by 57 publications

(41 citation statements)

References 5 publications

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“…The cut-off frequencies of the filters are 0.5–40 Hz. The sampling frequency of the ECG collection unit is 125 Hz, which is sufficient for a human ECG, according to the Nyquist theorem [ 16 ] and the dominant power spectral density of the ECG [ 17 ]. In addition, our ECG collection unit can also automatically detect an empty signal when the lead drops.…”

Section: Methodsmentioning

confidence: 99%

“…AI techniques have recently shown significant potential in cardiology [ 22 , 23 , 24 , 25 , 26 , 27 ] owing to their ability to automatically learn effective features from data without the help of domain experts. When focusing on deep learning methods applying ECG data, various architectures have been proposed for disease detection [ 15 , 17 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ], sleep staging [ 39 , 40 ], and biometric identification [ 41 , 42 , 43 , 44 ], among others (see a recent survey in [ 22 ]).…”

Section: Methodsmentioning

confidence: 99%

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Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management

Hong

Zhang

et al. 2021

Sensors

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The number of patients with cardiovascular diseases is rapidly increasing in the world. The workload of existing clinicians is consequently increasing. However, the number of cardiovascular clinicians is declining. In this paper, we aim to design a mobile and automatic system to improve the abilities of patients’ cardiovascular health management while also reducing clinicians’ workload. Our system includes both hardware and cloud software devices based on recent advances in Internet of Things (IoT) and Artificial Intelligence (AI) technologies. A small hardware device was designed to collect high-quality Electrocardiogram (ECG) data from the human body. A novel deep-learning-based cloud service was developed and deployed to achieve automatic and accurate cardiovascular disease detection. Twenty types of diagnostic items including sinus rhythm, tachyarrhythmia, and bradyarrhythmia are supported. Experimental results show the effectiveness of our system. Our hardware device can guarantee high-quality ECG data by removing high-/low-frequency distortion and reverse lead detection with 0.9011 Area Under the Receiver Operating Characteristic Curve (ROC–AUC) score. Our deep-learning-based cloud service supports 20 types of diagnostic items, 17 of them have more than 0.98 ROC–AUC score. For a real world application, the system has been used by around 20,000 users in twenty provinces throughout China. As a consequence, using this service, we could achieve both active and passive health management through a lightweight mobile application on the WeChat Mini Program platform. We believe that it can have a broader impact on cardiovascular health management in the world.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management

Hong

Zhang

et al. 2021

Sensors

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“…Recently, some methods try to explain the DNN model by highlighting the most relevant segments of health-condition monitoring data [ 38 ] and exploring feature effects [ 40 ] in the prediction process. Nevertheless, this kind of method cannot provide detailed domain technological-level information on “why”, as we still do not know the relationship between this kind of explanation and domain knowledge.…”

Section: Methodsmentioning

confidence: 99%

“…Nevertheless, in the real-life scenario of health-condition assessment, to realize “why” is, for the most part, more important. To explain the results of Deep Neural Network (DNN) models, most methods use the attention mechanism to highlight segments of an input record which are strongly associated with the model prediction and consider them as an explanation of prediction results [ 37 , 38 ], while others measure the role of each feature in the prediction process [ 39 , 40 ]. However, these kinds of highlighted segments and exploration of feature effects can only provide auxiliary information, which cannot be a decision-making basis actually, as they cannot provide domain technological proofs.…”

Section: Introductionmentioning

confidence: 99%

Addressing Noise and Skewness in Interpretable Health-Condition Assessment by Learning Model Confidence

Zhou

Hong

Shang

et al. 2020

Sensors

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Assessing the health condition has a wide range of applications in healthcare, military, aerospace, and industrial fields. Nevertheless, traditional feature-engineered techniques involve manual feature extraction, which are too cumbersome to adapt to the changes caused by the development of sensor network technology. Recently, deep-learning-based methods have achieved initial success in health-condition assessment research, but insufficient considerations for problems such as class skewness, noisy segments, and result interpretability make it difficult to apply them to real-world applications. In this paper, we propose a K-margin-based Interpretable Learning approach for health-condition assessment. In detail, a skewness-aware RCR-Net model is employed to handle problems of class skewness. Furthermore, we present a diagnosis model based on K-margin to automatically handle noisy segments by naturally exploiting expected consistency among the segments associated with each record. Additionally, a knowledge-directed interpretation method is presented to learn domain knowledge-level features automatically without the help of human experts which can be used as an interpretable decision-making basis. Finally, through experimental validation in the field of both medical and aerospace, the proposed method has a better generality and high efficiency with 0.7974 and 0.8005 F1 scores, which outperform all state-of-the-art deep learning methods for health-condition assessment task by 3.30% and 2.99%, respectively.

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“…Recently, deep learning methods have shown great potential in healthcare and medical area [11,16]. Specifically, there are some pioneer works that show successes of deep learning methods on ECG disease detection [1,4,6,7,15,17,19] (see [8] for a survey). However, these methods are still far away from practical applications because none of these models have been deployed for providing publicly available ECG disease detection services.…”

Section: Introductionmentioning

confidence: 99%

CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram

Hong¹,

Zhou³

et al. 2020

Companion Proceedings of the Web Conference 2020

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Electrocardiogram (ECG) is one of the most convenient and noninvasive tools for monitoring peoples' heart condition, which can use for diagnosing a wide range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome, et al. However, traditional ECG disease detection models show substantial rates of misdiagnosis due to the limitations of the abilities of extracted features. Recent deep learning methods have shown significant advantages, but they do not provide publicly available services for those who have no training data or computational resources. In this paper, we demonstrate our work on building, training, and serving such out-of-the-box cloud deep learning service for cardiac disease detection from ECG named CardioLearn. The analytic ability of any other ECG recording devices can be enhanced by connecting to the Internet and invoke our open API. As a practical example, we also design a portable smart hardware device along with an interactive mobile program, which can collect ECG and detect potential cardiac diseases anytime and anywhere.

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MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals

Cited by 57 publications

References 5 publications

Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management

Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management

Addressing Noise and Skewness in Interpretable Health-Condition Assessment by Learning Model Confidence

CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram

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