Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time
traces collected from surface recordings over the heart. Here we hypothesized that a deep …
traces collected from surface recordings over the heart. Here we hypothesized that a deep …
Interpretable machine learning techniques in ECG-based heart disease classification: a systematic review
Heart disease is one of the leading causes of mortality throughout the world. Among the
different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non …
different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non …
Deep learning models for electrocardiograms are susceptible to adversarial attack
Electrocardiogram (ECG) acquisition is increasingly widespread in medical and commercial
devices, necessitating the development of automated interpretation strategies. Recently …
devices, necessitating the development of automated interpretation strategies. Recently …
ECG signal classification based on deep CNN and BiLSTM
J Cheng, Q Zou, Y Zhao - BMC medical informatics and decision making, 2021 - Springer
Background Currently, cardiovascular disease has become a major disease endangering
human health, and the number of such patients is growing. Electrocardiogram (ECG) is an …
human health, and the number of such patients is growing. Electrocardiogram (ECG) is an …
Adversarial attacks and defenses in physiological computing: A systematic review
Physiological computing uses human physiological data as system inputs in real time. It
includes, or significantly overlaps with, brain-computer interfaces, affective computing …
includes, or significantly overlaps with, brain-computer interfaces, affective computing …
Detection of patient-ventilator asynchrony from mechanical ventilation waveforms using a two-layer long short-term memory neural network
L Zhang, K Mao, K Duan, S Fang, Y Lu, Q Gong… - Computers in biology …, 2020 - Elsevier
Background and objective Mismatch between invasive mechanical ventilation and the
requirements of patients results in patient-ventilator asynchrony (PVA), which is associated …
requirements of patients results in patient-ventilator asynchrony (PVA), which is associated …
[HTML][HTML] Explaining deep classification of time-series data with learned prototypes
The emergence of deep learning networks raises a need for explainable AI so that users
and domain experts can be confident applying them to high-risk decisions. In this paper, we …
and domain experts can be confident applying them to high-risk decisions. In this paper, we …
Arrhythmia detection model using modified DenseNet for comprehensible Grad-CAM visualization
JK Kim, S Jung, J Park, SW Han - Biomedical Signal Processing and …, 2022 - Elsevier
Diagnosing arrhythmia is difficult, requires significant efforts. Because arrhythmia can be
associated with serious diseases, it is important to classify arrhythmia patients with high …
associated with serious diseases, it is important to classify arrhythmia patients with high …
Automated diagnostic tool for hypertension using convolutional neural network
Background Hypertension (HPT) occurs when there is increase in blood pressure (BP)
within the arteries, causing the heart to pump harder against a higher afterload to deliver …
within the arteries, causing the heart to pump harder against a higher afterload to deliver …
[HTML][HTML] Explaining deep learning for ecg analysis: Building blocks for auditing and knowledge discovery
Deep neural networks have become increasingly popular for analyzing ECG data because
of their ability to accurately identify cardiac conditions and hidden clinical factors. However …
of their ability to accurately identify cardiac conditions and hidden clinical factors. However …