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Explainability Metrics of Deep Convolutional Networks for Photoplethysmography Quality Assessment

https://doi.org/10.1109/access.2021.3054613
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/jamacardio.2018.0136openalex-snapshot
Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch
inferredcites →https://doi.org/10.1001/jamainternmed.2018.3763openalex-snapshot
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
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Visualizing and Understanding Convolutional Networks
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Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor
inferredcites →https://doi.org/10.1038/s41746-019-0207-9openalex-snapshot
Photoplethysmography based atrial fibrillation detection: a review
inferredcites →https://doi.org/10.1088/0967-3334/28/3/r01openalex-snapshot
Photoplethysmography and its application in clinical physiological measurement
inferredcites →https://doi.org/10.1088/1361-6579/ab5b84openalex-snapshot
Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation
inferredcites →https://doi.org/10.1109/access.2017.2707472openalex-snapshot
A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPG
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.354openalex-snapshot
Network Dissection: Quantifying Interpretability of Deep Visual Representations
inferredcites →https://doi.org/10.1109/ecace.2019.8679356openalex-snapshot
A Machine Learning Approach for Heart Rate Estimation from PPG Signal using Random Forest Regression Algorithm
inferredcites →https://doi.org/10.1109/icassp.2017.7952190openalex-snapshot
Very deep convolutional neural networks for raw waveforms
inferredcites →https://doi.org/10.1109/jbhi.2019.2909065openalex-snapshot
A Supervised Approach to Robust Photoplethysmography Quality Assessment
inferredcites →https://doi.org/10.1109/tbcas.2019.2892297openalex-snapshot
CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment
inferredcites →https://doi.org/10.1109/tpami.2020.2982882openalex-snapshot
Interpretable CNNs for Object Classification
inferredcites →https://doi.org/10.1136/heartjnl-2018-313147openalex-snapshot
Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms
inferredcites →https://doi.org/10.1145/2783258.2788613openalex-snapshot
Intelligible Models for HealthCare
inferredcites →https://doi.org/10.1145/3219819.3219912openalex-snapshot
Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks
inferredcites →https://doi.org/10.1145/3233547.3233667openalex-snapshot
Interpretable Machine Learning in Healthcare
inferredcites →https://doi.org/10.1145/3292500.3330657openalex-snapshot
Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning
inferredcites →https://doi.org/10.1371/journal.pone.0110274openalex-snapshot
Insights into the Problem of Alarm Fatigue with Physiologic Monitor Devices: A Comprehensive Observational Study of Consecutive Intensive Care Unit Patients
inferredcites →https://doi.org/10.1609/aaai.v32i1.11501openalex-snapshot
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
inferredcites →https://doi.org/10.2196/12770openalex-snapshot
Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study
inferredcites →https://doi.org/10.3390/electronics8080832openalex-snapshot
Machine Learning Interpretability: A Survey on Methods and Metrics
inferredcites →https://doi.org/10.3390/s19143079openalex-snapshot
Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot
AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
inferredcites →https://doi.org/10.48550/arxiv.1412.6806openalex-snapshot
Striving for Simplicity: The All Convolutional Net
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1602.03616openalex-snapshot
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1605.09304openalex-snapshot
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
inferredcites →https://doi.org/10.48550/arxiv.1703.01365openalex-snapshot
Axiomatic Attribution for Deep Networks
inferredcites →https://doi.org/10.48550/arxiv.1711.06178openalex-snapshot
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
inferredcites →https://doi.org/10.48550/arxiv.1712.09923openalex-snapshot
What do we need to build explainable AI systems for the medical domain?

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4665555openalex-extracted
inferred← citeshttps://doi.org/10.1002/jmri.29639openalex-snapshot
Development of a Dual‐Plane MRI ‐Based Deep Learning Model to Assess the 1‐Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy
inferred← citeshttps://doi.org/10.1002/widm.1530openalex-snapshot
Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)
inferred← citeshttps://doi.org/10.1007/978-3-031-70959-3_16openalex-snapshot
Recursive Division Explainability as a Factor of CNN Quality
inferred← citeshttps://doi.org/10.1007/978-981-95-1248-5_5openalex-snapshot
XAI: Focusing on Fertility Assessment
inferred← citeshttps://doi.org/10.1007/s10462-022-10304-3openalex-snapshot
Explainable AI for clinical and remote health applications: a survey on tabular and time series data
inferred← citeshttps://doi.org/10.1007/s10462-024-10852-wopenalex-snapshot
A review of evaluation approaches for explainable AI with applications in cardiology
inferred← citeshttps://doi.org/10.1007/s10489-024-05354-9openalex-snapshot
Video-based beat-by-beat blood pressure monitoring via transfer deep-learning
inferred← citeshttps://doi.org/10.1007/s12194-023-00756-9openalex-snapshot
Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier
inferred← citeshttps://doi.org/10.1016/b978-0-323-90585-5.00003-5openalex-snapshot
Personalized decision support for cardiology based on deep learning: an overview
inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106240openalex-snapshot
Schrödinger spectrum and slim CNN architecture-based signal quality estimation for Photoplethysmogram signals
inferred← citeshttps://doi.org/10.1016/j.cmpb.2022.107161openalex-snapshot
Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022)
inferred← citeshttps://doi.org/10.1038/s42256-024-00898-4openalex-snapshot
Sparse learned kernels for interpretable and efficient medical time series processing
inferred← citeshttps://doi.org/10.1088/1361-6579/ac3b3dopenalex-snapshot
A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables
inferred← citeshttps://doi.org/10.1088/2057-1976/ae250fopenalex-snapshot
Assessing photoplethysmography signal quality for wearable devices during unrestricted daily activities
inferred← citeshttps://doi.org/10.1101/2021.08.19.456935openalex-snapshot
Characterising the Photoplethysmography Pulse Waveform for Use in Human Neuroscience: The Hybrid Excess and Decay (HED) Model
inferred← citeshttps://doi.org/10.1109/access.2022.3175436openalex-snapshot
Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation
inferred← citeshttps://doi.org/10.1109/cac63892.2024.10865774openalex-snapshot
A New Belief Rule Base Considering Explainability Optimization for Complex System Health State Assessment
inferred← citeshttps://doi.org/10.1109/embc48229.2022.9871494openalex-snapshot
Signal Quality Assessment of Photoplethysmogram Signals using Quantum Pattern Recognition Technique and lightweight CNN Module
inferred← citeshttps://doi.org/10.1109/embc48229.2022.9871741openalex-snapshot
Design of a Realtime Photoplethysmogram Signal Quality Checker for Wearables and Edge Computing
inferred← citeshttps://doi.org/10.1109/icaiot57170.2022.10121884openalex-snapshot
CNN Based PPG Signal Quality Assessment Using Raw PPG Signal for Energy-Efficient PPG Analysis Devices in Internet of Medical Things
inferred← citeshttps://doi.org/10.1109/jsen.2023.3234171openalex-snapshot
On-Device Multi-Level Signal Quality Aware Compression for Energy-Efficient Wearable PPG Sensing
inferred← citeshttps://doi.org/10.1109/tim.2024.3351256openalex-snapshot
On-Device Signal Quality Guided and Embedded Physiologic Information for High Fidelity Continuous PPG Compression
inferred← citeshttps://doi.org/10.12688/wellcomeopenres.17855.1openalex-snapshot
The Hybrid Excess and Decay (HED) model: an automated approach to characterising changes in the photoplethysmography pulse waveform
inferred← citeshttps://doi.org/10.31219/osf.io/5pj6aopenalex-snapshot
Machine Learning Approaches of cotact PPG and its Potential Research Challanges
inferred← citeshttps://doi.org/10.31219/osf.io/w8n2sopenalex-snapshot
Plethysmography Signal Assessment using Deep Learning Algorithms
inferred← citeshttps://doi.org/10.3390/app12199582openalex-snapshot
A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
inferred← citeshttps://doi.org/10.3390/s23239382openalex-snapshot
BCG Signal Quality Assessment Based on Time-Series Imaging Methods
inferred← citeshttps://doi.org/10.36227/techrxiv.24573304openalex-snapshot
A Review of Evaluation Approaches for Explainable AI With Applications in Cardiology
inferred← citeshttps://doi.org/10.36227/techrxiv.24573304.v1openalex-snapshot
A Review of Evaluation Approaches for Explainable AI With Applications in Cardiology
inferred← citeshttps://doi.org/10.53730/ijhs.v6ns6.12541openalex-snapshot
Design of an intrinsically scalable model for medical disease identification using hybrid swarm intelligence with augmented transfer learning
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