pwc-archive/papers-with-abstracts huggingface dataset:
Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks
Authors: Attila Reiss, Ina Indlekofer, Philip Schmidt, Kristof Van Laerhoven
Date: 2019-07-12 00:00:00
Proceeding: Sensors 2019 7
Tasks: Deep Learning, Heart rate estimation, Photoplethysmography (PPG)

Photoplethysmography (PPG)-based continuous heart rate monitoring is essential in a number of domains, e.g., for healthcare or fitness applications. Recently, methods based on time-frequency spectra emerged to address the challenges of motion artefact compensation. However, existing approaches are highly parametrised and optimised for specific scenarios of small, public datasets. We address this fragmentation by contributing research into the robustness and generalisation capabilities of PPG-based heart rate estimation approaches. First, we introduce a novel large-scale dataset (called PPG-DaLiA), including a wide range of activities performed under close to real-life conditions. Second, we extend a state-of-the-art algorithm, significantly improving its performance on several datasets. Third, we introduce deep learning to this domain, and investigate various convolutional neural network architectures. Our end-to-end learning approach takes the time-frequency spectra of synchronised PPG- and accelerometer-signals as input, and provides the estimated heart rate as output. Finally, we compare the novel deep learning approach to classical methods, performing evaluation on four public datasets. We show that on large datasets the deep learning model significantly outperforms other methods: The mean absolute error could be reduced by 31% on the new dataset PPG-DaLiA, and by 21% on the dataset WESAD.
A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor
Authors: Seyed M. A. Salehizadeh, Duy Dao, Jeffrey Bolkhovsky, Chae Cho, Yitzhak Mendelson and Ki H. Chon
Date: 2015-12-23 00:00:00
Proceeding: Sensors 2015 12
Tasks: Heart rate estimation, Heart Rate Variability, Photoplethysmography (PPG), Time Series Analysis

Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis. The algorithm is called Spectral filter algorithm for Motion Artifacts and heart rate reconstruction (SpaMA). The idea is to calculate the power spectral density of both PPG and accelerometer signals for each time shift of a windowed data segment. By comparing time-varying spectra of PPG and accelerometer data, those frequency peaks resulting from motion artifacts can be distinguished from the PPG spectrum. The SpaMA approach was applied to three different datasets and four types of activities: (1) training datasets from the 2015 IEEE Signal Process. Cup Database recorded from 12 subjects while performing treadmill exercise from 1 km/h to 15 km/h; (2) test datasets from the 2015 IEEE Signal Process. Cup Database recorded from 11 subjects while performing forearm and upper arm exercise. (3) Chon Lab dataset including 10 min recordings from 10 subjects during treadmill exercise. The ECG signals from all three datasets provided the reference HRs which were used to determine the accuracy of our SpaMA algorithm. The performance of the SpaMA approach was calculated by computing the mean absolute error between the estimated HR from the PPG and the reference HR from the ECG. The average estimation errors using our method on the first, second and third datasets are 0.89, 1.93 and 1.38 beats/min respectively, while the overall error on all 33 subjects is 1.86 beats/min and the performance on only treadmill experiment datasets (22 subjects) is 1.11 beats/min. Moreover, it was found that dynamics of heart rate variability can be accurately captured using the algorithm where the mean Pearson’s correlation coefficient between the power spectral densities of the reference and the reconstructed heart rate time series was found to be 0.98. These results show that the SpaMA method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities.
Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals
Authors: Tim Schäck, Michael Muma, Abdelhak M. Zoubir
Date: 2017-08-28 00:00:00
Proceeding: 2017 25th European Signal Processing Conference (EUSIPCO) 2017 8
Tasks: Heart rate estimation, Photoplethysmography (PPG)

Wearable devices that acquire photoplethysmographic (PPG) signals are becoming increasingly popular to monitor the heart rate during physical exercise. However, high accuracy and low computational complexity are conflicting requirements. We propose a method that provides highly accurate heart rate estimates at a very low computational cost in order to be implementable on wearables. To achieve the lowest possible complexity, only basic signal processing operations, i.e., correlation-based fundamental frequency estimation and spectral combination, harmonic noise damping and frequency domain tracking, are used. The proposed approach outperforms state-of-the-art methods on current benchmark data considerably in terms of computation time, while achieving a similar accuracy.
Reinforcement Learning via AIXI Approximation
Authors: Joel Veness, Kee Siong Ng, Marcus Hutter, David Silver
Date: 2010-07-13 00:00:00
Proceeding: AAAI 2010 2010 7
Tasks: General Reinforcement Learning, Open-Ended Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motivate the design of practical algorithms. We answer this hitherto open question in the affirmative, by providing the first computationally feasible approximation to the AIXI agent. To develop our approximation, we introduce a Monte Carlo Tree Search algorithm along with an agent-specific extension of the Context Tree Weighting algorithm. Empirically, we present a set of encouraging results on a number of stochastic, unknown, and partially observable domains.
Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network
Authors: Gašper Slapničar, Nejc Mlakar, Mitja Luštrek
Date: 2019-08-04 00:00:00
Proceeding: Sensors 2019 8
Tasks: Blood pressure estimation, Photoplethysmography (PPG)

Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP is a potential solution investigated in our study. We analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms, resulting in over 700 h of signals after preprocessing, belonging to 510 subjects. We then used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections. We have shown in a leave-one-subject-out experiment that the network is able to model the dependency between PPG and BP, achieving mean absolute errors of 9.43 for systolic and 6.88 for diastolic BP. Additionally we have shown that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult. We have made crucial parts of our study, especially the list of used subjects and our neural network code, publicly available, in an effort to provide a solid baseline and simplify potential comparison between future studies on an explicit MIMIC III subset.
A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals
Authors: Hao Dang, Muyi Sun, Guanhong Zhang, Xingqun Qi, Xiaoguang Zhou, Qing Chang
Date: 2019-05-24 00:00:00
Proceeding: IEEE Access 2019 5
Tasks: Arrhythmia Detection, Atrial Fibrillation Detection, Electrocardiography (ECG), General Classification, Rhythm, Specificity

Atrial fibrillation (AF), a common abnormal heartbeat rhythm, is a life-threatening recurrent disease that affects older adults. Automatic classification is one of the most valuable topics in medical sciences and bioinformatics, especially the detection of atrial fibrillation. However, it is difficult to accurately explain the local characteristics of electrocardiogram (ECG) signals by manual analysis, due to their small amplitude and short duration, coupled with the complexity and non-linearity. Hence, in this paper, we propose a novel deep arrhythmia-diagnosis method, named deep CNN-BLSTM network model, to automatically detect the AF heartbeats using the ECG signals. The model mainly consists of four convolution layers: two BLSTM layers and two fully connected layers. The datasets of RR intervals (called set A) and heartbeat sequences (P-QRS-T waves, called set B) are fed into the above-mentioned model. Most importantly, our proposed approach achieved favorable performances with an accuracy of 99.94% and 98.63% in the training and validation set of set A, respectively. In the testing set (unseen data sets), we obtained an accuracy of 96.59%, a sensitivity of 99.93%, and a specificity of 97.03%. To the best of our knowledge, the algorithm we proposed has shown excellent results compared to many state-of-art researches, which provides a new solution for the AF automatic detection.
Robust ConvNet Landmark-Based Visual Place Recognition by Optimizing Landmark Matching
Authors: Yaguang Kong (ygkong@hdu.edu.cn) and Wei Liu (weiliu994@gmail.com)
Date: 2019-02-27 00:00:00
Proceeding: journal 2019 2
Tasks: Visual Place Recognition

Visual place recognition (VPR) is a fundamental but challenging problem that has not been
solved completely for a long time, especially in a kaleidoscopic environment. Recent advanced works which
exploit ConvNet landmarks as a representation of an image for the VPR have demonstrated promising
performance under condition and viewpoint changes. In this paper, we propose an improved ConvNet
landmark-based VPR with better robustness and higher matching efficiency by extending this method from
two aspects. First, we introduce hashing to find global optimal landmark matches for each landmark in
the query image to boost the quality of matched landmark pairs. Second, we apply the sequence search for
finding the best matches basing on the temporal information attached in both query and reference images. The
experiments which conducted on four challengeable benchmark datasets show that our approach significantly
enhances the robustness of traditional ConvNet landmark-based VPR and outperforms the state-of-the-art
ConvNet feature-based VPR named SeqCNNSLAM. Moreover, our method has higher computing efficiency
than previous ConvNet landmark-based VPR.
A method for detection of atrial fibrillation using RR intervals
Authors: K. Tateno, L. Glass
Date: 2000-09-24 00:00:00
Proceeding: Computers in Cardiology 2000 9
Tasks: Atrial Fibrillation Detection, Specificity

This work describes a method for automatic detection of atrial fibrillation (AF) based on RR intervals. We define /spl Delta/RR to be the difference between successive RR intervals. The standard density histograms of RR and /spl Delta/RR intervals are determined from data in the MIT-BIH atrial fibrillation/flutter database. The present method estimates the similarity between the standard density histograms and a best density histogram by the Kolmogorov-Smirnov (KS) test. The algorithm returns significance (p) of difference between given histograms. If the p value is smaller than a value (P/sub c/), the test density histogram is significantly different from the standard density histogram. If the test density histogram is not significantly different from the standard density histogram, we say the data is AF: Using the standard density histogram of /spl Delta/RR with P/sub c/=0.01, the average sensitivity is 93.2% and the average specificity is 96.7%.
Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy
Authors: Xiaolin Zhou, Hongxia Ding, Benjamin Ung, Emma Pickwell-MacPherson, YuanTing Zhang
Date: 2014-02-17 00:00:00
Proceeding: BioMedical Engineering OnLine 2014 2
Tasks: Atrial Fibrillation Detection, Rhythm, Specificity

Background
Atrial fibrillation (AF) is the most common and debilitating abnormalities of the arrhythmias worldwide, with a major impact on morbidity and mortality. The detection of AF becomes crucial in preventing both acute and chronic cardiac rhythm disorders.

Objective
Our objective is to devise a method for real-time, automated detection of AF episodes in electrocardiograms (ECGs). This method utilizes RR intervals, and it involves several basic operations of nonlinear/linear integer filters, symbolic dynamics and the calculation of Shannon entropy. Using novel recursive algorithms, online analytical processing of this method can be achieved.

Results
Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were selected for investigation. The first database is used as a training set; in accordance with the receiver operating characteristic (ROC) curve, the best performance using this method was achieved at the discrimination threshold of 0.353: the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and overall accuracy (ACC) were 96.72%, 95.07%, 96.61% and 96.05%, respectively. The other three databases are used as testing sets. Using the obtained threshold value (i.e., 0.353), for the second set, the obtained parameters were 96.89%, 98.25%, 97.62% and 97.67%, respectively; for the third database, these parameters were 97.33%, 90.78%, 55.29% and 91.46%, respectively; finally, for the fourth set, the Sp was 98.28%. The existing methods were also employed for comparison.

Conclusions
Overall, in contrast to the other available techniques, the test results indicate that the newly developed approach outperforms traditional methods using these databases under assessed various experimental situations, and suggest our technique could be of practical use for clinicians in the future.
Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks
Authors: Runnan He, Kuanquan Wang, Na Zhao, Yang Liu, Yongfeng Yuan, Qince Li, Henggui Zhang
Date: 2018-08-30 00:00:00
Proceeding: Frontiers in Physiology 2018 8
Tasks: Atrial Fibrillation Detection, Electrocardiography (ECG), Specificity

Atrial fibrillation (AF) is the most common cardiac arrhythmias causing morbidity and mortality. AF may appear as episodes of very short (i.e., proximal AF) or sustained duration (i.e., persistent AF), either form of which causes irregular ventricular excitations that affect the global function of the heart. It is an unmet challenge for early and automatic detection of AF, limiting efficient treatment strategies for AF. In this study, we developed a new method based on continuous wavelet transform and 2D convolutional neural networks (CNNs) to detect AF episodes. The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities. Then a 2D CNN was trained to improve AF detection performance. The MIT-BIH Atrial Fibrillation Database was used for evaluating the algorithm. The efficacy of the proposed method was compared with those of some existing methods, most of which implemented the same dataset. The newly developed algorithm using CNNs achieved 99.41, 98.91, 99.39, and 99.23% for the sensitivity, specificity, positive predictive value, and overall accuracy (ACC) respectively. As the proposed algorithm targets the time-frequency feature of ECG signals rather than isolated atrial or ventricular activity, it has the ability to detect AF episodes for using just five beats, suggesting practical applications in the future.
Impact of ECG Dataset Diversity on Generalization of CNN Model for Detecting QRS Complex
Authors: Ahsan Habib, Chandan Karmakar, John Yearwood
Date: 2019-07-10 00:00:00
Proceeding: IEEE Access 2019 7
Tasks: Diversity, Electrocardiography (ECG), QRS Complex Detection

Detection of QRS complexes in electrocardiogram (ECG) signal is crucial for automated cardiac diagnosis. Automated QRS detection has been a research topic for over three decades and several of the traditional QRS detection methods show acceptable detection accuracy, however, the applicability of these methods beyond their study-specific databases was not explored. The non-stationary nature of ECG and signal variance of intra and inter-patient recordings impose significant challenges on single QRS detectors to achieve reasonable performance. In real life, a promising QRS detector may be expected to achieve acceptable accuracy over diverse ECG recordings and, thus, investigation of the model's generalization capability is crucial. This paper investigates the generalization capability of convolutional neural network (CNN) based-models from intra (subject wise leave-one-out and five-fold cross validation) and inter-database (training with single and multiple databases) points-of-view over three publicly available ECG databases, namely MIT-BIH Arrhythmia, INCART, and QT. Leave-one-out test accuracy reports 99.22%, 97.13%, and 96.25% for these databases accordingly and inter-database tests report more than 90% accuracy with the single exception of INCART. The performance variation reveals the fact that a CNN model's generalization capability does not increase simply by adding more training samples, rather the inclusion of samples from a diverse range of subjects is necessary for reasonable QRS detection accuracy.
Deep Learning for ECG Classification
Authors: Boris Pyakillya, Natasha Kazachenko, Nick Mikhailovsky
Date: 2017-01-01 00:00:00
Proceeding: Journal of Physics: Conference Series 2017 1
Tasks: Classification, Deep Learning, ECG Classification, Electrocardiography (ECG), General Classification

The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.
Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings
Authors: Sebastian D. Goodfellow, Andrew Goodwin, Robert Greer, Peter C. Laussen, Mjaye Mazwi, Danny Eytan
Date: 2018-08-17 00:00:00
Proceeding: Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR 85:83-101 2018 8
Tasks: Arrhythmia Detection, Attribute, ECG Classification, Electrocardiography (ECG), General Classification, Generative Adversarial Network, Rhythm

Access to electronic health record (EHR) data has motivated computational advances in medical research. However, various concerns, particularly over privacy, can limit access to and collaborative use of EHR data. Sharing synthetic EHR data could mitigate risk. In this paper, we propose a new approach, medical Generative Adversarial Network (medGAN), to generate realistic synthetic patient records. Based on input real patient records, medGAN can generate high-dimensional discrete variables (e.g., binary and count features) via a combination of an autoencoder and generative adversarial networks. We also propose minibatch averaging to efficiently avoid mode collapse, and increase the learning efficiency with batch normalization and shortcut connections. To demonstrate feasibility, we showed that medGAN generates synthetic patient records that achieve comparable performance to real data on many experiments including distribution statistics, predictive modeling tasks and a medical expert review. We also empirically observe a limited privacy risk in both identity and attribute disclosure using medGAN.
Support vector machine based arrhythmia classification using reduced features
Authors: Mi Hye Song, Jeon Lee, Sung Pil Cho, Kyoung Joung Lee, Sun Kook Yoo
Date: 2005-01-01 00:00:00
Proceeding: International Journal of Control, Automation, and Systems 2005 1
Tasks: Arrhythmia Detection, Classification, Electrocardiography (ECG), General Classification, Rhythm

In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were 99.307%, 99.274%, 99.854%, 98.344%, 99.441% and 99.883%, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.