| 1. |
- Abdukalikova, Anara, et al.
(författare)
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Detection of Atrial Fibrillation from Short ECGs : Minimalistic Complexity Analysis for Feature-Based Classifiers
- 2018
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Ingår i: Computing in Cardiology 2018. - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- In order to facilitate data-driven solutions for early detection of atrial fibrillation (AF), the 2017 CinC conference challenge was devoted to automatic AF classification based on short ECG recordings. The proposed solutions concentrated on maximizing the classifiers F 1 score, whereas the complexity of the classifiers was not considered. However, we argue that this must be addressed as complexity places restrictions on the applicability of inexpensive devices for AF monitoring outside hospitals. Therefore, this study investigates the feasibility of complexity reduction by analyzing one of the solutions presented for the challenge.
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| 2. |
- Bandaragoda, Tharindu, et al.
(författare)
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Trajectory clustering of road traffic in urban environments using incremental machine learning in combination with hyperdimensional computing
- 2019
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Ingår i: The 2019 IEEE Intelligent Transportation Systems Conference - ITSC. - : IEEE. - 9781538670248 - 9781538670255 ; , s. 1664-1670
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Konferensbidrag (refereegranskat)abstract
- Road traffic congestion in urban environments poses an increasingly complex challenge of detection, profiling and prediction. Although public policy promotes transport alternatives and new infrastructure, traffic congestion is highly prevalent and continues to be the lead cause for numerous social, economic and environmental issues. Although a significant volume of research has been reported on road traffic prediction, profiling of traffic has received much less attention. In this paper we address two key problems in traffic profiling by proposing a novel unsupervised incremental learning approach for road traffic congestion detection and profiling, dynamically over time. This approach uses (a) hyperdimensional computing to enable capture variable-length trajectories of commuter trips represented as vehicular movement across intersections, and (b) transforms these into feature vectors that can be incrementally learned over time by the Incremental Knowledge Acquiring Self-Learning (IKASL) algorithm. The proposed approach was tested and evaluated on a dataset consisting of approximately 190 million vehicular movement records obtained from 1,400 Bluetooth identifiers placed at the intersections of the arterial road network in the State of Victoria, Australia.
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| 3. |
- Kleyko, Denis, 1990-, et al.
(författare)
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A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation : Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 2017
- 2020
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Ingår i: Biomedical Engineering & Physics Express. - : Institute of Physics Publishing (IOPP). - 2057-1976. ; 6:2
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The 2017 PhysioNet/CinC Challenge focused on automatic classification of atrial fibrillation (AF) in short ECGs. This study aimed to evaluate the use of the data and results from the challenge for detection of AF in longer ECGs, taken from three other PhysioNet datasets.Approach: The used data-driven models were based on features extracted from ECG recordings, calculated according to three solutions from the challenge. A Random Forest classifier was trained with the data from the challenge. The performance was evaluated on all non-overlapping 30 s segments in all recordings from three MIT-BIH datasets. Fifty-six models were trained using different feature sets, both before and after applying three feature reduction techniques.Main Results: Based on rhythm annotations, the AF proportion was 0.00 in the MIT-BIH Normal Sinus Rhythm (N = 46083 segments), 0.10 in the MIT-BIH Arrhythmia (N = 2880), and 0.41 in the MIT-BIH Atrial Fibrillation (N = 28104) dataset. For the best performing model, the corresponding detected proportions of AF were 0.00, 0.11 and 0.36 using all features, and 0.01, 0.10 and 0.38 when using the 15 best performing features.Significance: The results obtained on the MIT-BIH datasets indicate that the training data and solutions from the 2017 Physionet/Cinc Challenge can be useful tools for developing robust AF detectors also in longer ECG recordings, even when using a low number of carefully selected features. The use of feature selection allows significantly reducing the number of features while preserving the classification performance, which can be important when building low-complexity AF classifiers on ECG devices with constrained computational and energy resources.
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| 4. |
- Kleyko, Denis, 1990-, et al.
(författare)
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A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization during Paced Deep Breathing
- 2019
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 7, s. 34403-34415
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Autonomic function during deep breathing (DB) is normally scored based on the assumption that the heart rate is synchronized with the breathing. We have observed individuals with subtle arrhythmias during DB where autonomic function cannot be evaluated. This study presents a novel method for analyzing cardiorespiratory synchronization: feature-based analysis of the similarity between heart rate and respiration using principles of hyperdimensional computing. Methods: Heart rate and respiration signals were modeled using Fourier series analysis. Three feature variables were derived and mapped to binary vectors in a high-dimensional space. Using both synthesized data and recordings from patients/healthy subjects, the similarity between the feature vectors was assessed using Hamming distance (high-dimensional space), Euclidean distance (original space), and with a coherence-based index. Methods were evaluated via classification of the similarity indices into three groups. Results: The distance-based methods achieved good separation of signals into classes with different degree of cardiorespiratory synchronization, also providing identification of patients with low cardiorespiratory synchronization but high values of conventional DB scores. Moreover, binary high-dimensional vectors allowed an additional analysis of the obtained Hamming distance. Conclusions: Feature-based similarity analysis using hyperdimensional computing is capable of identifying signals with low cardiorespiratory synchronization during DB due to arrhythmias. Vector-based similarity analysis could be applied to other types of feature variables than based on spectral analysis. Significance: The proposed methods for robustly assessing cardiorespiratory synchronization during DB facilitate the identification of individuals where the evaluation of autonomic function is problematic or even impossible, thus, increasing the correctness of the conventional DB scores.
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| 5. |
- Kleyko, Denis, et al.
(författare)
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Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks
- 2021
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Ingår i: IEEE Transactions on Neural Networks and Learning Systems. - : Institute of Electrical and Electronics Engineers Inc.. - 2162-237X .- 2162-2388. ; 32:8, s. 3777-3783
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Tidskriftsartikel (refereegranskat)abstract
- The deployment of machine learning algorithms on resource-constrained edge devices is an important challenge from both theoretical and applied points of view. In this brief, we focus on resource-efficient randomly connected neural networks known as random vector functional link (RVFL) networks since their simple design and extremely fast training time make them very attractive for solving many applied classification tasks. We propose to represent input features via the density-based encoding known in the area of stochastic computing and use the operations of binding and bundling from the area of hyperdimensional computing for obtaining the activations of the hidden neurons. Using a collection of 121 real-world data sets from the UCI machine learning repository, we empirically show that the proposed approach demonstrates higher average accuracy than the conventional RVFL. We also demonstrate that it is possible to represent the readout matrix using only integers in a limited range with minimal loss in the accuracy. In this case, the proposed approach operates only on small ${n}$ -bits integers, which results in a computationally efficient architecture. Finally, through hardware field-programmable gate array (FPGA) implementations, we show that such an approach consumes approximately 11 times less energy than that of the conventional RVFL.
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| 6. |
- Kleyko, Denis, 1990-, et al.
(författare)
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Distributed Representation of n-gram Statistics for Boosting Self-organizing Maps with Hyperdimensional Computing
- 2019
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Ingår i: Perspectives of System Informatics. - Cham : Springer. ; , s. 64-79, s. 64-79
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Konferensbidrag (refereegranskat)abstract
- This paper presents an approach for substantial reduction of the training and operating phases of Self-Organizing Maps in tasks of 2-D projection of multi-dimensional symbolic data for natural language processing such as language classification, topic extraction, and ontology development. The conventional approach for this type of problem is to use n-gram statistics as a fixed size representation for input of Self-Organizing Maps. The performance bottleneck with n-gram statistics is that the size of representation and as a result the computation time of Self-Organizing Maps grows exponentially with the size of n-grams. The presented approach is based on distributed representations of structured data using principles of hyperdimensional computing. The experiments performed on the European languages recognition task demonstrate that Self-Organizing Maps trained with distributed representations require less computations than the conventional n-gram statistics while well preserving the overall performance of Self-Organizing Maps.
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| 7. |
- Kleyko, Denis, 1990-, et al.
(författare)
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Integer Self-Organizing Maps for Digital Hardware
- 2019
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Ingår i: 2019 International Joint Conference on Neural Networks (IJCNN). - : IEEE. - 9781728119854
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Konferensbidrag (refereegranskat)abstract
- The Self-Organizing Map algorithm has been proven and demonstrated to be a useful paradigm for unsupervised machine learning of two-dimensional projections of multidimensional data. The tri-state Self-Organizing Maps have been proposed as an accelerated resource-efficient alternative to the Self-Organizing Maps for implementation on field-programmable gate array (FPGA) hardware. This paper presents a generalization of the tri-state Self-Organizing Maps. The proposed generalization, which we call integer Self-Organizing Maps, requires only integer operations for weight updates. The presented experiments demonstrated that the integer Self-Organizing Maps achieve better accuracy in a classification task when compared to the original tri-state Self-Organizing Maps.
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| 8. |
- Kleyko, Denis, 1990-, et al.
(författare)
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Vector-Based Analysis of the Similarity Between Breathing and Heart Rate During Paced Deep Breathing
- 2018
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Ingår i: Computing in Cardiology 2018. - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- The heart rate (HR) response to paced deep breathing (DB) is a common test of autonomic function, where the scoring is based on indices reflecting the overall heart rate variability (HRV), where high scores are considered as normal findings but can also reflect arrhythmias. This study presents a method based on hyperdimensional computing for assessment of the similarity between feature vectors derived from the HR and breathing signals. The proposed method was used to identify subjects where HR did not follow the paced breathing pattern in recordings from DB tests in 174 healthy subjects and 135 patients with cardiac autonomic neuropathy. Subjects were classified in 4 similarity classes, where the lowest similiarity class included 35 patients and 3 controls. In general, the autonomic function cannot be evaluated in subjects in the lowest similarity class if they also present with high HRV scores, since this combination is a strong indicator of the presence of arrhythmias. Thus, the proposed vector-based similarity analysis is one tool to identify subjects with high HRV but low cardiorespiratory synchronization during the DB test, which falsely can be interpreted as normal autonomic function.
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| 9. |
- Kleyko, Denis, et al.
(författare)
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Vehicle Classification using Road Side Sensors and Feature-free Data Smashing Approach
- 2016
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Ingår i: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). - Piscataway : IEEE. - 9781509018895 - 9781509018888 - 9781509018901 ; , s. 1988-1993
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Konferensbidrag (refereegranskat)abstract
- The main contribution of this paper is a study of the applicability of data smashing - a recently proposed data mining method - for vehicle classification according to the "Nordic system for intelligent classification of vehicles" standard, using measurements of road surface vibrations and magnetic field disturbances caused by passing vehicles. The main advantage of the studied classification approach is that it, in contrast to the most of traditional machine learning algorithms, does not require the extraction of features from raw signals. The proposed classification approach was evaluated on a large dataset consisting of signals from 3074 vehicles. Hence, a good estimate of the actual classification rate was obtained. The performance was compared to the previously reported results on the same problem for logistic regression. Our results show the potential trade-off between classification accuracy and classification method's development efforts could be achieved.
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| 10. |
- Wedekind, Daniel, et al.
(författare)
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Robust Methods for Automated Selection of Cardiac Signals After Blind Source Separation
- 2018
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Ingår i: IEEE Transactions on Biomedical Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9294 .- 1558-2531. ; 65:10, s. 2248-2258
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Novel minimum-contact vital signs monitoring techniques like textile or capacitive electrocardiogram (ECG) provide new opportunities for health monitoring. These techniques are sensitive to artifacts and require handling of unstable signal quality. Spatio-temporal blind source separation (BSS) is capable of processing suchlike multichannel signals. However, BSS's permutation indeterminacy requires the selection of the cardiac signal (i.e., the component resembling the electric cardiac activity) after its separation from artifacts. This study evaluates different concepts for solving permutation indeterminacy.Methods: Novel automated component selection routines based on heartbeat detections are compared with standard concepts, as using higher order moments or frequency-domain features, for solving permutation indeterminacy in spatio-temporal BSS. BSS was applied to a textile and a capacitive ECG dataset of healthy subjects performing a motion protocol, and to the MIT-BIH Arrhythmia Database. The performance of the subsequent component selection was evaluated by means of the heartbeat detection accuracy (ACC) using an automatically selected single component.Results: The proposed heartbeat-detection-based selection routines significantly outperformed the standard selectors based on Skewness, Kurtosis, and frequency-domain features, especially for datasets containing motion artifacts. For arrhythmia data, beat analysis by sparse coding outperformed simple periodicity tests of the detected heartbeats. Conclusion: Component selection routines based on heartbeat detections are capable of reliably selecting cardiac signals after spatio-temporal BSS in case of severe motion artifacts and arrhythmia.Significance: The availability of robust cardiac component selectors for solving permutation indeterminacy facilitates the usage of spatio-temporal BSS to extract cardiac signals in artifact-sensitive minimum-contact vital signs monitoring techniques.
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