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Multi-Level Reversible Encryption for ECG Signals Using Compressive Sensing

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Multi-Level Reversible Encryption for ECG Signals Using Comp...

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Mikko Impio Mehmet Yamac Jenni Raitoharju Faculty of Information Technology and Communication Sciences Tampere University Finland Programme for Environmental Information Finnish Environment Institute Jyvaskyla Finland

Privacy concerns in healthcare have gained interest recently via GDPR, with a rising need for privacy-preserving data collection methods that keep personal information hidden in otherwise usable data. Sometimes data needs to be encrypted for several authentication levels, where a semi-authorized user gains access to data stripped of personal or sensitive information, while a fully-authorized user can recover the full signal. In this paper, we propose a compressive sensing based multi-level encryption to ECG signals to mask possible heartbeat anomalies from semi-authorized users, while preserving the beat structure for heart rate monitoring. Masking is performed both in time and frequency domains. Masking effectiveness is validated using 1D convolutional neural networks for heartbeat anomaly classification, while masked signal usefulness is validated comparing heartbeat detection accuracy between masked and recovered signals. The proposed multi-level encryption method can decrease classification accuracy of heartbeat anomalies by up to 50%, while maintaining a fairly high R-peak detection accuracy.

关键词： Privacy Heart beat Medical services Electrocardiography Signal processing Encryption Sensors

Multi-level reversible encryption for ECG signals using compressive sensing

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arXiv 2021年

作者： Impiö, Mikko Yamaç, Mehmet Raitoharju, Jenni Faculty of Information Technology and Communication Sciences Tampere University Finland Programme for Environmental Information Finnish Environment Institute Jyväskylä Finland

关键词： Electrocardiography

Ensembling Object Detectors for Image and Video Data Analysis

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Ensembling Object Detectors for Image and Video Data Analysi...

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kateryna Chumachenko Jenni Raitoharju Alexandros Iosifidis Moncef Gabbouj Faculty of Information Technology and Communication Sciences Tampere University Finland Programme for Environmental Information Finnish Environment Institute Finland Aarhus University Denmark

In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We further extend it to video data by proposing a two-stage tracking-based scheme for detection refinement. The proposed method can be used as a standalone approach for improving object detection performance, or as a part of a framework for faster bounding box annotation in unseen datasets, assuming that the objects of interest are those present in some common public datasets.

关键词： Data analysis Annotations Conferences Detectors Object detection Signal processing Acoustics

IoT Prototype System for Healthcare on Monitoring of Knee Osteoarthritis Treatment Patients 2

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IoT Prototype System for Healthcare on Monitoring of Knee Os...

2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020

作者： Amplod, Supalak Choksuchat, Chidchanok Sopitpan, Suwat Prince of Songkla University Information and Communication Technology Programme Faculty of Science Thailand National Electronics and Computer Technology Center Center for Cyber-Physical Systems Thailand

ISBN: (数字)9781728170633

ISBN: (纸本)9781728170633

Currently, there are many numbers of patients with knee osteoarthritis (KOA) which is the most common joint pain of osteoarthritis (OA). Joint stiffness and pain in the knee are the KOA symptoms depending on many causes. There are 13% of women, 10% of 60 years and older than 70 years men, the prevalence rises to 40%. Males are prevalent lower than in females [9]. This makes the number of doctors and medical assistance who specialize in monitoring the treatment inadequate for patients' proportion. In addition, problems in patients' accommodation are the long-distance travel, rural area, insecure route issue, e.g., in southern of Thailand, transportation, and requesting a helper or caregivers accompany to see a doctor every period. Therefore, this research approaches Internet of Things (IoT) technology with physical stabilization knee support as electronic medical records (EMR) on a website monitoring the KOA patients' treatment results. The direct benefit is to increase the accuracy of the remote measurement, to analysis of symptoms and to facilitate the follow-up of medical treatment and improve the quality of rural patients' life. © 2020 IEEE.

关键词： Internet of things

Non-Linear Spectral Dimensionality Reduction Under Uncertainty

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arXiv 2022年

作者： Laakom, Firas Raitoharju, Jenni Passalis, Nikolaos Iosifidis, Alexandros Gabbouj, Moncef Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland The Programme for Environmental Information Finnish Environment Institute Jyväskylä Finland The Department of Informatics Aristotle University of Thessaloniki Greece The Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

In this paper, we consider the problem of non-linear dimensionality reduction under uncertainty, both from a theoretical and algorithmic perspectives. Since real-world data usually contain measurements with uncertainties and artifacts, the input space in the proposed framework consists of probability distributions to model the uncertainties associated with each sample. We propose a new dimensionality reduction framework, called NGEU, which leverages uncertainty information and directly extends several traditional approaches, e.g., KPCA, MDA/KMFA, to receive as inputs the probability distributions instead of the original data. We show that the proposed NGEU formulation exhibits a global closed-form solution, and we analyze, based on the Rademacher complexity, how the underlying uncertainties theoretically affect the generalization ability of the framework. Empirical results on different datasets show the effectiveness of the proposed framework. © 2022, CC BY.

关键词： Uncertainty analysis

Ensembling object detectors for image and video data analysis

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arXiv 2021年

作者： Chumachenko, Kateryna Raitoharju, Jenni Iosifidis, Alexandros Gabbouj, Moncef Tampere University Faculty of Information Technology and Communication Sciences Finland Finnish Environment Institute Programme for Environmental Information Finland Aarhus University Department of Electrical and Computer Engineering Denmark

关键词： Object detection

Learning distinct features helps, provably

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arXiv 2021年

作者： Laakom, Firas Raitoharju, Jenni Iosifidis, Alexandros Gabbouj, Moncef Faculty of Information Technology and Communication Sciences Tampere University Finland Faculty of Information Technology University of Jyväskylä Finland Programme for Environmental Information Finnish Environment Institute Jyväskylä Finland Department of Electrical and Computer Engineering Aarhus University Denmark

We study the diversity of the features learned by a two-layer neural network trained with the least squares loss. We measure the diversity by the average L2-distance between the hidden-layer features and theoretically investigate how learning non-redundant distinct features affects the performance of the network. To do so, we derive novel generalization bounds depending on feature diversity based on Rademacher complexity for such networks. Our analysis proves that more distinct features at the network’s units within the hidden layer lead to better generalization. We also show how to extend our results to deeper networks and different losses. © 2021, CC BY-NC-ND.

关键词： Network layers

Graph-Embedded Subspace Support Vector Data Description

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arXiv 2021年

作者： Sohrab, Fahad Iosifidis, Alexandros Gabbouj, Moncef Raitoharju, Jenni Faculty of Information Technology and Communication Sciences Tampere University TampereFI-33720 Finland Department of Electrical and Computer Engineering Aarhus University AarhusDK-8200 Denmark Programme for Environmental Information Finnish Environment Institute JyväskyläFI-40500 Finland

In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes the previously proposed subspace one-class techniques as its special cases and provides further insight on what these techniques actually optimize. The framework allows to incorporate other meaningful optimization goals via the graph preserving criterion and reveals a spectral solution and a spectral regression-based solution as alternatives to the previously used gradient-based technique. We combine the subspace learning framework iteratively with Support Vector Data Description applied in the subspace to formulate Graph-Embedded Subspace Support Vector Data Description. We experimentally analyzed the performance of newly proposed different variants. We demonstrate improved performance against the baselines and the recently proposed subspace learning methods for one-class classification. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

Boosting rare benthic macroinvertebrates taxa identification with one-class classification

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arXiv 2020年

作者： Sohrab, Fahad Raitoharjuy, Jenni Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Programme for Environmental Information Finnish Environment Institute Jyvaskylä Finland

rucial for understanding the consequences of rapid ecological changes, but taxa identification currently requires tedious manual expert work and cannot be scaled-up efficiently. Deep convolutional neural networks (CNNs), provide a viable way to significantly increase the biomonitoring volumes. However, taxa abundances are typically very imbalanced and the amounts of training images for the rarest classes are simply too low for deep CNNs. As a result, the samples from the rare classes are often completely missed, while detecting them has biological importance. In this paper, we propose combining the trained deep CNN with one-class classifiers to improve the rare species identification. One-class classification models are traditionally trained with much fewer samples and they can provide a mechanism to indicate samples potentially belonging to the rare classes for human inspection. Our experiments confirm that the proposed approach may indeed support moving towards partial automation of the taxa identification task. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks