Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector

Cardiovascular diseases (CVDs) are the number one cause of death worldwide. While there is growing evidence that the atrial fibrillation (AF) has strong associations with various CVDs, this heart arrhythmia is usually diagnosed using electrocardiography (ECG) which is a risk-free, non-intrusive, and cost-efficient tool. Continuously and remotely monitoring the subjects' ECG information unlocks the potentials of prompt pre-diagnosis and timely pre-treatment of AF before the development of any life-threatening conditions/diseases. Ultimately, the CVDs associated mortality could be reduced. In this manuscript, the design and implementation of a personalized healthcare system embodying a wearable ECG device, a mobile application, and a back-end server are presented. This system continuously monitors the users' ECG information to provide personalized health warnings/feedbacks. The users are able to communicate with their paired health advisors through this system for remote diagnoses, interventions, etc. The implemented wearable ECG devices have been evaluated and showed excellent intra-consistency (CVRMS=5.5%), acceptable inter-consistency (CVRMS=12.1%), and negligible RR-interval errors (ARE<1.4%). To boost the battery life of the wearable devices, a lossy compression schema utilizing the quasi-periodic feature of ECG signals to achieve compression was proposed. Compared to the recognized schemata, it outperformed the others in terms of compression efficiency and distortion, and achieved at least 2x of CR at a certain PRD or RMSE for ECG signals from the MIT-BIH database. To enable automated AF diagnosis/screening in the proposed system, a ResNet-based AF detector was developed. For the ECG records from the 2017 PhysioNet CinC challenge, this AF detector obtained an average testing F1=85.10% and a best testing F1=87.31%, outperforming the state-of-the-art

Post-stack seismic data compression with multidimensional deep autoencoders

Seismic data are surveys from the Earth's subsurface with the goal of representing the geophysical characteristics from the region where they were obtained in order to be interpreted. These data can occupy hundreds of Gigabytes of storage, motivating their compression. In this work, we approach the problem of three-dimensional post-stack seismic data using models based on deep autoencoders. The deep autoencoder is a neural network that allows representing most of the information of a seismic data with a lower cost in comparison to its original representation. To the best of our knowledge, this is the rst work to deal with seismic compression using deep learning. Four compression methods for post-stack data are proposed: two based on a bi-dimensional compression, named 2D-based Seismic Data Compression(2DSC) and 2D-based Seismic Data Compression using Multi-resolution (2DSC-MR), and two based on three-dimensional compression, named 3D-based Seismic Data Compression (3DSC) and 3D-based Seismic Data Compression using Vector Quantization (3DSC-VQ). The 2DSC is our simplest method for seismic compression, in which the volume is compressed through its bi-dimensional sections. The 2DSC-MR extends the previous method by introducing the data compression in multiple resolutions. The 3DSC extends the 2DSC method by allowing the seismic data compression by using the three-dimensional volume instead of 2D slices. This method considers the similarity between sections to compress a whole volume with the cost of a single section. The 3DSC-VQ uses vector quantization aiming to extract more information from the seismic volumes in the encoding part. Our main goal is to compress the seismic data at low bit rates, attaining a high quality reconstruction. Experiments show that our methods can compress seismic data yielding PSNR values over 40 dB and bit rates below 1.0 bpv.Dados sísmicos s~ao mapeamentos da subsuperfície terrestre que têm como objetivo representar as características geofísicas da região onde eles foram obtidos de forma que possam ser interpretados. Esses dados podem ocupar centenas de Gigabytes de armazenamento, motivando sua compressão. Neste trabalho o problema de compressão de dados sísmicos tridimensionais pós-pilha é abordado usando modelos baseados em autocodificadores profundos. O autocodificador profundo é uma rede neural que permite representar a maior parte da informação contida em um dado sísmico com um custo menor que sua representação original. De acordo com nosso conhecimento, este é o primeiro trabalho a lidar com compressão de dados sísmicos utilizando aprendizado profundo. Dessa forma, através de aproximações sucessivas, são propostos quatro métodos de compressão de dados tridimensionais pós-pilha: dois baseados em compressão bidimensional, chamados Método de Compressão 2D de Dado Sísmico (2DSC) e Método de Compressão 2D de Dado Sísmico usando Multi-resolução (2DSC-MR), e dois baseados em compressão tridimensional, chamados Método de Compressão 3D de Dado Sísmico (3DSC) e Método de Compressão 3D de Dado Sísmico usando Quantização Vetorial (3DSC-VQ). O método 2DSC é o nosso método de compressão do dado sísmico mais simples, onde o volume é comprimido a partir de suas seções bidimensionais. O método 2DSC-MR estende o método anterior introduzindo a compressão do dado em múltiplas resoluções. O método 3DSC estende o método 2DSC permitindo a compressão do dado sísmico em sua forma volumétrica, considerando a similaridade entre seções para representar um volume inteiro com o custo de apenas uma seção. O método 3DSC-VQ utiliza quantização vetorial para relaxar a etapa de codificação do método anterior, dando maior liberdade à rede para extrair informação dos volumes sísmicos. O objetivo deste trabalho é comprimir o dado sísmico a baixas taxas de bits e com alta qualidade de reconstrução em termos de PSNR e bits-por-voxel (bpv). Experimentos mostram que os quatro métodos podem comprimir o dado sísmico fornecendo valores de PSNR acima de 40 dB a taxas de bits abaixo de 1.0 bpv.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

深層学習に基づく画像圧縮と品質評価

早大学位記番号:新8427早稲田大

Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64x64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.Comment: Submitted to Journal of Visual Communication and Image Representation (first submission: March 20, 2017; second submission: August 2, 2017