Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression

Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems will play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder diseases. However, the real-time transmission of the significant corpus physiological signals over extended periods consumes substantial power and time, limiting the viability of battery-dependent physiological monitoring wearables. This paper presents a novel deep-learning framework employing a variational autoencoder (VAE) for physiological signal compression to reduce wearables' computational complexity and energy consumption. Our approach achieves an impressive compression ratio of 1:293 specifically for spectrogram data, surpassing state-of-the-art compression techniques such as JPEG2000, H.264, Direct Cosine Transform (DCT), and Huffman Encoding, which do not excel in handling physiological signals. We validate the efficacy of the compressed algorithms using collected physiological signals from real patients in the Hospital and deploy the solution on commonly used embedded AI chips (i.e., ARM Cortex V8 and Jetson Nano). The proposed framework achieves a 91% seizure detection accuracy using XGBoost, confirming the approach's reliability, practicality, and scalability

Robust Detection of Non-Regular Interferometric Fringes From a Self-Mixing Displacement Sensor Using Bi-Wavelet Transform

An innovative signal processing method based on custom-made wavelet transform (WT) is presented for robust detection of fringes contained in the interferometric signal of self-mixing (SM) laser diode sensors. It enables the measurement of arbitrarily-shaped vibrations even in the corruptive presence of speckle. Our algorithm is based on the pattern recognition capability of bespoke WTs for detecting SM fringes. Once the fringes have been correctly detected, phase unwrapping methods can be applied to retrieve the complete instantaneous phase of the SM signals. Here, the novelty consists in using two distinct mother wavelets Ψr(t) and Ψd(t) specifically designed to distinguish SM patterns as well as the displacement direction. The peaks, i.e. the maxima modulus of WT, then allow the detection of the fringes

Цифрова обробка низькоамплітудних компонент електрокардіосигналів

Посібник присвячено розробці і дослідженню методів та засобів для неінвазивного виявлення тонких проявів електричної активності серця. Особлива увага приділена вдосконаленню інформаційно-алгоритмічного забезпечення систем електрокардіографії високого розрізнення для ранньої діагностики електричної нестабільності міокарда, а також для оцінювання функціонального стану плоду під час обстеження вагітних. Теоретичні засади супроводжуються прикладами реалізації розглянутих алгоритмів за допомогою системи MATLAB. Для студентів вищих навчальних закладів, аспірантів, спеціалістів у галузі біомедичної електроніки та медичних працівників

Advances in Digital Processing of Low-Amplitude Components of Electrocardiosignals

This manual has been published within the framework of the BME-ENA project under the responsibility of National Technical University of Ukraine. The BME-ENA “Biomedical Engineering Education Tempus Initiative in Eastern Neighbouring Area”, Project Number: 543904-TEMPUS-1-2013-1-GR-TEMPUS-JPCR is a Joint Project within the TEMPUS IV program. This project has been funded with support from the European Commission.Навчальний посібник присвячено розробці методів та засобів для неінвазивного виявлення та дослідження тонких проявів електричної активності серця. Особлива увага приділяється вдосконаленню інформаційного та алгоритмічного забезпечення систем електрокардіографії високого розрізнення для ранньої діагностики електричної нестабільності міокарда, а також для оцінки функціонального стану плоду під час вагітності. Теоретичні основи супроводжуються прикладами реалізації алгоритмів за допомогою системи MATLAB. Навчальний посібник призначений для студентів, аспірантів, а також фахівців у галузі біомедичної електроніки та медичних працівників.The teaching book is devoted to development and research of methods and tools for non-invasive detection of subtle manifistations of heart electrical activity. Particular attention is paid to the improvement of information and algorithmic support of high resolution electrocardiography for early diagnosis of myocardial electrical instability, as well as for the evaluation of the functional state of the fetus during pregnancy examination. The theoretical basis accompanied by the examples of implementation of the discussed algorithms with the help of MATLAB. The teaching book is intended for students, graduate students, as well as specialists in the field of biomedical electronics and medical professionals

A Multiresolution Analysis of Temporal Logic

Is it possible to determine whether a signal violates a formula in Signal Temporal Logic (STL), if the monitor only has access to a low-resolution version of the signal? We answer this question affirmatively by demonstrating that temporal logic has a multiresolution structure, which parallels the multiresolution structure of signals. A formula in discrete-time Signal Temporal Logic (STL) is equivalently defined via the set of signals that satisfy it, known as its language. If a wavelet decomposition x = y + d is performed on each signal x in the language, we end up with two signal sets Y and D, where Y contains the low-resolution approximation signals y, and D contains the detail signals d needed to reconstruct the x’s. This thesis provides a complete computational characterization of both Y and D using a novel constraint set encoding of STL, s.t. x satisfies a formula if and only if its decomposition signals satisfy their respective encoding constraints. We obtain a sequence of lower-resolution formulas φ−1, φ−2, φ−3, ... which thus constitute a multiresolution analysis of φ. This work lays the foundation for multiresolution monitoring in distributed systems. One potential application of these results is a multiresolution monitor that can detect specification violation early by simply observing a low-resolution version of the signal to be monitored. We illustrate these results with experiments on synthetic signals

Adapted wavelets for pattern detection

Dans cette thèse, nous sommes intéressés à la conception d'ondelettes à support compact adaptées pour la détection de motifs dans les signaux 1-D. Abry et al. ont proposé des méthodes utilisant des combinaisons linéaires et des projections. Cependant, elles peuvent conduire à des filtres très longs approchant des filtres infinis. Nous proposons d'abord ici une technique basée sur le lifting pour construire des ondelettes adaptées à une forme donnée. Les ondelettes construites peuvent être aussi régulières que désiré et avoir plusieurs moments nuls. Nous étudions la stabilité de la méthode par rapport aux petites variations du motif ainsi que trois possibilités d'extension. Notre méthode est, en outre, comparée a celle proposé par Abry et al. dans le cas unidimensionnel. Trois applications sont ensuite développées dans ce travail. Toutes les trois sont liées aux problèmes de reconnaissance ou de détection de motifs. Nous proposons une méthodologie pour résoudre ce type de problèmes avec des ondelettes adaptées en décrivant l'intérêt de les utiliser au lieu de choisir l'une des ondelettes classiques. Les deux premières applications sont en relation avec la médecine, plus précisément avec les neurosciences. Elles consistent en le traitement des électroencéphalogrammes (EEG) : la première porte sur la détection des épis épileptiques et la seconde est liée a l'étude des potentiels évoqués. La troisième et dernière application présentée concerne la détection automatique de deux types des défauts dans la caténaire à partir des mesures de la force de contact avec le pantographe des trains. Pour finir, un chapitre décrit les outils logiciels créés lors de ce travail.In this thesis, we are interested in efficient design of compactly supported adapted wavelets for pattern detection in the 1-D case. Interesting schemes have been developed by Abry et al. using projections and linear combinations. Nevertheless, they may lead to very long filters in order to approximate IIR filters. We first propose a lifting based technique to construct adapted wavelets starting from the function to approximate. This procedure allows to constructconstructing wavelets as smooth as wanted with several vanishing moments. We study the stability of this construction method for small variations of the given pattern and three extension possibilities. We also compare the proposed approach to the one based on projection and linear combinations. Then we examine then three applications where we used adapted wavelets with satisfactory results. All of them are related to pattern recognition or detection problems so we start by proposing a methodology to solve this kind of problems with pattern adapted wavelets, describing the interest of using them instead of the classical ones. The two first applications are related with medicine, more precisely with neurosciences. They consist of the processing of electroencephalogram (EEG) signals: The first one is the detection of epileptic spikes and the second is related to the study of evoked potentials. The third and last application consists of the automatic detection of two kinds of defects in the catenary lines of trains. To end the thesis, a chapter describes some of the software tools that were implemented during this thesis.ORSAY-PARIS 11-BU Sciences (914712101) / SudocORSAY-PARIS 11-Bib. Maths (914712203) / SudocSudocFranceF

Adapted wavelets for pattern detection

Dans cette thèse, nous sommes intéressés à la conception d'ondelettes à support compact adaptées pour la détection de motifs dans les signaux 1-D. Abry et al. ont proposé des méthodes utilisant des combinaisons linéaires et des projections. Cependant, elles peuvent conduire à des filtres très longs approchant des filtres infinis. Nous proposons d'abord ici une technique basée sur le lifting pour construire des ondelettes adaptées à une forme donnée. Les ondelettes construites peuvent être aussi régulières que désiré et avoir plusieurs moments nuls. Nous étudions la stabilité de la méthode par rapport aux petites variations du motif ainsi que trois possibilités d'extension. Notre méthode est, en outre, comparée a celle proposé par Abry et al. dans le cas unidimensionnel. Trois applications sont ensuite développées dans ce travail. Toutes les trois sont liées aux problèmes de reconnaissance ou de détection de motifs. Nous proposons une méthodologie pour résoudre ce type de problèmes avec des ondelettes adaptées en décrivant l'intérêt de les utiliser au lieu de choisir l'une des ondelettes classiques. Les deux premières applications sont en relation avec la médecine, plus précisément avec les neurosciences. Elles consistent en le traitement des électroencéphalogrammes (EEG) : la première porte sur la détection des épis épileptiques et la seconde est liée a l'étude des potentiels évoqués. La troisième et dernière application présentée concerne la détection automatique de deux types des défauts dans la caténaire à partir des mesures de la force de contact avec le pantographe des trains. Pour finir, un chapitre décrit les outils logiciels créés lors de ce travail.In this thesis, we are interested in efficient design of compactly supported adapted wavelets for pattern detection in the 1-D case. Interesting schemes have been developed by Abry et al. using projections and linear combinations. Nevertheless, they may lead to very long filters in order to approximate IIR filters. We first propose a lifting based technique to construct adapted wavelets starting from the function to approximate. This procedure allows to constructconstructing wavelets as smooth as wanted with several vanishing moments. We study the stability of this construction method for small variations of the given pattern and three extension possibilities. We also compare the proposed approach to the one based on projection and linear combinations. Then we examine then three applications where we used adapted wavelets with satisfactory results. All of them are related to pattern recognition or detection problems so we start by proposing a methodology to solve this kind of problems with pattern adapted wavelets, describing the interest of using them instead of the classical ones. The two first applications are related with medicine, more precisely with neurosciences. They consist of the processing of electroencephalogram (EEG) signals: The first one is the detection of epileptic spikes and the second is related to the study of evoked potentials. The third and last application consists of the automatic detection of two kinds of defects in the catenary lines of trains. To end the thesis, a chapter describes some of the software tools that were implemented during this thesis.ORSAY-PARIS 11-BU Sciences (914712101) / SudocORSAY-PARIS 11-Bib. Maths (914712203) / SudocSudocFranceF