Phonocardiogram Segmentation with Tiny Computing

The stethoscope is a daily used tool that allows medical doctors to diagnose common cardiovascular diseases by listening to heart sounds. However, dedicated medical training is required to operate it. Numerous machine learning techniques have been used in attempts to automate this process and have yielded highly accurate results. However, creating a low power, portable, economical, and accurate machine learning stethoscope calls for tiny processing of phonocardiograms i.e., heart sound digital processing to run within an embedded device. To address the need to deploy the solution within a constrained tiny device, we propose an 8-bit deep learning model with low embedded FLASH and RAM utilization of 126 KiB and 45 KiB respectively, which is optimized for inference on an off-the-shelf STM32H7 microcontroller with an inference time of 12 ms, in 126KiB FLASH and 45 KiB RAM being 91.65% accurate

A Hardware-Software System for Accurate Segmentation of Phonocardiogram Signal

Background: Phonocardiogram (PCG) signal provides valuable information for diagnosing heart diseases. However, its applications in quantitative analyses of heart function are limited because the interpretation of this signal is difficult. A key step in quantitative PCG is the identification of the first and second sounds (S1 and S2) in this signal. Objective: This study aims to develop a hardware-software system for synchronized acquisition of two signals electrocardiogram (ECG) and PCG and to segment the recorded PCG signal via the information provided in the acquired ECG signal. Material and Methods: In this analytical study, we developed a hardware-software system for real-time identification of the first and second heart sounds in the PCG signal. A portable device to capture synchronized ECG and PCG signals was developed. Wavelet de-noising technique was used to remove noise from the signal. Finally, by fusing the information provided by the ECG signal (R-peaks and T-end) into a hidden Markov model (HMM), the first and second heart sounds were identified in the PCG signal. Results: ECG and PCG signals from 15 healthy adults were acquired and analyzed using the developed system. The average accuracy of the system in correctly detecting the heart sounds was 95.6% for S1 and 93.4% for S2.   Conclusion: The presented system is cost-effective, user-friendly, and accurate in identifying S1 and S2 in PCG signals. Therefore, it might be effective in quantitative PCG and diagnosing heart diseases

Комплекс задач з аналізу фонокардіографічного сигналу

Пояснювальна записка дипломного проекту складається з шести розділів, містить 17 рисунків, 5 таблиць, 1 додаток, 12 джерел. Дипломний проект присвячений розробці комплексу задач з аналізу фонокардіографічного сигналу. Метою роботи є дослідження та покращення існуючих алгоритмів сегментації та класифікації фонокардіограми для виявлення аномалій у роботі серця. У розділі інформаційного забезпечення було описано вхідні та вихідні дані програми. Описані набори даних, які використовувалися для навчання моделей. Розділ математичного забезпечення присвячений постановці задачі, опису методів, які були використані для розв’язку задача, а також обгрунтуванню доцільності їх використання. В розділі програмне забезпечення описані засоби для розробки програмного продукту, вимоги до технічного забезпчення та архітектура програми. У технологічному розділі описано керівництво користувача та наведено результати проведених тестів з програмним забезпеченням.Structure and scope of work. The explanatory note of the diploma project consists of six sections, contains 17 drawings, 5 tables, 1 application, 12 sources. The diploma project is devoted to the development of a set of tasks for the analysis of phonocardiographic signal. The aim of the work is to study and improve the existing algorithms for segmentation and classification of the phonocardiogram to detect abnormalities in the heart. The information and input data of the program were described in the information support section. Describes the data sets used to train the models. The section of mathematical support is devoted to the formulation of the problem, the description of the methods that were used to solve the problem, as well as the justification of their feasibility. The software section describes software development tools, hardware requirements, and program architecture. The technological section describes the user manual and presents the results of tests performed with the software

Synthesis of normal and abnormal heart sounds using Generative Adversarial Networks

En esta tesis doctoral se presentan diferentes métodos propuestos para el análisis y síntesis de sonidos cardíacos normales y anormales, logrando los siguientes aportes al estado del arte: i) Se implementó un algoritmo basado en la transformada wavelet empírica (EWT) y la energía promedio normalizada de Shannon (NASE) para mejorar la etapa de segmentación automática de los sonidos cardíacos; ii) Se implementaron diferentes técnicas de extracción de características para las señales cardíacas utilizando los coeficientes cepstrales de frecuencia Mel (MFCC), los coeficientes de predicción lineal (LPC) y los valores de potencia. Además, se probaron varios modelos de Machine Learning para la clasificación automática de sonidos cardíacos normales y anormales; iii) Se diseñó un modelo basado en Redes Adversarias Generativas (GAN) para generar sonidos cardíacos sintéticos normales. Además, se implementa un algoritmo de eliminación de ruido utilizando EWT, lo que permite una disminución en la cantidad de épocas y el costo computacional que requiere el modelo GAN; iv) Finalmente, se propone un modelo basado en la arquitectura GAN, que consiste en refinar señales cardíacas sintéticas obtenidas por un modelo matemático con características de señales cardíacas reales. Este modelo se ha denominado FeaturesGAN y no requiere una gran base de datos para generar diferentes tipos de sonidos cardíacos. Cada uno de estos aportes fueron validados con diferentes métodos objetivos y comparados con trabajos publicados en el estado del arte, obteniendo resultados favorables.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic

Automatic Segmentation of Selected Biological Signals based on Machine Learning

Segmentácia EKG a intepretácia jeho vĺn zohráva význmanú úlohu v analýze kardiovaskulárnych ochorení, preto sú v súčasnosti skúmané mnohé automatické prístupy využívajúce pokročilé techniky strojového učenia na detekciu EKG vĺn. Diplomová práca je zameraná na návrh modelu neurónovej siete založenej na hlbokom učení pre automatickú detekciu jednotlivých vĺn EKG signálu s dôrazom na testovanie rôznych nastavení hyperparametrov modelu s ohľadom na celkový výkon a robustnosť. Teoretická časť práce obsahuje základné princípy supervizórneho učenia a prehľad prístupov strojového učenia k segmentácii 1D biosignálov. V rámci praktickej časti bol navrhnutý segmentačný model neurónovej siete, ktorý bol následne testovaný pre rôzne nastavenia hyperparametrov. Súčasťou praktickej časti je aj vytvorenie SW rozhrania umožňujúceho trénovanie segmentačného modelu na užívateľských dátach.The segmentation of ECG and the interpretation of its waves play a significant role in the analysis of cardiovascular diseases, which is why many automatic approaches using advanced machine learning techniques for ECG wave detection are currently being studied. The diploma thesis focuses on designing a neural network model based on deep learning for the automatic detection of individual ECG waves, emphasizing testing various hyperparameter settings of the model concerning overall performance and robustness. The theoretical part of the thesis includes basic principles of supervised learning and an overview of machine learning approaches to the segmentation of 1D biosignals. In the practical part, a segmentation model of the neural network was designed and subsequently tested for various hyperparameter settings. The practical part also includes creating a software interface to train the segmentation model on the user's data.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř