Peak detection in intracranial pressure signal waveforms: a comparative study.

BACKGROUND: The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work quantitatively evaluates existing computational frameworks for automatically extracting peaks within ICP waveforms. METHODS: Peak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying noise levels. The evaluation was performed on a dataset of ICP signals assembled from 700 h of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13, 611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise. RESULTS: The quantitative analysis of peak detection algorithms applied to individual waveforms indicates that most techniques provide acceptable accuracy with a mean absolute error (MAE) ≤ 10 ms without noise. In the presence of a higher noise level, however, only kernel spectral regression and random forest remain below that error threshold while the performance of other techniques deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and long short-term memory (LSTM) can be applied continuously and provide additional robustness in situations where single pulse analysis methods fail, such as missing data. CONCLUSION: While machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to noise and temporary artifacts that commonly arise as part of the monitoring setup in the clinical setting

Proposta de uma arquitetura de redes neurais para estimativa da frequência cardíaca fetal a partir do ECG abdominal em gestantes

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, 2018.A estimativa da frequência cardíaca fetal (FHR – Fetal Heart Rate) tem se mostrado um parâmetro de fundamental importância na avaliação das condições do feto durante a gestação. A partir do ECG abdominal (AECG) da mãe, é possível estimar a FHR após o devido processamento do sinal original. Considerando que o AECG é composto pelo ECG da mãe, pelo ECG do feto e por ruídos diversos, várias abordagens têm sido utilizadas para extrair o ECG do feto (FECG) e, a partir dele, estimar a FHR contando os picos. Alguns exemplos de soluções propostas envolvem o uso de filtros adaptativos, wavelets, blind source separation, etc. Recentemente, alguns trabalhos de conclusão de curso na Engenharia Eletrônica da Faculdade do Gama – UnB têm sido direcionados para a implementação de um protótipo para estimar a FHR utilizando um FPGA na parte de processamento, pois o mesmo permite fazer aceleração de algoritmos e também utilizar diferentes abordagens, visto que é reconfigurável. Além do bloco de processamento, no qual está sendo utilizada atualmente uma abordagem que usa um filtro adaptativo e o método dos mínimos quadrados (LMS), o protótipo também contém blocos de aquisição de sinal e comunicação. Considerando que o filtro adaptativo apresentou um desempenho aceitável com sinais simulados de ECG, mas não apresentou bons resultados com sinais provindos de bases de dados, este trabalho tem como objetivo a proposta de uma arquitetura de redes neurais para o bloco de processamento do protótipo. Espera-se, assim, obter um melhor resultado, visto que que as redes neurais são adaptativas às características não lineares e variantes no tempo dos sinais de ECG.Fetal heart rate (FHR) has been a fundamental parameter in the evaluation of the fetal condition during gestation. Starting from the mother’s abdominal ECG, it’s possible to estimate the FHR through adequate signal processing. Considering that this signal is composed by the mother ECG, fetal ECG and noise, a variety of forms has been used to extract the fetal ECG (FECG) and, from this signal, estimate the FHR counting the peaks. Some examples of solution cover the use of adaptive filtering, neural networks, wavelets, blind source separation, etc. Recently, some final year projects in Electronic Engineering at Faculdade do Gama - UnB have been directed to the implementation of a prototype to estimate the FHR using an FPGA as processing unit, because it allows us to accelerate algorithms and also make use of different approaches, since its reconfigurable. Beyond the processing unit, that has been developed using an adaptive filter and least minimum square algorithm, the prototype also has signal acquisition and communication blocks. Considering that the adaptative filtering presented an acceptable performance using simulated ECG signal, but it didn’t bring out good results using database signals, this work aims to propose a neural network architecture to be used in the processing unit of the prototype. It is expected to obtain a better result, since neural networks are adaptive to the nonlinear and variant characteristics of the ECG signal

Fetal electrocardiogram extraction and R-peak detection for fetal heart rate monitoring using artificial neural network and correlation

Conventional techniques are often unable to achieve the Fetal Electrocardiogram FECG extraction and Rpeak detection in FECG from the abdominal ECG (AECG) in satisfactorily level for Fetal Heart Rate (FHR) monitoring. A new methodology by combining the Artificial Neural Network (ANN) and Correlation approach has been proposed in this paper. Artificial Neural Network is chosen primarily since it is adaptive to the nonlinear and time-varying features of the ECG signal. The supervised multilayer perception (MLP) network has been used because it requires a desired output in order to learn. Similarly, the Correlation method has been chosen as the correlation factor can be used to scale the MECG when subtracting it from the AECG, in order to get the FECG. By combining these two approaches the proposed methodology gives better and efficient result in terms of accuracy for FECG extraction and R-peak detection in the AECG signal due to its above characteristics. The proposed approach involves the FECG extraction from the AECG signal with the accuracy of 100% and R-peak detection performance is 93.75%, even though the overlapping situation of MECG and FECG signal in the AECG signal. Therefore the physician and clinician can make the correct decision for the well-being status of the fetus and mother during the pregnancy period

Artificial Neural Networks as Approach for Fetal Electrocardiogram Extraction and R-peak Detection

Tato diplomová práce se zabývá extrakcí fetálního plodového elektrokardiogramu (fEKG) pomocí metod využívající umělé neuronové sítě (ANN). Po prostudování problematiky zpracování neinvazivního fEKG (NI-fEKG) signálu byla provedena rešerše současných metod využívající ANN pro extrakci fEKG signálu z abdominálního signálu (aEKG). Na základě provedené rešerše byly vybrány metody využívající lineární adaptivní neuron (ADALINE), adaptivní neuro-fuzzy inferenční systém (ANFIS) a rekurentní sítě (RNN) tzv. Echo state sítě. Tyto metody byly také využity v kombinaci s dopřednou vícevrstvou ANN (ANN-ADALINE, ANN-ANFIS, ANN-ESN). Testování vybraných metod bylo provedeno na reálných datech z databáze Labour dataset a Pregnancy dataset. Pro vyhodnocení extrakce a stanovení plodové srdeční frekvence (fHR) byly detekovány R-kmity pomocí dvou detektorů. První detektor byl založen na spojité vlnkové transformaci (CWT), druhý detektor byl založen na dopředné vícevrstvé ANN. Pro zhodnocení byla stanovena celková pravděpodobnost správné detekce (ACC), senzitivita (SE), pozitivní prediktivní hodnota (PPV) a jako harmonický průměr SE a PPV byl stanoven parametr F1. Funkčnost metod byla ověřena vůči referenčním anotacím. Ve srovnání s metodami ADALINE, ANFIS, ANN-ADALINE, ANN-ANFIS a ANN-ESN, dosáhla metoda ESN nejlepších výsledků. Pro data z databáze Labour dataset dosahovala metoda hodnoty ACC 78,65 %, pro data z databáze Pregnancy dataset byla hodnota ACC přes 80 %. Pro zpracování, analýzu a vyhodnocení bylo vytvořeno grafické uživatelské rozhraní (GUI) v programu MATLAB.This thesis deals with the extraction of fetal electrocardiogram (fECG) through methods that use Artificial Neural Networks (ANN). After careful examination of non-invasive fECG (NI-fECG) signal processing, a search of current methods using ANN for extraction of fECG signal was performed. Based on the search, methods using a Linear Adaptive Neuron (ADALINE), an Adaptive Neuro-fuzzy Inference System (ANFIS) and a Recurrent Network (RNN), the so-called Echo State Network (ESN), were selected. These methods were also used in combination with Multilayer Feedforward ANN (ANN-ADALINE, ANN-ANFIS, ANN-ESN). Testing of the chosen methods was performed on real data from the Labour dataset and Pregnancy dataset databases. R-peaks were detected using two detectors to evaluate extraction and fetal heart rate (fHR). The first detector was based on continuous wavelet transform (CWT), the second detector was based on Multilayer Feedforward ANN. For evaluation the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and the harmonic mean of SE and PPV (F1) were determined. The functionality of chosen methods was verified by comparison to reference anotations. In comparison to methods ADALINE, ANFIS, ANN-ADALINE, ANN-ANFIS a ANN-ESN, the ESN method achieved the best results. For data from the Labor dataset, the ACC value reached 78.65 %, for data from the Pregnancy dataset, the ACC value was over 80 %. A graphical user interface (GUI) was created for processing, analysis and evaluation in MATLAB.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Hybrid Methods for Fetal Electrocardiogram Extraction

Cílem této diplomové práce je neinvazivní zpracování plodového elektrokardiogramu (fEKG) pomocí hybridních metod, které kombinují dvě a více transabdominálních metod. Teoretická část práce je věnována problematice plodové elektrokardiografie, rozsáhlé rešerši již existujících hybridních metod a matematickému popisu implementovaných metod. Experimentální část je primárně zaměřena na testování a analýzu vzájemných kombinací analýzy nezávislých komponent (ICA), vlnkové transformace (WT), prahování vlnkových koeficientů (WS), empirické modální dekompozice (EMD), souboru empirické modální dekompozice (EEMD) a analýzy hlavních komponent (PCA). Hodnocení extrakce je provedeno na základě stanovení variability tepové frekvence plodu (fHRV). Použitím hybridních metod je v této práci dosaženo lepších výsledků než při samotném použití metody ICA. Výstupem práce je také implementace metod v grafickém uživatelské rozhraní v prostředí Matlab.The aim of this thesis is non-invasive processing of fetal electrocardiogram (fECG) using hybrid methods, which combine two or more transabdominal methods. The theoretical part is dedicated to the problems of fetal electrocardiography, complex overview of already existing hybrid methods and mathematical description of implemented methods. The experimental part is primarily focused on testing and analysis combinations of independent component analysis (ICA), wavelet transform (WT), wavelet shrinkage (WS), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and principal component analysis (PCA). The evaluation of extraction quality is based on the determination of fetal heart rate variability (fHRV). The performence of hybrid methods in this thesis is better than usage of individual ICA method. The output of the thesis is also implementation of methods in the graphical user interface in Matlab.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn