TY - JOUR TI - Evaluation of mathematical models for QRS feature extraction and QRS morphology classification in ECG signals AU - do Vale Madeiro, João Paulo AU - Lobo Marques, João Alexandre AU - Han, Tao AU - Coury Pedrosa, Roberto T2 - Measurement AB - It is plausible to assume that the component waves in ECG signals constitute a unique human characteristic because morphology and amplitudes of recorded beats are governed by multiple individual factors. According to the best of our knowledge, the issue of automatically classifying different ’identities’ of QRS morphology has not been explored within the literature. This work proposes five alternative mathematical models for representing different QRS morphologies providing the extraction of a set of features related to QRS shape. The technique incorporates mechanisms of combining the mathematical functions Gaussian, Mexican-Hat and Rayleigh probability density function and also a mechanism for clipping the waveform of those functions. The searching for the optimal parameters which minimize the normalized RMS error between each mathematical model and a given QRS search window enables to find an optimal model. Such modeling behaves as a robust alternative for delineating heartbeats, classifying beat morphologies, detecting subtle and anomalous changes, compression of QRS complex windows among others. The validation process evaluates the ability of each model to represent different QRS morphology classes within 159 full ECG signal records from QT database and 584 QRS search windows from MIT-BIH Arrhythmia database. From the experimental results, we rank the winning rates for which each mathematical model best models and also discriminates the most predominant QRS morphologies Rs, rS, RS, qR, qRs, R, rR’s and QS. Furthermore, the average time errors computed for QRS onset and offset locations when using the corresponding winner mathematical models for delineation purposes were, respectively, 12.87±8.5 ms and 1.47±10.06 ms. DA - 2020/05/01/ PY - 2020 DO - 10.1016/j.measurement.2020.107580 DP - ScienceDirect VL - 156 SP - 107580 J2 - Measurement LA - en SN - 0263-2241 UR - https://www.sciencedirect.com/science/article/pii/S0263224120301172 Y2 - 2022/09/21/05:15:22 KW - ECG feature extraction KW - Mathematical modeling KW - Morphology classification KW - QRS complex delineation ER - TY - CHAP TI - Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks AU - Caldas, Weslley Lioba AU - Do Vale Madeiro, João Paulo AU - Pedrosa, Roberto Coury AU - Gomes, João Paulo Pordeus AU - Du, Wencai AU - Marques, João Alexandre Lobo T2 - Computer and Information Science A2 - Lee, Roger CY - Cham DA - 2023/// PY - 2023 DP - DOI.org (Crossref) VL - 1055 SP - 117 EP - 129 LA - en PB - Springer International Publishing SN - 9783031121265 9783031121272 UR - https://link.springer.com/10.1007/978-3-031-12127-2_8 Y2 - 2023/04/26/07:31:40 ER - TY - CHAP TI - Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks AU - Caldas, Weslley Lioba AU - do Vale Madeiro, João Paulo AU - Pedrosa, Roberto Coury AU - Gomes, João Paulo Pordeus AU - Du, Wencai AU - Marques, João Alexandre Lobo T2 - Computer and Information Science A2 - Lee, Roger T3 - Studies in Computational Intelligence AB - Continuous cardiac monitoring has been increasingly adopted to prevent heart diseases, especially the case of Chagas disease, a chronic condition that can degrade the heart condition, leading to sudden cardiac death. Unfortunately, a common challenge for these systems is the low-quality and high level of noise in ECG signal collection. Also, generic techniques to assess the ECG quality can discard useful information in these so-called chagasic ECG signals. To mitigate this issue, this work proposes a 1D CNN network to assess the quality of the ECG signal for chagasic patients and compare it to the state of art techniques. Segments of 10 s were extracted from 200 1-lead ECG Holter signals. Different feature extractions were considered such as morphological fiducial points, interval duration, and statistical features, aiming to classify 400 segments into four signal quality types: Acceptable ECG, Non-ECG, Wandering Baseline (WB), and AC Interference (ACI) segments. The proposed CNN architecture achieves a $$0.90 \pm 0.02$$accuracy in the multi-classification experiment and also $$0.94 \pm 0.01$$when considering only acceptable ECG against the other three classes. Also, we presented a complementary experiment showing that, after removing noisy segments, we improved morphological recognition (based on QRS wave) by 33% of the entire ECG data. The proposed noise detector may be applied as a useful tool for pre-processing chagasic ECG signals. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 117 EP - 129 LA - en PB - Springer International Publishing SN - 978-3-031-12127-2 UR - https://doi.org/10.1007/978-3-031-12127-2_8 Y2 - 2023/08/01/12:50:19 KW - Chagas disease KW - Deep learning KW - ECG quality assessment ER - TY - CONF TI - Training Strategies for Covid-19 Severity Classification AU - Pordeus, Daniel AU - Ribeiro, Pedro AU - Zacarias, Laíla AU - de Oliveira, Adriel AU - Marques, João Alexandre Lobo AU - Rodrigues, Pedro Miguel AU - Leite, Camila AU - Neto, Manoel Alves AU - Peixoto, Arnaldo Aires AU - do Vale Madeiro, João Paulo A2 - Rojas, Ignacio A2 - Valenzuela, Olga A2 - Rojas Ruiz, Fernando A2 - Herrera, Luis Javier A2 - Ortuño, Francisco T3 - Lecture Notes in Computer Science AB - The COVID-19 pandemic has posed a significant public health challenge on a global scale. It is imperative that we continue to undertake research in order to identify early markers of disease progression, enhance patient care through prompt diagnosis, identification of high-risk patients, early prevention, and efficient allocation of medical resources. In this particular study, we obtained 100 5-min electrocardiograms (ECGs) from 50 COVID-19 volunteers in two different positions, namely upright and supine, who were categorized as either moderately or critically ill. We used classification algorithms to analyze heart rate variability (HRV) metrics derived from the ECGs of the volunteers with the goal of predicting the severity of illness. Our study choose a configuration pro SVC that achieved 76% of accuracy, and 0.84 on F1 Score in predicting the severity of Covid-19 based on HRV metrics. C1 - Cham C3 - Bioinformatics and Biomedical Engineering DA - 2023/// PY - 2023 DO - 10.1007/978-3-031-34953-9_40 DP - Springer Link SP - 514 EP - 527 LA - en PB - Springer Nature Switzerland SN - 978-3-031-34953-9 KW - COVID-19 KW - Electrocardiogram (ECG) KW - Heart Rate Variability (HRV) KW - disease severity classification KW - signal processing ER -